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 .
Applicant Argument Response
The examiner considered all applicant arguments presented in the remarks filed on 02/18/2026, page 8, regarding 35 U.S.C. 112(b) as follows:
Applicant argues that the terms proximate to and the use of both pre-defined threshold and pre-determined confidence threshold in Claims 1 and 14 are moot under § 112(b), as the amendments replace proximate to with within and consolidate the threshold language to a single pre-defined confidence threshold.
The Examiner agrees. The amendments resolve both the antecedent ambiguity and the redundant-threshold ambiguity, consequently, the amended claim language is reasonably clear and definite therefore the § 112(b) non-final rejection is withdrawn.
The examiner considered all applicant arguments presented in the remarks filed on 02/18/2026, page 8-13, regarding 35 U.S.C. 101 subject matter eligibility as follows:
Applicant argues that Claim 14 was not independently analyzed under § 101 Step 2A Prong One and that conclusory treatment deprives applicant of the opportunity to rebut. The Examiner respectfully disagreed because under proper BRI, Claims 1 and 14 share the exact same underlying abstract idea of gathering health data, identifying gaps, applying rules to estimate substitute data, and using a confidence score to decide whether to accept the substitute or collect more data. The record shows the previous Office Action explicitly evaluated both independent claims together, stating the independent claims 1 and 14 abstract idea recite a process... and explicitly analyzed their shared additional elements under Prong Two and Step 2B. Thus, the applicant's position is not persuasive because examiners are permitted to group claims that recite substantially similar judicial exceptions and share similar additional elements, which provides sufficient notice of the rationale to allow the applicant to meaningfully rebut the rejection on the merits. Therefore, the rejection is maintained.
Applicant argues that Claims 1 and 14, adding… the substitute… data to the testing period data and extending… the testing period, use the exception to effect a particular treatment for a diabetic condition under MPEP § 2106.04(d)(2).
The Examiner respectfully disagreed. Under MPEP § 2106.04(d)(2), in order to qualify as a treatment or prophylaxis limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. Claims 1 and 14 recite only data-management acts receiving, determining, imputing, calculating, adding, and extending none of which administer therapy. Merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the treatment or prophylaxis consideration. Therefore, the rejection is maintained.
Applicant argues that Claims 1 and 14 reflect an improvement to computer functioning reduced processing resources and wireless-communication bandwidth citing Spec. [0063] and [0076].
The Examiner respectfully disagreed. Under MPEP § 2106.05(a), a technical improvement must be reflected in the claim, not merely described in the specification. Claims 1 and 14 recite only at least one computing device executing generic receive/determine/impute/add/extend operations; the specification confirms generic hardware, reciting that the processor performs signal coding, data processing, power control, image processing, input/output processing, or any other functionality (Spec. [0166]). Thus, the applicant's position is not persuasive the claim merely applies the abstract idea on generic computing hardware per MPEP § 2106.05(f). Therefore, the rejection is maintained.
The examiner considered all applicant arguments presented in the remarks filed on 02/18/2026, page 8-13, regarding 35 U.S.C. 103 subject matter eligibility as follows:
Applicant argues the examiner's note Claim 14 is rejected as being similar to claim 1, and claim 15 is rejected as being similar to claims 2 and 3 fails to establish a prima facie § 103 case for Claims 14 and 15.
The Examiner respectfully disagreed. Under MPEP § 2143, a prima facie case is established when each limitation is mapped to the references. Claim 14's shared limitations testing period/protocol, imputation, confidence-level gating, and extending are mapped through the Claim 1 analysis to Shima [0071], [0076], [0127]–[0128], [0158] and Patek [0052]. Claim 14’s limitation directed to determining that the user consumed the meal based on a blood glucose level… being greater than a pre-defined blood glucose threshold is taught by Shima [0127], as explained for substantially similar limitations of claim 1 on Pgs 17-18 of the non-final rejection. Claim 15 reads on Shima [0084], [0145]–[0146] (cohort/population-based model) and [0071], [0084] (user-specific time-weighted model) as previously explained for substantially similar claims 2-3 on pages 21-22 of the non-final rejection. Thus, the applicant's position is not persuasive because the mapping is supplied through the shared-limitation analysis and clarified here. Therefore, the rejection is maintained.
Applicant argues that Shima fails to teach a testing period configured for collecting testing period data according to a testing protocol, wherein the testing protocol defines parameters of the testing period data for reliable capture to enable one or more of disease progression analysis or therapy analysis.
The Examiner respectfully disagreed. Under proper BRI per MPEP § 2111, a testing protocol that defines parameters… for reliable capture reads on any rule set specifying minimum data volumes required for downstream health analysis over a defined window. Shima Fig. 9I-2 recites: January requires a certain level of data coverage at least 3 meal/food logs a day and at least 20 hours of heart rate and glucose data… we recommend obtaining 23 hours of data every day, applied over Shima's defined monitoring window ( [0076], Default time period of 7 days ). Those parameters enable Shima's personalized recommendations (therapy analysis) and metabolic/lifestyle scoring (disease-progression analysis, [0145]–[0149]). Thus, the applicant's position is not persuasive because Shima's coverage rule within a defined window reads on the claimed testing protocol. Therefore, the rejection is maintained.
Applicant argues that Shima only adjusts display time frames and does not extend the testing period in response to any of the four claimed triggers, and that the examiner admits Shima does not teach confidence level.
The Examiner respectfully disagreed. Claim 1 recites extending… the testing period in response to one or more of, so the limitation is met when any single trigger reads on the combination. Under proper BRI per MPEP § 2111, extending… the testing period reads on continuing data collection beyond the originally scheduled window when a data-quality condition is unmet. Shima's testing protocol requires a minimum coverage for high quality insights (Fig. 9I-2), and Patek teaches validating data against a confidence value exceeding a predetermined threshold ( [0052]). Under MPEP § 2143(I)(A) and (C), a POSITA integrating Patek's confidence check into Shima's coverage-driven framework would continue collection when the confidence falls below the threshold, because Shima's Fig. 9I-2 message ( data coverage at least… 20 hours ) already instructs further data collection on insufficient coverage. Thus, the applicant's position is not persuasive because the combination teaches extending in response to the confidence-level trigger, which alone satisfies the one or more of language. Therefore, the rejection is maintained.
