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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zamanakos et al. (US 20160098848 A1- Previously cited), hereinafter Zamanakos, further in view of Frank et al. (US 20210401330), hereinafter Frank.
Regarding claims 1, 9 and 17, Zamanakos teaches a device, method, and non-transitory computer method, implemented in a continuous glucose level monitoring (CGM), the device (¶ [0051]), method and system comprising:
a sensor configured to continuous sense glucose levels of a user to generate diabetes management measurements of the user (¶ [0017,0051], sensor data is used to provide recommendations to user);
a transmitter configured to transmit the diabetes management of the user to a device (¶ [0005-6,0011-14,0126,0150-152], a transmitter is required to ); and the device in communication with the continuous glucose monitoring device, the device comprising:
a display device (¶ [0150]);
a memory storing: executable instructions (¶ [0134,0328]); and
a library of diabetes management feedbacks (¶ [0174], patterns are stored and retrieved for detection and identification);
and a processor in data communication with the memory and configured to execute instructions to (¶ [0328]);
receive a data stream including data associated with the user (¶ [0051]);
determining multiple time periods for the user based on the data included in the data stream; obtain, from the continuous monitoring device, (¶ [0051,0148,0318], invasive CGM comprising a processor to perform the steps), first glucose measurements measured for the user for a first time period of the multiple time periods of a current day (¶ [0061,0205], multiple periods, e.g., overnight, after breakfast, first period, second period etc., of glucose measurements can be obtained for a current day), the glucose sensor being inserted at an insertion site of the user;
generate, from the first glucose measurements, one or more features for the first time period of the current day (¶ [0022,0035,0178], events/features of identified patterns/features can be generated for each time period, that is both generated events and patterns can be broadly interpreted as features);
analyze the one or more features for the first time period of the current day to determine at least one feature that satisfies one or more rules for the first time period of the current day (¶ [0022,0033,0061], indicators of patterns can be compared, events can be compared to event criteria, analyte levels can be compared between time periods);
generate a time period score based on the one or more features generated for the first time period of the current day and corresponding one or more features for the first time period across a prior day (see figs. 5-6 and 27A-C and ¶ [0269,0289,0314], fig. 5 states, “Weekend Highs” and “Blood glucose usually goes over 180 mg/dL on Saturdays and Sundays.” Fig. 6 states under “Weekend Highs” that the blood glucose readings were above 200 mg/dl at 66% of the weekend days.” When viewed as a whole, if the patient were to check their physiological information on a weekend day and the stated information above, we have a time period score (66%) that is based on the current day (Saturday or Sunday) and at least one prior day (the previous Saturday(s) and/or Sunday(s)). A difference is determined by analyzing when the difference between 200 mg/dL is determined above or below 0, therefore high and further impacts the time period score e.g. 66%. Moreover, ¶ [0269], “The time periods shown can be “sliding windows” of time, and can thus be adjusted in duration and starting point (equivalently, ending point). While the compared time periods are arbitrary, the user can select the same so as to illuminate the effects of various modifications or events. For example, the user may modify the time periods to illuminate the effects of a medical intervention, attempted lifestyle changes, or the like. For example, the user may find it informative to compare the week/month before a doctor's appointment and the week/month after, or the user may compare a workweek versus a weekend, a week before and after a holiday, or successive weekends to gauge improvement, and so on. While in some cases the time periods compared are equal in duration, in other cases the durations will vary” indicating that comparison is to aid in determining effects of interventions);
identify which at least one diabetes management feedback of multiple diabetes management feedbacks in the library of diabetes management feedbacks corresponds to the satisfied one or more rules (¶ [0205] and figs. 5-6 and 27A-C, multiple feedbacks with respective outputs, e.g., overnight lows, weekend highs, etc., are output based on the features extracted, e.g. glucose values, glucose in range, time period, etc.);
generating a user interface including the identified at least one diabetes management feedback and an indication of the first time period; and causing the identified at least one diabetes management feedback to be displayed (see fig. 5, time period 259 and feedback (258) are displayed in generated UI).
