DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 24 August 2026 has been entered.
Status
This Office Action is in response to the communications filed on 10 August 2026 and 24 August 2026. Claims 2-3, 6-7, 9, 12, 15-22, 24-25, 27, and 32 are canceled currently or previously, claims 1, 4, 23, 26, and 28-31 are amended, and new claims 34-35 have been added; therefore, claims 1, 4-5, 8, 10-11, 13-14, 23, 26, 28-31, and 33-35 are pending and presented for examination.
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 .
Response to Amendment
A summary of the Examiner’s Response to Applicant’s amendment:
Applicant’s amendment does not overcome the rejection(s) under 35 USC § 101; therefore, the Examiner maintains the rejection(s) while updating phrasing in keeping with current examination guidelines.
Applicant’s amendment overcomes, in a manner, the rejection(s) under 35 USC §§ 102 and/or 103; therefore, the Examiner indicates allowability over the prior art of record.
Applicant’s arguments are found to be not persuasive; please see the Response to Arguments below.
Claim Interpretation
The Examiner notes that the claims indicate “comparing a change in a time in range of first portion of glucose data”, where it may be considered unclear what “a change in a time in range of first portion of the glucose data”. The Examiner notes that this is being interpreted as a range of glucose data (e.g., high, low, normal/intermediate) and a time within a particular range such that there is a change in the amount or portion of time that is within a particular glucose range. See, e.g., Applicant ¶ 0057.
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 1-2, 4-8, 10-14, 23-24, and 26-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to a method (claims 1-2, 4-8 and 10-14) and a system (claims 23-24 and 26-31), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites a computer-implemented method of using a supervised machine learning model to determine user non-adherence to a dose recommendation, the method comprising: receiving, by a processor, glucose data of a user for a time period from an on-body unit configured to be worn on a skin surface of the user, the on-body unit comprising: a glucose sensor configured to be in contact with interstitial fluid of the user and monitor glucose levels of the user; and sensor electronics coupled to the glucose sensor and configured to wirelessly transmit glucose data of the user; outputting, by the processor on a display of an electronic device, a dose recommendation to the user during the time period; training, by the processor, a compliance classifier based on the glucose data of the user, wherein the training comprises: generating a feature set having one or more features of the glucose data, wherein the one or more features comprises a time in range; determining a predictive ability of the trained compliance classifier, wherein the predictive ability comprises distinguishing user adherence to dose recommendations from user non-adherence to dose recommendations; and selecting one or more features of the feature set to maximize the determined predictive ability, wherein the one or more features comprises a first time in range associated with a first set of glucose profiles corresponding to user adherence to dose recommendations and a second time in range associated with a second set of glucose profiles corresponding to user non-adherence to dose recommendations; classifying, by the processor using the supervised machine learning model comprising the trained compliance classifier, glucose data received after the dose recommendation is outputted wherein the classifying comprises comparing a change in a time in range of a first portion of the glucose data from before the dose recommendation was outputted to a second portion of the glucose data from after the dose recommendation was outputted; determining, by the processor, based at least in part on the classification, user non-adherence to the dose recommendation or user adherence to the dose recommendation; and performing, by the processor, an action based at least in part on the determination, wherein the action comprises: based on a determination of user non-adherence to the dose recommendation, preventing a second dose recommendation from being outputted to the user; and based on a determination of user adherence to the dose recommendation, permitting the second dose recommendation to be outputted to the user.
Independent claim 23 is analyzed in the same manner as claim 1 above since it is directed to a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for determining adherence or non-adherence to a dose recommendation, the operations comprising the same or similar operations and components as at claim 1.
The dependent claims (claims 4-5, 8, 10-11, 13-14, 26, 28-31, and 33-35) appear to be encompassed by the abstract idea of the independent claims since they merely indicate outputting a second recommendation or a communication for remedial action (claims 4, 26, and 33), who the output is to (claim 5), applying a Kolmogorov-Smirnov equality test (claims 8 and 28), applying a hypothesis test (claims 10, 14, 29, and 31) including fitting log-normal distribution parameters (claim 11), predicting and comparing dose mismatch values as a basis for the adherence determination (claims 13 and 30), and/or the second dose recommendation being a change to the dose recommendation (claim 34) based on glucose data received after output of the dose recommendation (claim 35).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of tracking glucose data to identify a patient did not adhere to a recommended dose; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within the following grouping(s) of subject matter, or a mix of the groupings:
Mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion) – based in part, or at least, on the monitoring glucose levels, classifying data as compliant or non-compliant, and/or determining adherence or non-adherence;
Certain methods of organizing human activity (e.g. … commercial or legal interactions such as … advertising, marketing or sales activities/behaviors, or business relations; and/or managing personal behavior or relationships between people such as social activities, teaching, and following rules or instructions) – based in part, or at least, on the determining dose recommendation adherence, and/or outputting a recommended dose and or action; and
Mathematical concepts (e.g., relationships, formulas, equations, and/or calculations) – based in part, or at least, on the use of machine learning, and/or the using, comparing, and differences in glucose data (including at some of the dependent claims).