Applicant argues that Patek's generic confidence-threshold disclosure does not teach adding… the substitute… data to the testing period data when the confidence level is greater than a pre-defined confidence threshold.
The Examiner respectfully disagreed. Shima teaches imputing a substitute meal by derive a possible food corresponding to the BG data based on data from a subject ( [0128]) and adding the derived entry to the food log [0130]–[0132]). Patek teaches gating data acceptance on whether it has been determined to have a confidence value exceeding a predetermined threshold ([0052]). Under MPEP § 2143(I)(C) use of a known technique to improve a similar device in the same way a POSITA would apply Patek's confidence-gated validation to Shima's imputed entry before it is added to the coverage-compliant testing-period dataset used for therapy/progression analysis, yielding the claimed adding… when the confidence level is greater than a pre-defined… threshold. Thus, the applicant's position is not persuasive because the combination, not Patek alone, supplies the limitation. Therefore, the rejection is maintained.
Claim Rejections - 35 USC § 112
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.
Claim 1- 16 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 1 and 14 recite reliable capture a relative term render indefinites because a skilled person cannot determine, with reasonable certainty, the boundary the protocol must meet to achieve reliable capture.
Claim 16 recites the testing data, but no antecedent basis exists. Claim 1 introduce use testing period data . They never define or distinguish testing data. This mismatch makes it unclear whether the testing data in claim 16 is the same dataset as testing period data in claim 1 or a broader, different category.
Note: The dependent claims 2-13, and 15 are rejected for being indefinites for the above rejection.
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 1-16 are rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception without reciting elements that integrate the exception into a practical application or provide an inventive concept amounting to significantly more than the exception itself. The amendments to Claims 1 and 14 and the addition of Claim 16 do not cure the defect each amendment recites rules, purposes, or narrower bounds for the same abstract mental and organizational workflow.
Step 1 Statutory Categories
Claims 1-16 are drawn to a process under MPEP § 2106.03 because each recites a series of acts including receiving, determining, imputing, calculating, adding, and extending.
Step 1 is satisfied, therefore claims transition to prong one analysis below.
Step 2A, Prong One Judicial Exception Analysis
Step 2A, Prong One asks whether the claims recite a judicial exception.
Representative Claim 1 recite the following non-bold abstract idea and additional elements in bold:
Claim 1.
A method comprising: receiving, via at least one computing device, blood glucose data from a blood glucose monitor, the blood glucose data comprising a plurality of blood glucose levels of a user that are measured during a predefined testing period configured for collecting testing period data according to a testing protocol, wherein the testing protocol defines parameters of the testing period data for reliable capture to enable one or more of disease progression analysis or therapy analysis to be performed to treat a diabetic condition of the user at an end of the testing period;
receiving, via the at least one computing device, the testing period data associated with the user during the predefined testing period the testing period data comprising:
dietary intake data associated with a plurality of meals consumed by the user during the testing period,medication data associated with a plurality of medication doses taken by the user during the testing period, andactivity data associated with a plurality of pre-scheduled activities for the user during the testing period;
determining, via the at least one computing device, that a portion of the testing period data is missing based on an analysis of the received data;
imputing, via the at least one computing device, the missing portion of the testing period data with substitute dietary intake data in response to determining that the missing portion of the testing period data comprises dietary intake data associated with a meal of the plurality of meals and determining that the user consumed the meal;imputing, via the at least one computing device, the missing portion of the testing period data with substitute medication data in response to determining that the missing portion of data comprises medication data associated with a medication dose of the plurality of medication doses and determining that the user took the medication dose;imputing, via the at least one computing device, the missing portion of the testing period data with substitute activity data in response to determining that the missing portion of data comprises activity data associated with a pre-scheduled activity of the plurality of pre-scheduled activities and determining that the user participated in the pre-scheduled activity;calculating, via the at least one computing device, a confidence level associated with the substitute dietary intake data, the substitute medication data, or the substitute activity data;adding, via the at least one computing device, the substitute dietary intake data, the substitute medication data, or the substitute activity data to the testing period data when the confidence level is greater than a pre-defined confidence threshold; andextending, via the at least one computing device, the testing period in response to one or more of:
determining that the user did not consume the meal based on an analysis of one or more of the plurality of glucose levels within a first time period associated with the meal,determining that the user did not take the medication dose based on an analysis of one or more of the plurality of glucose levels within a second time period associated with the medication dose,determining that the user did not participate in the pre-scheduled activity based on an analysis of one or more of the plurality of glucose levels within a third time period associated with the pre-scheduled activity, or determining that the confidence level is below the pre-defined confidence threshold.
Under its broadest reasonable interpretation, Claim 1 reads on the following non-bold above functional sequence that a person can perform mentally:
Intake data under a protocol, spot a gap, infer and substitute, score confidence and decide the substitution is acceptable in the context of blood glucose data to treat or progression diabetic analysis.
Claim 14 is a narrower species of the same sequence because it applies that same logic specifically to dietary data, including deciding meal consumption from a blood-glucose value exceeding a predefined threshold within the meal-associated time period.
That sequence fits the abstract-idea groupings in MPEP 2106.04(a)(2). Identifying the missing data, inferring a substitute, scoring confidence, and deciding whether to accept the substitute or extend the period are evaluations and judgments that fall within mental processes, while applying a testing protocol that defines what data to collect and how often adds a rule-based workflow that also aligns with managing personal behavior or following rules or instructions.
A human analogy evidences how and why the claims recites abstract idea:
A diabetes coach hands a patient a three-day paper logbook with a rule sheet the testing protocol stating which data to record and how often. The coach reviews the returned log, notices the 1:00 PM lunch entry is blank, checks the finger-stick reading inside the lunchtime window, sees it above the coach's expected range, and infers the patient ate lunch. The coach pencils in an estimated meal and notes high confidence next to it. When confidence is high across the day's imputations, the coach accepts the log and sends it to the doctor; when confidence is low, the coach asks the patient to log another day. Every step setting the protocol, reading the log, spotting the blank, inferring the meal from the sugar reading within the event window, writing in a substitute, scoring confidence, deciding whether to extend can be done mentally by the coach without any device. The amendment additions testing protocol, parameters, within a time period map to the coach's rule sheet and the coach's event-window check. No step requires a device, a sensor, or any tangible element to be performed.