Zamanakos fail to teach the time period score includes: an effect size that is a difference between the first time period of the current day and the first time period across the prior day, and a significance value that is a number of standard deviation away from a feature mean calculate based on the one or more features for the first time period and the corresponding one or more features for the first time period across the prior day.
Frank teaches a diabetes prediction using glucose measurements (abstract). The glucose features are extracted from the measurements and used to calculate rate of change measures, time over threshold measures, and observation anomalies (¶[0096]). The glucose features include mean glucose over days, median glucose, day to day variability, and standard deviation of glucose (¶[0036,0096,0100]). The processing manager is configured to statistically analyze the glucose features, e.g. standard deviation of glucose, variance, etc., based on the mean and median of those features at particular times over time, i.e. first time period and prior day, (¶[0101-05]). Additionally, an effect size that is a difference between the first time period of the current day and the first time period across the prior days is extracted from the glucose features (¶[0096], “mean of daily difference (MODD)”).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Zamanako, such that the time period score includes an effect size that is a difference between the first time period of the current day and the first time period across the prior day, and a significance value that is a number of standard deviation away from a feature mean calculate based on the one or more features for the first time period, and the corresponding one or more features for the first time period across the prior day, as taught by Frank, to aid in describing the state of the user or whether the user is predicted to experience adverse effects of diabetes (¶[0009] of Frank).
Regarding claims 2, 10, and 18, Zamanako teaches wherein each of the multiple time periods of the current day comprises a different multi-hour period of time during the current day or 24 hour period (¶ [0205], “patterns shown include overnight lows, lows after breakfast, weekend highs, highs after dinner”).
Regarding claims 3, 11, and 19, Zamanako teaches wherein the multiple time periods of the current day include an overnight time period, a breakfast time period, a lunch time period, and a dinner time period (¶ [0187,0205], “patterns shown include overnight lows, lows after breakfast, weekend highs, highs after dinner”).
Regarding claims 4 and 12, Zamanako teaches comparing the first glucose measurements to glucose measurements measured for the user during one or more additional time periods of the current day to determine whether the first glucose measurements were within a particular range or were below a particular glucose level for a longer duration of time than the glucose measurements measured for the user during the one or more additional time periods (¶ [0183-186,0215], ranges and thresholds can be established for comparison with glucose levels, this information can be compared with information related to other time periods), and
determining that the first glucose measurements satisfy the one or more rules in response to determining that the first glucose measurements were within the particular range or were below the particular glucose level for a longer duration of time than the glucose measurements measured for the user during the one or more additional time periods (¶ [0205,0215], determinations based on the comparison are made, e.g., overnight lows, etc.), and
Zamanako-Frank fail to explicitly teach wherein the identified at least one diabetes management feedback includes an indication that glucose levels for the user during the current day were best during the first time period. However, Zamanako teaches that feedback can be provided based on selected time period (¶ [0205]) and that best day time periods can also be determined (see fig. 5, the best day row includes that the subject was in target range 72% indicating that during specific time periods amounting to 72% are considered to indicate “better” glucose levels than other periods). As such, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Zamanako-Frank, such that the diabetes management feedback includes an indication that glucose levels for the user during the current day were best during the first time period, as taught by Zamanako, to aid in providing positive reinforcement by noting a pattern in which the patient’s disease management was under particularly good control (¶ [0205]).
Regarding claims 5 and 13, Zamanako teaches comparing the first glucose measurements to glucose measurements measured for the user during the time period of one or more previous days to determine whether the first glucose measurements were within a particular range or were below a particular glucose
level for at least a threshold longer duration of time than the glucose measurements measured for the user during the time period of the one or more previous days (¶ [0183-186,0215], ranges and thresholds can be established for comparison with glucose levels, this information can be compared with information related to other time periods including comparing information over a multiple days), and
determining that the first glucose measurements satisfy the one or more rules in response to determining that the first glucose measurements were within the particular range or were below a particular glucose level for a longer duration of time than the glucose measurements measured for the user during the time period of the one or more previous days (¶ [0205,0215], determinations based on the comparison is made, e.g., overnight lows, etc. over multiple days), and
and the identified at least one diabetes management feedback includes an indication that glucose levels for the user for the time period of the current day were better than glucose levels for the user for the time period during the one or more previous day (see fig. 5, the best day row includes that the subject was in target range 72% indicating that during specific time periods amounting to 72% indicate “better” glucose levels than other periods).