The Examiner notes that the claims appear primarily directed to the observations, evaluations, and judgments made regarding glucose data from a patient in response to a dose recommendation (of, e.g., insulin or other such medication) - when the glucose data is out of range too long or too often (i.e., “time in range” is not what was expected or predicted), the claims deem this or label this as “non-adherence” even though the patient or user may well have fully adhered to the dose recommendation, but the patient’s body has not responded as predicted or in an anticipated manner. Therefore, the claims appear primarily directed to mental processes. However, since the claims also impact or include human activities such as managing personal behavior, relationships, etc., as well as the use of mathematical concepts, the Examiner notes the additional groupings additionally.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are: receiving, by a processor, from an on-body unit configured to be worn on a skin surface of the user, the on-body unit comprising: a glucose sensor configured to be in contact with interstitial fluid of the user and; and sensor electronics coupled to the glucose sensor and configured to wirelessly transmit; the outputting being by the processor and on a display of an electronic device; classifying, by the processor; determining, by the processor, and performing an action by the processor. These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
The receiving, as well as the act of outputting (even if/when/though the outputting is considered part of the abstract idea), are both extra-solution activity that is, as such, considered to be insignificant (see, e.g., MPEP § 2106.05(d)(I)).
The processor (for, e.g., receiving, outputting, classifying, determining, etc.) is described and claimed at a high level of generality (see, e.g., Applicant ¶¶ 0022, 0046, 0049, and 0087, as submitted) such that the recitation of a processor is apparently a mere indication of applying the abstract idea using a generic computer.
The indication of a body unit with an interstitial fluid sensor and electronics to wirelessly transmit indicates a typical or general continuous glucose monitoring system. There is no claim, description, or indication of any change or invention to such a monitor, and the claim merely indicates using the monitor device to gather data, i.e., the body unit, sensor, and transmission electronics are merely the use of the monitor for what it was apparently designed and intended to do. As such, this does not constitute a practical application.
The display of an electronic device is, similar to the processor as analyzed above, is described and claimed at a high level of generality (see, e.g., Applicant ¶¶ 0021-0022, 0030-0031, 0033, 0093-0094, as submitted) such that the recitation of a processor is apparently a mere indication of applying the abstract idea using a generic computer.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity.
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. The computers, such as desktops, laptops, and/or tablets, as well as the memory and storage described by Applicant are merely generic or general-purpose computers, i.e., described at a high level of generality (see, e.g., Applicant ¶¶ 0088-0091, as submitted).
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
Allowable Subject Matter
Claims 1, 4-5, 8, 10-11, 13-14, 23, 26, 28-31, and 33 are indicated to be allowable over the prior art.
The following is a statement of reasons for the indication of allowable subject matter:
The closest art of record appears to be King (U.S. Patent Application Publication No. 2009/0036753), which indicates compliance with dosing regimens. King, though, does not appear to disclose the machine learning aspects and preventing a second dose recommendation; however, McRaith et al. (U.S. Patent Application Publication No. 2017/0329917, hereinafter McRaith) teaches the supervised machine learning and Zivitz et al. (U.S. Patent Application Publication No. 2007/0078818, hereinafter Zivitz) teaches preventing a second dose recommendation. None of the above, though, appear to indicate a predictive ability to distinguish adherence or non-adherence based solely on the blood glucose readings after a dose recommendation (i.e., a conclusion that when blood glucose differs from an expected result it is necessarily non-adherence to the dose recommendation rather than any from any other cause).
Response to Arguments
Applicant's arguments filed 10 August 2026 have been fully considered but they are not persuasive.
Applicant first argues the 101 rejection (Remarks at 8-12), alleging generally that Applicant traverses based on the amendment (Id. at 8). Applicant then alleges “the claims as a whole integrate the alleged abstract idea into a practical application and provide an improvement to the technological field of glucose monitoring and adherence detection systems for diabetes management at prong II of Step 2A” (Remarks at 9). However, there is no change indicated for the sensor, transmitting, display, or training of a supervised machine learning classifier – the only apparent, potential change is what the recommended dose would be
Applicant’s arguments continue to be based on the assumption or conclusion that if or when the glucose data does not respond (for whatever reason) as projected by the model, it is necessarily due to non-adherence. The Examiner notes that there is NO actual tracking of whether the correct insulin is used, whether diet, sleep, exercise or activity are consistent, or any other factors (see, e.g., King at 0012, 0034-0035, 0077-0081 as indicating other factors that can or should be considered, see also the American Diabetes Association handout at the pertinent prior art below, also further indicating other factors that can or should be considered). The claims are based on the presumption or assumption that ANYTIME the blood glucose readings are consistent with a time in range profile – regardless of any actual cause or effect such as any other factor that may produce a different than anticipated glucose reading – the reason or cause is “adherence” or “non-adherence”.