Dependent Claims Analysis
The dependent claims 2-13 and 15-16 are also directed to an abstract idea as they merely refine the abstract processes recited in the independent claims.
Claims 2-3, 7-8, and 15: These claims recite the specific mathematical models used for imputation and threshold setting ( generic population-based blood glucose model, time-weighted blood glucose model ). Under BRI, these are different types of calculations or data sources used within the abstract mental process of estimation. They do not add a practical application but merely refine the abstract calculation itself, and thus inherit the Mental Process classification (MPEP § 2106.04(a)(2)).
Claims 4-6: These claims recite the specific inputs and rules for the determination steps ( analysis of one or more blood glucose levels, determining that a blood glucose level is greater than a pre-defined blood glucose threshold, and the basis for that threshold like carbohydrate content or dynamic fingerprint ). These limitations are further refinements of the abstract mental process of comparison and determination and inherit the Mental Process classification.
Claim 9: This claim recites the source of the data ( a continuous glucose monitor (CGM) or a spot-monitoring blood glucose (SMBG) meter ). This limitation is a generic data-gathering step, nominating a conventional sensor to supply data for the abstract analysis. Such insignificant data-gathering steps... do not integrate the abstract idea (MPEP § 2106.05(d)). The claim inherits the abstract idea.
Claim 10: This claim recites the trigger for the determination step by analyzing scheduled meals, scheduled medication doses, and pre-scheduled activities. This limitation is a further refinement of the data management rules and fits squarely within the Method of Organizing Human Activity classification.
Claim 11: This claim recites aggregating confidence levels to get a daily aggregate confidence level and making a decision based on it. This is another mathematical calculation and decision rule, inheriting the Mental Process classification.
Claim 12: This claim recites identifying... a progression or a regression and adjusting a configuration of an insulin pump. The identifying step is a mental conclusion derived from the imputed data. The adjusting step is a conventional, subsequent action that is not integrated with the imputation method itself; it is a field-of-use limitation that merely applies the abstract idea's output (MPEP § 2106.05(g)). The claim inherits the abstract idea.
Claim 13: This claim recites updating a learning model. This is a mathematical step of refining the model with new data and is part of the abstract Mental Process.
Claim 16: This claim recites under BRI wherein the testing protocol limits a number of imputations during the testing period for the reliable capture of the testing data to enable … disease progression analysis or therapy analysis … without starting another testing period a rule capping how many times the abstract estimation step may run inside the managed workflow, which is certain methods of organizing human activity including managing personal behavior … including following rules or instructions per MPEP § 2106.04(a)(2)(II).
Because Claims 1-16 recite a single abstract idea collecting and evaluating health data under a rule-governed observation protocol, inferring and substituting values for missing entries using threshold comparisons within event windows, scoring the substitutes, and deciding whether to accept them or extend the observation the analysis proceeds to Step 2A, Prong Two to evaluate whether the remaining additional elements integrate that abstract idea into a practical application.
Step 2A, Prong Two Integration into a Practical Application
Prong Two asks whether the additional elements integrate the judicial exception into a practical application. Integration requires a specific mechanism — not a goal, a purpose, or a generic environment.
Independent Claims 1 and 14 Additional Elements
via at least one computing device. Reciting that each step is performed via at least one computing device links the abstract workflow to a generic computing environment. MPEP § 2106.05(h) generally linking the use of the judicial exception to a particular technological environment or field of use applies because the recitation adds no particular computing operation beyond what any general-purpose computer would perform. The recitation is also mere instructions to apply an exception under MPEP § 2106.05(f). The specification confirms the device is described at a generic level in the example of computing device 1200: The processor 1202 performs signal coding, data processing, power control, image processing, input/output processing, or any other functionality that enables the computing device 1200 to perform as described herein ((Patent pub. US20230301560A1) spec, par. 0166). No specific structural or algorithmic improvement is claimed.
blood glucose monitor. Reciting that blood glucose data is received from a blood glucose monitor supplies the data feeding the abstract analysis. MPEP § 2106.05(g) treats mere data-gathering as insignificant extra-solution activity, and MPEP § 2106.05(h) treats a recitation that links the exception to a particular field as a field-of-use limitation. The specification describes the monitor generically in the example of blood glucose monitoring device 900: The blood glucose monitoring device 900 is a CGM or FGM, for example, and Examples of blood glucose monitoring devices include, but are not strictly limited to, continuous glucose monitoring devices, flash glucose monitoring devices, and blood glucose meters that provide a single measurement of blood glucose levels from a blood sample in a spot monitoring process (Patent pub. Spec, par. 0141). No improvement to the sensor is claimed.
Viewed as a whole, Claims 1 and 14 instruct a generic computing device to receive data from a generic glucose monitor and execute the abstract mental sequence within a rule-governed observation window. The combination applies the abstract idea in its intended field without a specific technical mechanism that would elevate the claim to a practical application. Dependent Claims Analysis
The dependent claims add only minor limitations that fail to provide the necessary integration.
Learning Models (Claims 2-3, 7-8, 11, 13, 15):
These claims recite specific mathematical concepts and instructions ( generic population-based blood glucose model, time-weighted blood glucose model, daily aggregate confidence level, updating a learning model ).
(MPEP § 2106.05(f) - Mere Instructions): These elements fail to integrate the abstract idea because they are, themselves, abstract mathematical limitations. They are mere instructions for performing the abstract mental steps of imputing, calculating confidence, and determining. As such, they are further refinements of the abstract Mental Process idea itself (MPEP § 2106.04(a)(2)) and do not provide a practical application.
Rules and Triggers (Claims 4-6, 10):
These claims add specific rules ( greater than a pre-defined... threshold ) and triggers ( scheduled meals, scheduled medication doses ). These are further refinements of the abstract Mental Process and Method of Organizing Human Activity ideas, not integrating elements.
Generic Sensors (Claim 9):
This claim adds specific types of generic sensors ( a continuous glucose monitor (CGM) or a spot-monitoring blood glucose (SMBG) meter ). This fails to integrate the abstract idea because the sensors are merely a more specific source of the data, which is insignificant pre-solution activity (MPEP § 2106.05(g)). The claim is not directed to a new sensor, but to using data from a high-level sensor in an abstract analysis, which is a mere field-of-use limitation (MPEP § 2106.05(h)).