Regarding claims 6 and 14, Zamanako teaches wherein the analyzing includes:
determining whether the first glucose measurements as well as glucose measurements measured for the user during the time period of one or more previous days were within a particular range or were below a particular glucose level for each of the current day and the one or more previous days (¶ [0205], determinations of events over multiple days can be established), and
determining that the first glucose measurements satisfy the one or more rules in response to determining that the first glucose measurements as well as glucose measurements measured for the user during the time period of one or more previous days were within the particular range or were below a particular glucose level for each of the current day and the one or more previous days (¶ [0205,0215] and fig. 5, determinations based on the comparison are made, e.g., overnight lows, etc. for current and previous days), and
determining a total number of day by counting each of the current day and the one or more previous days (see fig. 5, total days of the time period selected are counted and displayed, “over this 14 day period”), and the identified at least one diabetes management feedback includes an indication that glucose levels for the user for the time period have been within the particular range for the total number of days (¶ [0215] and figs. 5 and 22, the feedback UI includes a summary section that includes how long the user has been in/out of the target range during the time period selected).
Regarding claims 7, 15 and 20, Zamanako teaches wherein the obtaining includes first activity data from an activity tracker worn by the user, and the generating includes generating the one or more features for the first time period from both the first glucose measurements and the first activity data (¶ [0219], “The receiving available data 320 may further include receiving data about patient activity (step 326), e.g., from an accelerometer or GPS. Certain types of patient activity data may be gleaned from other sources as well, including time of day, e.g., indicating waking activity versus sleeping. The receiving data may further include receiving a glucose value from a sensor (step 328), e.g., CGM data, SMBG data, or the like. Such receiving data may further include calculating or deriving data from the received data, e.g., slopes, accelerations, or the like. Such derived data can then be employed in certain fields and visualizations” indicating that an activity tracker is worn by the user to obtain the activity data in conjunction with the glucose measurements).
Regarding claims 8 and 16, Zamanako teaches wherein the identifying includes:
identifying two or more to rules satisfied by the first glucose measurements (¶ [0215-216], the summary section comprises rules met by the glucose information received, e.g., target in range, duration in/out range, etc.), the method further including determining priorities for each of the two or more rules, and
identifying, as the at least one diabetes management feedback, the diabetes management feedback corresponding to one of the two or more rules having a highest priority (¶ [0216], “It is again noted that the above types of data are those measured within the selected timeframe, and that in each case the above noted data types may be provided by dynamic generation of the report… dictated by the prioritization or ranking scheme”).
Response to Arguments
Applicant's arguments filed 03/11/2026 have been fully considered but they are not fully persuasive.
Applicant’s arguments with respect to amended claims have been considered but are moot because amendments require new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhong teaches a mean absolute difference, standard deviation, or other statistical measurement may be calculated by comparing a set of output values from a glucose prediction model corresponding to a prediction horizon after a point of time (e.g., the four hours of predicted glucose values following the 8 AM reference point) to the corresponding historical glucose measurement values (e.g., the four hours of historical patient sensor glucose measurement values following the 8 AM reference point on a Wednesday). US 20180277246
Myoujou et al. teaches an average graph and a standard deviation line graph within the same range as that of the average graph 111 and the blood glucose level information table 112 within an immediately preceding period to be displayed on the lower side of the graph display region. US 20110196217
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARTIN NATHAN ORTEGA whose telephone number is (571)270-7801. The examiner can normally be reached M-F 7:10 am - 5:00 pm.
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/MARTIN NATHAN ORTEGA/Examiner, Art Unit 3791 /TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
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