Applicant argues analogy to Diehr (Remarks at 9-10); however, the claims are apparently the exact opposite of the Diehr case – where Diehr actually measured the factors that impact tire curing (e.g., time and temperature) and continually recalculated to optimize the result of the rubber being properly cured, Applicant’s claims apparently completely ignore most, many, or even all relevant factors that would produce a result of being within an expected time in range – there are NO inputs actually measured, only a result is measured. The instant claims would be analogous to Diehr only determining if a tire is cured, and merely or completely guessing at whether the temperature was set at all, whether the temperature was too high or too low, whether the time was too long or too short – and based solely on a cure determination that indicates the tire was not cured properly, preventing anyone or someone from providing information on how or whether to change an input such as time or temperature. Analogy to Diehr therefore appears unreasonable.
Applicant then argues Example 47, claim 3 (Remarks at 10); however, the analysis of Example 47 claim 3 specifically indicates “The claim recites the additional elements of ‘(d) detecting a source address associated with the one or more malicious network packets,’ ‘(e) dropping the one or more malicious network packets,’ and ‘(f) blocking future traffic from the source address.’” (p. 12, third paragraph of the “Step 2A, Prong Two” analysis), and then “Steps (d)-(f) provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets” (p. 12, last paragraph – that rolls over onto p. 13). Therefore, Example 47 claim 3 actually identifies the source of the problem and takes measures to remediate, whereas the instant claims do not attempt to monitor or detect why, or by what source, readings may not be in range – the claims merely detect readings afterward and when they are not in range, prevent any correction or adjustment (a second recommendation is prevented). Therefore, analogy to Example 47, claim 3 is also unreasonable.
Applicant then alleges that “A human cannot perform the claimed function of automatically preventing or permitting the therapy management system's own output based on a trained classifier's evaluation of glucose data” (Remarks at 10); however, first, the claims do NOT say that the preventing or permitting is done “automatically”, but rather only that this is performed “by the processor” (which apparently includes using the processor as a tool). Second, even if the claims indicated automatic prevention or permitting as at the argument, this would ONLY be true based on computer implementation – a human can assess glucose readings (such as a doctor assessing patient data), including whether they are in a projected or anticipated range and whether the readings are within that range for a time period (i.e., time in range), and based on whether the readings are acceptable or not, either provide a further recommendation, or stop communicating (i.e., preventing a second dose recommendation). This appears to be (or literally is) the same arrangement as a generic client-server would have – sending data to a server so that a recommendation can be provided back after analysis.
Therefore, Applicant’s arguments are not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Continuous Glucose Monitoring, from Medtronic, dated 2 June 2016, downloaded 20 November from the Archive.org WayBack machine at https://web.archive.org/web/20160602144143/https://www.medtronicdiabetes.com/treatments/continuous-glucose-monitoring describes Medtronic’s Continuous Glucose Monitoring (CGM) (at 1) as working by being “under the skin to measure glucose levels in tissue fluid” (at 2), i.e., interstitial fluid monitoring.
Cengiz et al., A tale of two compartments: interstitial versus blood glucose monitoring. Diabetes Technol Ther. 2009 Jun;11 Suppl 1(Suppl 1):S11-6. doi: 10.1089/dia.2009.0002. PMID: 19469670; PMCID: PMC2903977. Downloaded from https://pmc.ncbi.nlm.nih.gov/articles/PMC2903977/ on 20 November 2024, explaining the differences between interstitial fluid (IF) and plasma/blood glucose readings (at S-11), including a “Timeline for Continuous Glucose Monitors (CGMs)” (at S-13-14), describing that “three of the current CGM devices in common use provide sensor glucose levels in real time and use glucose oxidase-based electrochemical methods. Sensor signals are transmitted by wireless radiofrequency telemetry to the receiver” (at S-14), and that these new CGM systems have “opened up a new compartment that has not been heretofore accessible—the IF space” (at S-14, under the “What Does the Future Hold?” heading).