Post-Solution Activity (Claim 12):
This claim adds identifying... a progression or a regression (another mental step) and adjusting a configuration of an insulin pump. This fails to integrate the abstract idea because 'identifying' is a mental step, and 'adjusting an insulin pump' is a generic, subsequent action that is not itself part of the abstract analysis. This is insignificant post-solution activity (MPEP § 2106.05(g)) that merely links the abstract idea's output to a conventional piece of hardware in the diabetes field (MPEP § 2106.05(h)).
Claim 16 adds the imputation cap the testing protocol limits a number of imputations during the testing period … without starting another testing period. The cap is a rule that governs how often the abstract estimation step runs; it recites no hardware improvement, no data-structure improvement, and no algorithmic mechanism beyond the rule itself. MPEP § 2106.05(f) mere instructions to apply.
The dependent claim combinations add narrower abstract rules, mathematical refinements, a sensor species, a generic pump adjustment, and a rule cap. None introduces a specific technical mechanism applying the abstract idea in a meaningful way.
The additional elements, alone and in combination, do not integrate the abstract idea into a practical application. The analysis proceeds to Step 2B.
Step 2B Inventive Concept Analysis
Step 2B asks whether the additional elements, alone and in combination, amount to significantly more than the judicial exception.
Independent Claims 1 and 14
Additional elements under evaluation:
at least one computing device. The specification admits the device performs only generic functions see the description of computing device 1200: The computing device is a mobile computing device, such as a tablet, a cellular phone, a wearable device, a CGM controller device, or another computing device, for example (patent pub. Spec, par. 0166), and The processor 1202 performs signal coding, data processing, power control, image processing, input/output processing, or any other functionality that enables the computing device 1200 to perform as described herein (patent pub. Spec, par. 0166). The recited computing operations receiving, storing, comparing, calculating, transmitting are generic computer functions identified by MPEP § 2106.05(d)(II) as well-understood, routine, and conventional computer functions, including receiving or transmitting data over a network, performing repetitive calculations, and electronic recordkeeping. Nothing in Claims 1 or 14 improves the device.
blood glucose monitor. The specification admits the monitor is a pre-existing device see the description in blood glucose monitoring device 900: Examples of blood glucose monitoring devices include, but are not strictly limited to, continuous glucose monitoring devices, flash glucose monitoring devices, and blood glucose meters that provide a single measurement of blood glucose levels from a blood sample in a spot monitoring process (patent pub, spec, 0037). No improvement to sensing, sampling, or communication is claimed. The monitor performs its ordinary data-supply function a well-understood, routine, and conventional activity in the diabetes field that MPEP § 2106.05(g) identifies as insignificant extra-solution activity.
A generic computing device receiving data from a pre-existing blood glucose monitor and executing the claimed abstract sequence amounts to the instruction to apply the abstract idea using a standard mobile health setup. The combination adds no inventive concept.
Dependent Claims Analysis
The dependent claims fail to add an inventive concept.
Learning Models (Claims 2-3, 7-8, 11, 13, 15):
(MPEP § 2106.05(f) - Mere Instructions): These claims add specific mathematical models and calculations ( generic population-based blood glucose model, time-weighted... model, daily aggregate confidence level ). These limitations are mere instructions to apply an abstract idea (MPEP § 2106.05(f)) as they simply define the abstract logic. The specification describes these as the logic of the system, not a specific, tangible inventive component: The pre-defined blood glucose threshold is determined based on a generic population-based glucose model... (Spec., para. 0005); the learning model includes data from a generic population-based model. (Spec., para. 0077).
Rules and Triggers (Claims 4-6, 10):
(MPEP § 2106.05(f) - Mere Instructions): These claims add specific rules and triggers ( greater than a pre-defined... threshold, scheduled meals ). These are also mere instructions to apply an abstract idea that define the rules of the abstract mental process. The specification confirms this: The analysis of the one or more blood glucose levels includes determining that a blood glucose level is greater than a pre-defined blood glucose threshold (Spec., para. 0005).
WRC Hardware (Claim 9):
(MPEP § 2106.05(d) - WRC Hardware): This claim adds specific types of WRC hardware ( CGM or... SMBG meter ). This adds no inventive concept because it recites conventional, WRC data-gathering hardware. The claim is not improving the sensor itself, but merely using a known sensor as a data source. The specification explicitly names these as existing, standard devices: The blood glucose monitor is a continuous glucose monitor (CGM) or a spot-monitoring blood glucose (SMBG) meter (Spec., para. 0004, US 8,358,210 B2, Col. 1, ll. 20-30).
Post-Solution Activity (Claim 12):
(MPEP § 2106.05(g) - Insignificant Activity): This claim adds insignificant post-solution activity. This adds no inventive concept because 'identifying' is a mental conclusion, and 'adjusting an insulin pump' is a conventional, WRC post-solution activity in diabetes therapy. The claim does not recite how to adjust the pump in a new way, only that it should be adjusted based on the abstract analysis. The specification identifies insulin pumps as known, standard devices (Spec., para. 0047, US20150051583A1, par. 0083).
Claim 16 , the specification describes the imputation cap as a quality-control rule, not a tangible component: Imputation of data on each day of the testing period is limited, for example, to ensure quality of the testing period data. In examples, one imputation is allowed on each day of the testing period (par. 0064). In examples, two imputations are allowed on day two or later of the testing period. The cap adds no hardware, no data-structure improvement, no control-algorithm improvement only a rule limiting how many times the abstract estimation step may run. MPEP § 2106.05(f) mere instructions to apply the exception.
The independent and dependent claims together describe a standard mobile health setup executing the abstract analysis under narrower abstract rules and a rule cap. The combination amounts to no more than applying the abstract idea using standard components performing their ordinary functions.
The claims are directed to an abstract idea and lack an inventive concept. Therefore, Claims 1-16 are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Shima- US20220093234 in view of Patek- US20200205742.
Claim 1.
