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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/18/2026 has been entered.
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 11-12 and 14-23 are rejected under 35 U.S.C. 101 because 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.
STEP 1
Regarding claims 11, 21, and 22, the claims recite a series of steps or acts, including:
predicting future glucose levels of the patient in between 2-8 hours in the future in claims 11;
continuously refining the trained prediction model using the predicted future glucose levels and the blood glucose received from the continuous glucose monitor in claim 21; and
decoding the brain activity signal to predict future glucose levels of the user using a trained prediction model in claim 22.
Thus, the claim is directed to a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The steps of predicting future glucose levels of the patient in between 2-8 hours in the future in claims 11;
continuously refining the trained prediction model using the predicted future glucose levels and the blood glucose received from the continuous glucose monitor in claim 21; and
decoding the brain activity signal to predict future glucose levels of the user using a trained prediction model in claim 22 set respectively forth a judicial exception. These steps describe distinct mathematical calculations based on a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. Thus, the claims are drawn to Mathematical Concepts, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claims fail to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claims 11 and 22 recite providing a warning based on the predicted future glucose levels, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). Claim 21 is silent on insignificant extra-solution activity to the judicial exception. In Claims 11 and 22, the prediction nor the confirmation provide an improvement to the technological field, the methods do not effect a particular treatment or effect a particular change based on the prediction or confirmation, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, Claim 11 recites additional steps of receiving brain activity signal from a scalp brain recorder; Claim 21 recite additional steps of receiving brain activity signals, decoding the brain activity signal using a trained prediction model, receiving blood glucose levels from a monitor, and confirming the accuracy of the predicted future glucose; and Claim 22 recites additional steps of receiving brain activity signal from a scalp brain recorder and training a prediction model on standardized data and from the user. The receiving, decoding, confirming, and training activities are well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the recording and decoding steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Claims 16 -18, as dependent on Claim 11, contain subject matter that can overcome the rejection by incorporating them into the independent claim. Particularly, the positively recited delivery of therapy to the user.
Claims 21 and 22 do not have any dependent claim, however, similar subject matter as in Claims 16-18 can be incorporated therein to overcome the rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 11 and 21-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 11, 21, and 22 recite the use of a model for predicting future glucose, but fail to provide details of the model. The specification merely recites that machine learning model can be used but fails to specify what the components of the model are (¶[0047]). In other words, the input and output of the model can be identified, but not the how the model uses the inputs, manipulates them, and calculates the output. Therefore, the model is not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor had possession of the invention.
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 11-12, 14-22, and 24-25 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.
Claims 11, 21, and 22 recite the use of a model for predicting future glucose, but is indefinite because it is unclear how the model processes the inputs to get the output.
Claims not listed are rejected by virtue of claim dependence.
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.
Claims 11, 14-15, 20-22, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Madsen et al. (US 20130274580-Previously cited), hereinafter Madsen, and further in view of Wexler et al. (US 20200375549- previously recited), hereinafter Wexler.
Regarding claims 11 and 22, Madsen teaches recording a brain activity signal of a user's brain using a non-invasive scalp worn brain signal recorder (¶[0041], “multiple electrodes to gather EEG signals,” “[t]hese may be positioned on the scalp of the patient,” “[w]hen outside the skull at least, preferred positions for the electrodes will be such as (P4-T6), (P4-T4), (P3-T5), or (P3-T3) in the standard EEG 10-20 system”);
using a trained prediction model (¶[0068], “coefficients are obtained by training signal processing unit in a process of machine learning”), decoding the brain activity signal to predict future glucose levels of the patient (see abstract, EEG signals are measured and processed to determine future hypoglycemia event, e.g., predicting future glucose levels), wherein the trained prediction model is pretrained on standardized data (¶[0055-57,0069], “The hypoglycemia event classifier and artifact classifiers, all have the same structure, which accepts as input in each case the N normalized features, which are inputs to each of the structure,” “In the Event Classifier, the normalized features are first weighted by a first set of coefficients and then added together giving a cost function which is output from the module marked `Weighting and Adding` to the module marked `Event Detector`. The derivation of the first set of coefficients will be described later below” and “We indicated above that the normalised Features are subjected to weighting and adding in the "Weighting and adding" blocks in FIG. 3 using respective sets of parameters that determine to what extent each normalised feature should influence the outcome of the event detector and the artifact detectors. These sets of coefficients once obtained can be used generally for all patients. The coefficients are obtained by training signal processing unit in a process of machine learning.” (emphasis added). One of ordinary skill in the art understands that normalization is one method of standardizing data. ); and
providing a warning to the user on a personal device about the predicted future glucose levels (¶[0083], “The alarm emitted by the Hypo Detector block may activate an alarm signal generator to output a physically perceivable alarm signal such as a sound or vibration or mild electrical shock perceivable by a user of said apparatus.” and “ The analysis of the EEG and the generation of any form of alarm signal may, as tasks, be split between an implanted module connected to the EEG electrodes and an external module communicating with the internal module, as described in WO2006/066577 and in WO2009/090110.” (emphasis added) The disclosure of WO2006/066577 teaches that the external module for alarm generation is incorporated into a “pocket computer device.” ).