Schuster (U.S. Patent Application Publication No. 2014/0005596) discloses that “A mobile phone may be integrated with the injection device. This allows for further reducing the amount of equipment to be carried by the patient. The processing resources of the mobile phone may be shared to control the functions of the injection device and the functions of the blood glucose measuring device if applicable. Data and user interfaces of the mobile phone such as visual display, audio output, vibration alarm, keyboard, touch screen, wireless connections such as Bluetooth.RTM., SMS, etc., may be shared to allow interaction with the injection device and with the blood glucose measuring device if applicable. This may be used for compliance monitoring or for reminding the user to administer their dose of drug. The set units to be administered may be displayed. Voice output of data may be used to assist visually impaired users. The user effort to deliver their drug may be reduced to just pushing at least one button or performing at least one gesture on the touch screen. The injection device may remind the user to replace needles or drug containers as required. The injection device may supervise the storage conditions, e.g. the storage temperature of the container and may issue a warning if the storing conditions are out of the specification requiring the container to be replaced. Blood glucose measurements may be stored, processed and graphically displayed. The injection device may recommend the appropriate dosage depending on the blood glucose measurement. The injection device may store and graphically display an injection history comprising the number of units delivered” (Schuster at 0013, with similar indications at 0031).
Mayer et al. (U.S. Patent Application Publication No. 20140278468, hereinafter Mayer) discloses “a self-contained medication monitor for producing medication usage data may be provided. The self-contained medication monitor may include … having a target agent sensor for detecting a target agent in a biological sample from a user.… The self-contained medication monitor may also include a processor that produces information and/or recommendations based on detected removal of the medication and an amount of glucose sensed, and that outputs the information and/or recommendations to the patient or a caregiver. The processor may also output a recommendation to a health care provider, and may assist the health care provider in determining target drag levels or benchmark chug levels. The processor may output raw glucose concentration data, compare the amount glucose sensed with a pre-determined or other baseline glucose level, and/or quantitatively determine the amount of the target agent present in the biological sample based on a difference between the baseline glucose level and the amount of glucose sensed. The self-contained medication monitor may also include a display device configured to display the information and/or recommendations to the patient. Such recommendations may include, for example, a reminder to take a medication in accordance with a pre-established regimen, a recommendation to expedite or delay a dose of medication, and/or a recommendation to consult with a caregiver.” (Mayer at 0025), and further dose compliance pattern and corresponding adjustments are described at least at 0047-0053 of Mayer.
Farnsworth, Carolyn, Useful tips to help avoid insulin stacking, Medical News Today, downloaded from https://www.medicalnewstoday.com/articles/insulin-stacking on 11 April 2025, dated 8 February 2022, indicating that “While individuals may wish to lower their blood glucose as quickly as possible, taking multiple doses at close intervals can result in blood sugar levels becoming too low. This is known as insulin stacking, which can lead to negative side effects.” (at p. 1).
Van Orden et al. (U.S. Patent Application Publication No. 2019/0272912, hereinafter Van Orden) indicates “Systems and methods for treating a subject are provided. A first dataset comprising timestamped autonomous glucose measurements of the subject over a first time course is obtained. A second dataset, associated with a standing insulin regimen for the subject over the first time course and comprising insulin medicament records, is also obtained. Each record comprises a timestamped administration event including an amount and type of insulin medicament administered into the subject by an insulin delivery device. The first and second datasets serve to calculate a glycaemic risk measure and an insulin sensitivity factor of the subject during the first time course, which are used to obtain a basal rate titration schedule and a fasting blood glucose profile model over a subsequent second time course for the subject. The model predicts the fasting blood glucose level of the subject based upon amounts of insulin medicament administered into the subject” (Van Orden at Abstract) and “Once clustering has been completed (e.g., as described in blocks 426 and 428 of FIG. 4E) and the subject enters the second time course, individual patient treatment response within each group will be processed continually using a supervised machine learning decision model based on regression analysis, multiclass classification, or a combination of learning techniques” (Van Orden at 0202).
American Diabetes Association, Good to Know: Factors Affecting Blood Glucose. Clin Diabetes. 2018 Apr;36(2):202. doi: 10.2337/cd18-0012. PMID: 29686462; PMCID: PMC5898168, downloaded 11 May 2026 from https://pmc.ncbi.nlm.nih.gov/articles/PMC5898168/pdf/202.pdf , indicating various factors that can make blood glucose rise (e.g., food/diet, activities, medication, side effects of other medications, illness, stress, pain, menstrual periods, dehydration) or fall (e.g., food/diet, alcohol consumption, medication, side effects of other medications, activities).
Forlenza GP. Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy. Diabetes Technol Ther. 2019 Jun;21(S2):S24-S28. doi: 10.1089/dia.2019.0077. PMID: 31169433; PMCID: PMC6551985. Downloaded from https://pmc.ncbi.nlm.nih.gov/articles/PMC6551985/ on 24 September 2026, indicating use of a continuous glucose monitor (CGM) and an interoperable continuous subcutaneous insulin infusion (CSII) pump by multiple daily injection (MDI) users (at S2-4) and further discussing “automated decision support (ADS) systems, or the use of computer-based artificial intelligence technology to provide feedback or guidance to patients or clinicians. In the case of CSII users, ADS could provide recommendations to adjust pump settings, whereas for MDI users, it could suggest changes to basal insulin, carbohydrate ratios, or even sliding scale dosing.” (at S2-5).
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685