Shima teaches, A method comprising:
receiving, via at least one computing device, blood glucose data from a blood glucose monitor, the blood glucose data comprising a plurality of blood glucose levels of a user that are measured during a predefined testing period configured for collecting testing period data according to a testing protocol, wherein the testing protocol defines parameters of the testing period data for reliable capture to enable one or more of disease progression analysis or therapy analysis to be performed to treat a diabetic condition of the user at an end of the testing period; (Shima, monitoring a glucose level of a subject 102 … can include monitoring with a CGM (par. 0071); data for spiker events can be retrieved … for a predetermined amount of time (e.g., the current week and possibly a previous week) (par. 0076); January requires a certain level of data coverage at least 3 meal/food logs a day and at least 20 hours of heart rate and glucose data (par. 0158 / FIG. 9I-2); … can monitor and store a subject's health related activities and provide a graphical representation of the activities in relation to health responses (par.0093), fig.3B, fig.4A-4C, fig9G, fig.10B)
receiving, via the at least one computing device, the testing period data associated with the user during the predefined testing period the testing period data comprising:
dietary intake data associated with a plurality of meals consumed by the user during the testing period, medication data associated with a plurality of medication doses taken by the user during the testing period, and activity data associated with a plurality of pre-scheduled activities for the user during the testing period; (Shima, par. 0003, 0093, fig. 3A, fig. 5E, Fig 1B)
Shima describes a system that monitors and stores food events (dietary data), user activities (activity data), and medication(s) (medication data) in logs. This collection happens concurrently with health response monitoring, such as glucose levels. The concept of recommending activities implies an expected or planned activity, aligning with the pre-scheduled aspect of the limitation. The testing period is broadly covered by Shima's disclosure of monitoring events over a duration of time, such as the Default time period of 7 days mentioned in Figure 1B, and the general monitoring of blood glucose levels over a duration of time.
determining, via the at least one computing device, that a portion of the testing period data is missing based on an analysis of the received data; (Shima, par. 0127-0128, fig. 6A)
Shima teaches detecting a blood glucose spike and then checking if a food was logged in the corresponding time window. If no food was logged, the system has determined that food data is missing.
imputing, via the at least one computing device, the missing portion of the testing period data with substitute dietary intake data in response to determining that the missing portion of the testing period data comprises dietary intake data associated with a meal of the plurality of meals and determining that the user consumed the meal; (Shima, par. 0042, 0127-0128, 0130)Shima describes a system that detects a blood glucose (BG) spike, determines if a corresponding food was logged, and imputes missing data by detecting gaps (like unlogged meals) and then generating plausible entries essentially filling omissions to maintain complete datasets.imputing, via the at least one computing device, the missing portion of the testing period data with substitute medication data in response to determining that the missing portion of data comprises medication data associated with a medication dose of the plurality of medication doses and determining that the user took the medication dose; (Shima, par. 0093, 0156-0157,0042, 0127-0128, 0130)
Shima describes a system for monitoring various user activities, including medication doses, and is concerned with the completeness ( coverage ) of this data. The reference also provides a specific method for detecting and filling omissions in a parallel data stream the food log.
imputing, via the at least one computing device, the missing portion of the testing period data with substitute activity data in response to determining that the missing portion of data comprises activity data associated with a pre-scheduled activity of the plurality of pre-scheduled activities and determining that the user participated in the pre-scheduled activity; (Shima, par. 0011-0013, 0093, 0156-0157, 0127-0128, 0130, 0042)
Shima's discloses a system that monitors user activities and maintains activity logs. The system is also concerned with the completeness ( coverage ) of its data logs and explicitly teaches a method for detecting omissions in a... log and filling such omissions .
; andextending, via the at least one computing device, the testing period in response to one or more of: Shima, par. 0097, 0142, fig. 9G, 0108-0112)
Shima invention allows the system to lengthen or adjust the monitoring window essentially extending the testing period so that more data can be captured before final analysis.
determining that the user did not consume the meal based on an analysis of one or more of the plurality of glucose levels within a first time period associated with the meal, (Shima, par. 0011, 0127)
Shima describe determining if user meal or not by analyzing glucose level around a meal time, if the expected blood glucose response is absent or deviates significantly, the system infers the user did not actually consume the logged meal.
determining that the user did not take the medication dose based on an analysis of one or more of the plurality of glucose levels within a second time period associated with the medication dose, (Shima, par. 0093-0095)
Shima links medication dose events with glucose responses, so if the expected BG change is absent near the logged medication time, the system can infer the user did not actually take the dose.
determining that the user did not participate in the pre-scheduled activity based on an analysis of one or more of the plurality of glucose levels within a third time period associated with the pre-scheduled activity, or determining that the confidence level is below the pre-defined confidence threshold. (Shima, 0093-0096)
Shima, cross-references scheduled activities with glucose responses, so if the expected BG pattern is missing or the data quality is too low, the system can infer non-participation or flag it as below a confidence threshold
Obvious Rationale:
Shima's health monitoring system tracks and stores various data points from a user over a set period. It collects blood glucose levels from a continuous glucose monitor (CGM), as well as dietary intake (food events), activity (user activities), and medication doses. The system identifies missing data by analyzing the collected information, for example, by detecting a blood glucose spike and checking for a corresponding food log entry, or by calculating a data coverage score for different data types and imputing missing data. Refer to claim 1 above for further explanation
However, Shima does not disclose calculating a confidence level associated with the substitute dietary intake data, the substitute medication data, or the substitute activity data;
adding the substitute dietary intake data, the substitute medication data, or the substitute activity data to the testing period data when the confidence level is greater than a pre-defined threshold;
Patek teaches the missing elements, describing a system that validates data based on its reliability. Specifically, Patek discloses distinguishing between untrusted and trusted data, where data becomes trusted only when its confidence value exceeds a certain threshold (para. 0052).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of Shima with Patek because both references address the common problem of managing potentially unreliable health data to ensure a quality dataset for analysis. Shima is concerned with data completeness, employing a method for detecting omissions in a...log and filling such omissions (Shima, para. 0042), while Patek is concerned with data reliability, providing a method to validate data using a confidence value (Patek, para. 0052). A skilled artisan, when implementing Shima's data imputation feature, would recognize the uncertainty of such substitute data and would naturally seek a method to validate it before incorporating it into the final dataset used for disease progression analysis or therapy analysis. Patek provides just such a validation method.