Madsen fails to teach wherein the predicted glucose levels of the patient are at least 2 hours and no more than 8 hours in the future, and wherein training of the trained prediction model is completed using data acquired from the user.
Wexler teaches systems and methods for glucose-based forecasting and explaining health metrics (abstract and ¶[0006]). The system requires obtaining population wide data to train (as taught in Madsen) and refine the model (¶[0050]) along with data from a specific user (¶[0081,0083]). This combination of population with user specific data allows models to make connections between individuals with similar features and to make predictions with uses with few, irregular, and/or incomplete data (¶[0014]). The predictions being made within 2-8 hours (¶[0061]). The personalization of the data along with the population data helps in providing real time improvement health outcomes of patients with diabetes (¶[0014]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Madsen, such that the predicted glucose levels of the patient are between 2-8 hours in the future and that training the prediction model is completed with user specific data, as taught by Wexler, to aid in providing real-time improvements health outcomes for patients with diabetes having few, irregular, and/or incomplete data (¶[0014] of Wexler).
Regarding claim 12, Madsen teaches providing a warning when estimated glucose levels rise above a threshold value (see para. [0011,0047,0066], “The alarm detector continuously monitors the output from the integrator block. When the integrated evidence passes a certain threshold, the evidence of impending hypoglycemia is high enough for the unit to emit a hypoglycemia alarm signal”).
Regarding claim 14, Madsen teaches decoding the brain activity to a multivariate decoder trained on spectral profiles of intracranial activity (see para. [0058], one skill in the art understands that an event classifier is a type of multivariate decoder).
Regarding claims 15, Madsen teaches wherein the brain activity signal describes the spectral profile of the broadband brain activity (see para. [0049], “Each feature normalization block (1:N) (in FIG. 1) receives the full EEG stream” one of ordinary skill in the art understands that a full EEG stream comprises broadband brain activity).
Regarding claims 20, Madsen teaches wherein the glucose level consists of blood glucose level (see para. [0011], “In accordance with the present invention, the detection of hypoglycemia or impending hypoglycemia may be conducted by detecting EEG signal patterns that are associated with a blood glucose level”).
Regarding claim 21, Madsen teaches a computer executable method executable by one or more processors, the method comprising:
receiving brain activity signal of a user's brain from a non-invasive scalp-worn brain signal recorder (¶[0041], “multiple electrodes to gather EEG signals,” “[t]hese may be positioned on the scalp of the patient,” “[w]hen outside the skull at least, preferred positions for the electrodes will be such as (P4-T6), (P4-T4), (P3-T5), or (P3-T3) in the standard EEG 10-20 system”); and
using a trained prediction model, decoding the brain activity signal to predict future glucose levels of the user (see abstract, EEG signals are measured and processed to determine future hypoglycemia event, e.g., predicting future glucose levels. ¶[0068], trained classifier information), and
However, Madsen fails to teach receiving blood glucose levels from a continuous glucose monitor worn by the user; and confirming the accuracy of the predicted future glucose levels using the blood glucose levels received from the continuous glucose monitor and continuously refining the trained prediction model using the predicted future glucose levels and the blood glucose levels received from the continuous glucose monitor.
Wexler’s system includes a continuous glucose monitor (¶[0014). The model can further compute a confidence interval based on the initial predictions being confirmed as inaccurate, i.e. low confidence, and accurate, i.e. high confidence, when inconsistent with input data (¶[0023,0066], the input data being from the CGM). Furthermore, Wexler teaches that the patient specific model population model add aggregate model can be periodically updated as new data is received and on predicted glucose values (¶[0082,0087], predictions from the patient specific model and population model can be input into the aggregate model 430. “The features generated from these predictions can also be included in the feature data used to train the aggregate model 430. In some embodiments, the predictions that are used to generate features vary depending on the time horizon of the predictions to be made by the aggregate model 430.”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Madsen, to input blood glucose levels from a continuous glucose monitor worn by the user, confirm the accuracy of the predicted future glucose levels using the blood glucose levels received from the continuous glucose monitor, and updating the model with predicted and actual data, as taught by Wexler, to aid in providing real-time improvements health outcomes for patients with diabetes having few, irregular, and/or incomplete data (¶[0014] of Wexler).
Regarding claim 24, Madsen fails to teach wherein decoding the brain activity signal comprises generating a plurality of predictions of future glucose levels at different time points, and using only those predictions that are above a predetermined confidence threshold.
Wexler teaches that multiple predictions can be computed, but only high confidence predictions are retained (¶[0066,0127], “Optionally, the initial prediction(s) can be filtered, e.g., to exclude predictions that are outliers, inconsistent with the input data, and/or contradictory. Filtering can also be performed to exclude predictions that are more likely to be inaccurate (e.g., low confidence predictions) while retaining predictions that are more likely to be accurate (e.g., high confidence predictions)” and “quantifying confidence bounds on the forecasted data,” Indicating, that there is a confidence range that must be met).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Madsen-Wexler, such that decoding the brain activity signal comprises generating a plurality of predictions of future glucose levels at different time points, and using only those predictions that are above a predetermined confidence threshold, as taught by Wexler, to aid in providing real-time improvements health outcomes for patients with diabetes having few, irregular, and/or incomplete data (¶[0014] of Wexler).
Claims 16-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Madsen and Wexler, as applied to claim 11, and further in view of Grosman et al. (US 20160256629- Previously cited), hereinafter Grosman.
Regarding claims 16 and 17, Madsen-Wexler fails to disclose delivering a therapy to the user based on the predicted future glucose levels in order to manage glucose levels in a desired therapeutic range; wherein the therapy is insulin provided via an insulin pump.
Grosman teaches a closed loop insulin infusion system that is configured to provide therapy to a user based on the predicted glucose level and manage the user’s glucose (see abstract and para. [0540,0593,0634], insulin pump 1436).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Madsen-Wexler, such that therapy is delivered to the user based on the predicted future glucose levels in order to manage glucose levels in a desired therapeutic range and wherein the therapy is insulin provided via an insulin pump, as taught by Grosman, to aid in treating hypoglycemic attacks (see para. [0002] of Grosman). Additionally, the modification is merely combining prior art elements (glucose measuring methods) according to known methods (insulin infusion therapy) to yield predictable results.
Regarding claim 19¸ Madsen teach storing the predicted future glucose level in the memory (see para. [0014]), but fails to teach validating the predicted future glucose level based on a measured glucose level received from a glucose monitor.
Grosman teaches wherein the model calculated the predicted sensor glucose value and compares it against the actual senso glucose value to aid in generating appropriate alerts, warnings, or otherwise take corrective action (see para. [0593]).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Madsen-Wexler, such that the predicted future glucose level based on a measured glucose level received from a glucose monitor, as taught by Grosman, to aid in generating appropriate alerts, warnings, or otherwise take corrective action.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Madsen in view of Wexler and Grosman, as applied to claim 16, further in view of Kist et al. (Persistent blood glucose reduction upon repeated transcranial electric stimulation in men-AUG 2017- Previously cited), hereinafter Kist.
Regarding claim 18, Madsen-Wexler-Grosman fail to teach wherein the therapy is brain stimulation.
Kist teaches that transcranial direct current stimulation aids in managing blood glucose concentrations (see Abstract and Result section).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Madsen-Wexler-Grosman, such that the therapy is brain stimulation, as taught by Kist, to aid in lowering/controlling a user’s blood glucose (see conclusion of Kist). Additionally, the modification is merely applying a known technique (brain stimulation) to a known methods (blood glucose measurements) ready for improvement to yield predictable results.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Madsen in view of Wexler, as applied to claim 25, further in view of Shanahan et al. (US 20210327580), hereinafter Shanahan.
Regarding claim 25, Madsen teaches generating a feature vector in which frequency bands across a plurality of electrode channels are flattened into a single feature vector (¶[0045-46], “he EEG signals are digitized and sampled to provide 64 samples per second with a signal dynamic range of 12 bit and one LSB (least significant bit) set at 1 .mu.V” and “The first block is fed with the 64 Hz sampled EEG data stream and converts this stream to a 1 Hz feature vector stream.” That is, the EEG vector is converted to a 1 Hz stream. ¶[0049], “The feature extraction and normalization functionality is shown in FIG. 2. Each feature normalization block (1:N) (in FIG. 1) receives the full EEG stream.” The vector is converted into a feature block.); selecting a subset of features from the feature vector (¶[0049], “There is a filter in each feature block that extracts specific characteristics of the EEG.” That is, after the conversion, specific characteristics/features are selected). Madsen-Wexler fail to teach using a least absolute shrinkage and selection operator (LASSO) model to select the subset of features; regularizing the selected subset of features; and providing the regularized to the trained prediction model.