A person of ordinary skill in the art would have been motivated to integrate the confidence level calculation and threshold check from Patek into the system of Shima to achieve the benefit of improved data integrity. Adding this validation step ensures that only reliable imputed data is used for subsequent analysis, thereby increasing the accuracy and trustworthiness of the therapy or disease progression insights generated by Shima's system. Patek teaches that this method is beneficial for distinguishing between untrusted and trusted data, a clear advantage when dealing with imputed information (para. 0052).
A PHOSITA would have had a reasonable expectation of success in combining the references. The modification required only ordinary skill in implementing a known data validation rule. The references provide sufficient enabling disclosure, and the techniques for calculating confidence scores and applying data thresholds were well-understood in the art of data management.
Shima in combination with Patek teaches, Claim 2.
The method of claim 1, wherein the testing period comprises a plurality of days, and wherein the substitute dietary intake data, the substitute activity data, and the substitute medication data are based on a generic population-based blood glucose model in response to the missing portion of the testing period data being associated with a first time period on a first day of the plurality of days.
Shima discloses monitoring a subject over a day or week period (par. [0076]) and specifically identifying periods where blood glucose data are missing (e.g., pars. [0109], [0156]; Fig. 5B). The specification further teaches generating a response using models based on a cohort response (par. [0084]) or population data derived from a matching system (pars. [0145]- [0146]; Fig. 9A). Therefore, applying this disclosed population-based (cohort) model to generate a response (i.e., substitute... data ) for a missing portion of data is a direct and fully supported application of Shima's teachings.
Shima in combination with Patek teaches Claim 3.
The method of claim 2, wherein the substitute dietary intake data, the substitute activity data, and the substitute medication data are based on a time-weighted blood glucose model developed using blood glucose data of the user received on the first day of the plurality of days in response to the missing portion of the testing period data being associated with a second time period on a second or third day of the plurality of days.
Shima discloses monitoring a subject over a plurality of days, such as a week (par. [0140]), and explicitly identifies missing portion of data on specific days (pars. [0108], [0161]; Fig. 5B). The specification teaches generating a response or possible food substitute based on data from a subject (e.g., previous meals eaten, par. [0128]) and time stamped event data (par. [0071]), thereby establishing a personal, time-sensitive model (i.e., a time-weighted blood glucose model based on first day data).
Shima in combination with Patek teaches Claim 4.
The method of claim 1, wherein the analysis of the received testing period data comprises:
an analysis of one or more blood glucose levels of the plurality of glucose levels;
Shima discloses performing an analysis of the blood glucose levels. The system compares glucose levels to predetermined limits (para. 0072) to determine if there has been a spike. The system also determines if a subject's glucose level exceeds a limit (para. 0085), generates glucose TIR data (para. 0109), and uses CGM data as a basis for a glucose metabolization evaluation (para. 0147). These evaluation and comparison steps clearly constitute an analysis of... blood glucose levels.
and an analysis of the dietary intake data, the medication data, and the activity data. (Shima, par. 0073, 0093, 0147, 0148, 0157, 0162)
Shima, uses food logs for glucose evaluation, HR/activity data to calculate activity values, and medication logs to compute a data coverage score, all of which constitute various forms of system analysis.
Shima in combination with Patek teaches Claim 5.
The method of claim 4, wherein the analysis of the one or more blood glucose levels comprises determining that a blood glucose level is greater than a pre-defined blood glucose threshold. (Shima, par. 0072, 0085, 0119)
Shima's analysis of blood glucose levels explicitly includes this step as the primary method for detecting a spike. Shima teaches comparing glucose levels to predetermined limits and determining if a subject's glucose level exceeds a limit. This action of monitoring BG data... for levels that exceed a limit is the same as the claimed analysis of determining that a blood glucose level is greater than a pre-defined blood glucose threshold.
Shima in combination with Patek teaches Claim 6.
The method of claim 5, wherein the pre-defined blood glucose threshold is determined based on an expected blood glucose level associated with one or more of a carbohydrate content of the meal, a glycemic profile of the meal, a dynamic fingerprint associated with a medicine of the medication dose, a type of the pre-scheduled activity, a length of the pre-scheduled activity, or a pre-determined blood glucose level increase associated with the user. (Shima, par. 0072, 0084, 0107, 0118, and 0172)
Shima teaches that the blood glucose detection threshold is customized based on specific user inputs, as its system analyzes food content (glycemic index/carbohydrates) and adjusts the BG range dynamically in response to medication and the type/length of activity. This personalization is further supported by Shima's reliance on the user's previous responses to determine the appropriate monitoring limits for detecting an undesirable spike or deviation.
Shima in combination with Patek teaches Claim 7.
The method of claim 1, wherein the testing period comprises a plurality of days, and wherein the pre-defined blood glucose threshold is determined based on a generic population-based blood glucose model in response to the meal, pre-scheduled activity, or medication dose being on a first day of the plurality of days. (Shima, par. 0076, 0084, 0107-0109, 0118, 0145, 0162)
Shima monitors for a spike from the start of operation, requiring an active threshold. Shima's system uses either generic cohort responses or the subject's personalized previous responses for this threshold. When insufficient data exists on the first day for example, the personalized model is unavailable, so the system defaults to generic from cohort responses model.
Shima in combination with Patek teaches Claim 8.
The method of claim 7, wherein the pre-defined blood glucose threshold is determined based on a time-weighted blood glucose model in response to the meal, pre-scheduled activity, or medication dose being on a second or third day of the plurality of days, the time-weighted blood glucose model developed using blood glucose data of the user received on the first day of the plurality of days. (Shima, par. Shima, par. 0073, 0076, 0084, 0101, 0109, 0112, 0162)
Shima teaches this entire phased-model approach. Shima monitors for a plurality of days, including a second or third day, by analyzing data over a week and comparing a previous day to other days (para. 0076, 0101, 0109). Shima teaches a model developed using... data... received on the first day by disclosing models based on previous responses of the subject (para. 0084) and, more specifically, by generating a baseline value from initial data (para. 0162). Finally, Shima teaches the time-weighted... model because its entire analysis is based on evaluating glucose data within specific predetermined time windows (para. 0073, 0112) relative to food intake (para. 0078), which is a form of time-weighting.