Shanahan teaches a method stratifying a patient with irritable bowel syndrome (IBS), e.g. a medical condition, which comprises detecting the presence, absence, or abundance of multiple bacteria in a sample, generating a patient profile, and operating a trained classifier on the patient profile to output a singal stratifying the patient into either having the condition or not (abstract). The method includes identifying features from a first and second subset of profiles using LASSO (step 301) because LASSO is used to improve accuracy and interpretability of models by efficiently selecting features (¶[0091]). The LASSO then selects predictive features to train the classifier (¶[0093], step 301), optimized in step 302 (¶[0094]), data points with high correlation to healthy patients are output in step 303 (¶[0095]), and features identified using LASSO are used to generate a random forest to optimize the accuracy of the trained classifier in step 304 (¶[0096] and fig. 3). In step 107, a random forest is generated to make the determination of the medical condition (fig. 3 and ¶[0101-106]). The method of fig. 3 (step 301) uses an elastic-net penalty with LASSO to compute a regularization path, which is then input into the trained prediction classifier/random forest (¶[0101-106], “The elastic-net penalty alleviates these issues, and regularizes and selects variables as well” indicating that it is describing the selection process in 301).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Madsen-Wexler, to use a least absolute shrinkage and selection operator (LASSO) model to select the subset of features; regularize the selected subset of features; and provide the regularized features to the trained prediction model, as taught by Shanahan, to improve accuracy and interpretability of models and optimizing the accuracy of trained classifiers.
Response to Arguments
Applicant's arguments filed 08/18/2026 have been fully considered but they are not persuasive.
Applicant contends that the two stage architecture overcomes 35 U.S.C. 112(a) written description rejection, on page 7 of the Remarks. Examiner disagrees. The description must include more than just the what is being input and what is being output. In this case, it appears that the model is a “black box” that receives input and outputs a value, but is unknown how the inputs are processed. Applicant states that the specification generally discloses linear models or more complex predictive machine learning models, on page 7 of the Remarks. However, this is insufficient to satisfy possession of the claimed invention.
Applicant’s arguments related to 35 U.S.C. 101 have been considered. Without conceding to the arguments, Examiner has updated the 35 U.S.C. 101 rejection to fall within a Mathematical Concept, abstract idea. As such, Applicant’s arguments are moot and must now address the amended rejections.
Applicant’s arguments related to Wexler ‘916 have been considered and found persuasive based on the amendments. Thus, the rejection now relies on Wexler ‘916 for the rejection. Any related arguments to Wexler ‘916 are therefore moot.
Applicant argues that Madsen teaches away from the claimed two-stage personalized trained and therefore cannot be combined with prior art that does use a two stage model architecture, on page 11 of the Remarks. Examiner disagrees. Applicant is invited to point out where Madsen explicitly teaches that an individual’s data cannot be used to train the model. As best understood by Madsen’s specification (¶[0076-77]), the model is trained on population data. However, this is not the same as teaching away. Therefore, references that use both individual specific and population data to classify blood glucose data would be obvious, as is the case in both Wexler’s specifications.
Applicant contends that Madsen does not teach obtaining spectral profiles of intracranial activity, on page 13 of the Remarks. Examiner disagrees. It appears applicant is conflating intracranial EEG electrodes and intracranial activity, which are mutually exclusive. Which are also different than intracranial recordings. Madsen teaches that there is an alternative to subcutaneous implantation and that they can be implanted beneath the skull thereby satisfying both intracranial electrodes and intracranial recordings (¶[0041] of Madsen). Moreover, Madsen records EEG signals representing brain signals and separates them based on their frequencies to obtain spectral profiles (¶[0044-54] of Madsen). Thus, Applicant’s arguments noted above are unpersuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Diamanti teaches one or more regularization process is implemented as two (2) regularization processes respectively utilizing the Least Absolute Shrinkage and Selection Operator (LASSO, or L1) regularization method and the Ridge Regression (RR or L2) regularization method. Both LASSO (L1) regularization and Ridge Regression (L2) regularization add a respective regularization term to a modeling equation in order to prevent the coefficients from overfitting to the training data. US 20190370694
Rebec teaches “adaptive algorithm” or “learning algorithm” is to be given its ordinary and customary meaning to a person of ordinary skill in the art, and refers without limitation to an algorithm that can be trained on user specific data (e.g., current and/or historical user specific data). The adaptive algorithm can be used to ensure adjustments to a particular set of data are reflective of a particular user's physiology and/or known environmental conditions. US 20210228114
Tran teaches systems and methods disclosed for monitoring a user by calibrating one or more noninvasive sensors to track a user glucose level at one or more user physical activity conditions; generating a calibration based on the one or more user conditions; and in real time detecting a current user condition and applying the calibration to accurately estimate the user glucose or insulin level. US 20210212606
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/MARTIN NATHAN ORTEGA/Examiner, Art Unit 3791 /TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
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