Shima in combination with Patek teaches Claim9.
The method of claim 1, wherein the blood glucose monitor is a continuous glucose monitor (CGM) or a spot-monitoring blood glucose (SMBG) meter. (Shima, par. 0009, 0071, 0188.)
Shima in combination with Patek teaches Claim 10.
The method of claim 1, wherein determining that the missing portion of the testing period data comprises dietary intake data comprises analysis of one or more determining that the missing portion of data comprises medication data comprises analysis of one or more
Shima teaches monitoring glucose, food, medication, and activity data (Shima, para. 0093) and determining if data is missing based on detecting a glucose spike without a corresponding food log (Shima, para. 0073) or by calculating a general data coverage score (Shima, para. 0157).
However, Shima fails to disclose determining that the missing portion of data comprises dietary intake data based on analysis of one or more scheduled meals, determining that the missing portion comprises medication data based on analysis of one or more scheduled medication doses, or determining that the missing portion comprises activity data based on analysis of one or more pre-scheduled activities.
Patek teaches the missing element, describing a system that performs historical and replay meal bolus reconciliation to identify mis-timed boluses and missing meal boluses (Patek, para. 0038). This reconciliation involves comparing historical logs against expected events, such as calculating a Pre-meal Bolus Compliance score (Patek, para. 0126, Fig. 12) by analyzing historical data against an ideal pre-meal bolus timing (Patek, para. 0099), which constitutes the claimed analysis against a schedule.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Shima with Patek because both references address the analysis of CGM, insulin, meal, and activity data for diabetes management (Shima, Abstract; Patek, Abstract) and seek to improve insights based on this data. Shima recognizes the problem that incomplete data degrades the quality of system outputs (January requires a certain level of data coverage..., Shima, Fig. 9I-2), while Patek provides a known technique in the field for precisely assessing data completeness through reconciliation (Patek, para. 0038) and compliance analysis against expected timings (Patek, para. 0099). A POSITA would combine Shima's general missing data detection with Patek's specific schedule-based compliance analysis logic to achieve a more accurate determination of which specific data points are missing, thereby improving the quality of Shima's insights and recommendations (Shima, Fig. 9I-2).
A person of ordinary skill in the art would have been motivated to integrate the compliance analysis from Patek into the system of Shima to achieve the benefit of improved accuracy in identifying specific missing data events, leading to better inferences about diabetes meal management (Patek, para. 0003). Patek teaches that its analysis identifies specific opportunities for improvement (Patek, para. 0038).
Shima in combination with Patek teaches Claim 11.
The method of claim 1, wherein the confidence levels for each instance of substitute data imputation on a day of the testing period are aggregated to determine a daily aggregate confidence level, and wherein the testing period is extended in response to the .
Shima teaches monitoring health data (para 0002) over a time period (para 0108), assessing data coverage (para 0157), identifying missing data (Fig. 6A), and suggesting entries (derive a possible food, para 0128). Shima continues data collection until the period ends (para 0157). However, Shima fails to teach calculating confidence levels for its suggested substitute data, aggregating these non-existent levels into a daily aggregate confidence level, or extending the testing period specifically in response to this daily confidence level being below a threshold.
Patek teaches the missing elements: performing data estimation (imputation) (para 0063), assessing data quality using a confidence value exceeding a predetermined threshold (para 0052), aggregating data into daily scores (daily average risk score, para 0143), and analyzing data over extended periods (para 0162), all to ensure reliable and interpretable solutions (para 0074).
It would have been obvious to a POSITA to combine Shima and Patek. Both aim to analyze health data reliably despite gaps (Shima, para 0108; Patek, para 0039). Shima identifies missing data but lacks a quantitative quality check for its suggestions. Patek provides this check via confidence values (para 0052) aggregated daily (daily average risk score, para 0143). A POSITA would integrate Patek's quantitative confidence assessment into Shima to objectively validate the daily dataset before analysis. When Patek's assessment indicates low daily quality (below the... threshold, para 0052), directly addressing Shima's INSUFFICIENT DATA concern (para 0108), the logical action within Shima's existing multi-day collection framework (monitoring... for a... week, para 0076) is to continue collecting more actual data by extending the testing period until Patek's quality standard is met. This ensures the benefit taught by Patek: achieving reliable and interpretable solutions (para 0074).
This combination represents the Use of known technique to improve similar devices... in the same way. Applying Patek's known technique of quantitative data validation (confidence scores, aggregation, thresholds - paras 0052, 0143) to Shima's system, which lacks it (para 0108), predictably improves data reliability. Extending the testing period based on this quantitative check is the predictable result, as collecting more data is the standard solution for insufficient data quality in monitoring systems. A POSITA would have had a reasonable expectation of success.
Shima in combination with Patek teaches Claim 12.
The method of claim 1, further comprising:
identifying, using the substitute dietary intake data, the substitute medication data, or the substitute activity data, a progression or a regression in a diabetic condition associated with the user; (Shima, paragraphs 0042, 0128, 0130, 0162, 0165)
Shima describes an analytical system that tracks a lifestyle score over time (current week versus previous week, fig. 10B) as a measure of user health status (progression or regression in a diabetic condition). Since Shima's methodology includes proactively identifying missing data in a log (detecting omissions... and filling such omissions, para 0042) by deriving a possible food (substitute dietary intake data, para 0128) and integrating this derived data into the continuous food log data used by the analysis section 1002 (para 0162), the system performs its trend analysis (identifying... progression or a regression) using the substitute dietary intake data.
.
Shima teaches the necessary trend analysis to determine a progression or the regression in the diabetic condition associated with the user by tracking a continuous lifestyle score (Shima, paras 0162, 0165). However, Shima fails to disclose adjusting a configuration of an insulin pump to increase or reduce the supply of insulin to the user;
Patek teaches the core missing element, describing a system that comprises a Processor 130 communicating with an insulin device 110 (Patek, para 0043), which is an external pump (Patek, para 0044). Patek's system analyzes data to determine therapy suggestions that include adjusting the amount or timing of the insulin bolus (Patek, para 0161).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Shima with Patek because both references are directed to optimizing diabetes management using continuous monitoring data (Shima, Abstract; Patek, Abstract). Shima provides the detailed, long-term trend analysis, and a POSITA would look to Patek to automate the therapeutic delivery of Shima's recommendations by leveraging Patek's existing hardware interface with the insulin device 110 / pump (Patek, paras 0043-0044).
A person of ordinary skill in the art would have been motivated to integrate the insulin pump control from Patek into the system of Shima to achieve the benefit of automated and optimized insulin delivery, thereby improving patient outcomes. Patek teaches that this method is intended to achieve the reduction of risk, stating its analysis leads to therapy suggestions (Patek, para 0103) that can substantially reduce exposure to hyperglycemia (Patek, para 0109).
Claim 13.
Shima in combination with Patek teaches, The method of claim 1, further comprising updating a learning model associated with the user using the testing period data. (Shima, paragraphs 0018, 0019, 0084, 0162, 0163)
Shima describes a system that constantly refines its predictive functions based on new, incoming data specific to the subject. The system utilizes at least one metabolism model for the subject (a learning model associated with the user) which is continually fed CGM, HR and food log data (the testing period data) and stores the blood glucose responses of the subject corresponding to prior actions to generate the next predicted subject blood glucose response (updating).
Claim 16.
Shima in combination with Patek teaches, The method of claim 1, wherein the testing protocol limits a number of imputations during the testing period for the reliable capture of the testing data to enable the one or more of disease progression analysis or therapy analysis to be performed at the end of the testing period without starting another testing period. (Shima, In order to provide you with high quality insights and personalized recommendations, January requires a certain level of data coverage at least 3 meal / food logs a day and at least 20 hours of heart rate and glucose data..., FIG. 9I-2).
Shima's minimum data coverage rule limits the number of imputations during the testing period. Because a monitoring period (such as a 24-hour day) is a finite unit of time, Shima’s requirement of a minimum threshold of actual data (e.g., at least 20 hours) and mathematically establishes a maximum limit on imputed or missing data (e.g., no more than 4 hours). By restricting the allowable unlogged data to ensure high quality insights Shima functionally executes the exact limitation claimed.
Note: Claim 14 is rejected as being similar to claim 1, and claim 15 is rejected as being similar to claims 2 and 3.
Relevant Prior Arts:
US 20140365534 A1 Bousamra:
Abstract
Methods for performing a structured collection procedure by utilizing a collection device … for one or more data event instances occurring according to a schedule of events. …data collection pertaining to a biomarker to be performed according to one or more conditions of an adherence criterion. Each data event instance is determined to be successful or unsuccessful on the basis of actual performance of the data collection and meeting certain conditions of the predetermined adherence criteria for the data event instance. Contextual information for successful data collections is generated and a data file generated for storing records relating to successful data collections. For unsuccessful data event instances, substitute data relating to data collections performed separately from the collection procedure are included in the data file records for the collection procedure if the substitute data is determined to meet conditions of the predetermined adherence criterion for the corresponding data event instance. [0110]
In another example and in one embodiment, a [blood glucose] bG measurement must be collected before each meal in order for a structured collection procedure 70 to provide data that is useful in addressing the medical use case or question for which it was designed, such as identified by the use case parameter 220. If, in this example, the patient fails to take a bG measurement for the lunch meal in response to a request 240 for such a collection according to the schedule of the event 222, and hence the adherence criteria 224 for that event 237 fails to be satisfied, the processor 102 in response to the associated adherence event 242 can be programmed according to instructions in the collection procedure 70 to cancel all remaining events 237 in the schedule of events 222 for that day, mark the morning bG measurement stored in the data file (such as data file 145 (FIG. 4) as invalid, and reschedule for the schedule of event 222 for the next day. Other examples of further actions in which the processor 102 may take in response to an adherence event 242 may be to dynamically change the structure testing procedure by switch to a secondary schedule of event, which may be easier for the patient to perform, provide additional events for measurements to make up the missing data, change the exit criteria from a primary to a secondary exit criterion providing modified criterion(s), change the adherence criteria from a primary to a secondary adherence criterion, fill in the missing data for the failing event with historical data or an estimate based on the historical data, perform a particular calculation to see if the structured collection procedure 70 can still be successfully performed, send a message to a particular person, such as a clinician, of the failing event, provide a certain indication in the associated data record 152 to either ignore or estimate the missing data point, and the likes. In still another embodiments, the adherence criteria 224 can be dynamically assessed, such as for example, based on one or more biomarker values and/or input received from the user interface in response to one or more questions, via an algorithm which determines whether the collected data provides a value which is useful in addressing the medical use case or case. In this example, if the calculated adherence value is not useful, for example, does not fall into a desired range or meet a certain pre-define value, then further processing as defined by the resulting adherence event would then take place, such as any one or more of the processes discussed above. Also refer to claims 5-6, 12, and 16.
US 9659037 B2 – Soni
… provide additional events for measurements to make up the missing data, change the exit criteria from a primary to a secondary exit criterion providing modified criterion(s), change the adherence criteria from a primary to a secondary adherence criterion, fill in the missing data for the failing event with (an estimate from) historical data, perform a particular calculation to see if the structured collection procedure 70 can still be successfully performed, send a message to a particular person, such as a clinician, of the failing event, provide a certain indication in the associated data record 152 to either ignore or estimate the missing data point, and the likes. In still another embodiments, the adherence criteria 224 can be dynamically assessed, such as for example, based on one or more biomarker values and/or input received from the user interface in response to one or more questions, via an algorithm which determines whether the collected data provides a value which is useful in addressing the medical use case or case. In this example, if the calculated adherence value is not useful, for example, does not fall into a desired range or meet a certain pre-define value, then further processing as defined by the resulting adherence event would then take place, such as any one or more of the processes discussed above…. (fig. 5A,Col.25, ll. 1-34 )
US 20200359913 A1 - GHODRATI
[0058] The confidence interval for each estimated event can then be calculated. Specifically, using the probabilistic model, the expected value and the confidence interval of behavioral parameters (e.g., sleep duration or amount of activity) can be estimated from the probabilistic model for the missing intervals. An estimated value for the total duration of the sleep or amount of activity within the 24 hour period can then be calculated from the known information and the probabilistic model and a confidence interval is generated for each estimation. See also claim 3.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JOSHUA DAMIAN RUIZ/Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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