DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 26, 2026, has been entered.
Response to Amendment
Claims 6 and 13 have been amended. Claims 1-5 and 18 have been canceled. Claims 6-17 and 19-25 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant's arguments filed May 26, 2026, have been fully considered but they are not persuasive. A response is provided below in bold where appropriate.
Applicant argues 35 USC §112, pg. 9 of Remarks:
Claim Rejections - 35 U.S.C. § 112
112(a)
Claims 6-17 and 19-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. § 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. Applicant respectfully traverses.
Claims 6 and 13 have been amended. Applicant respectfully submits that the amendments to claims 6 and 13 overcome the present rejection under 35 U.S.C. § 112(a). Applicant respectfully requests withdrawal of the rejection of claims 6-17 and 19-25 under 35 U.S.C. § 112(a).
Withdrawn based on the claim amendments. However, Applicant’s amendments have caused a new rejection.
112(b)
Claims 6-17 and 19-25 are 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. Applicant respectfully traverses.
Claims 6 and 13 have been amended. Applicant respectfully submits that the amendments to claims 6 and 13 overcome the present rejection under 35 U.S.C. § 112(b). Applicant respectfully requests withdrawal of the rejection of claims 6-17 and 19-25 under 35 U.S.C. § 112(b).
Withdrawn based on the claim amendments. However, Applicant’s amendments have caused a new rejection.
Applicant argues 35 USC §103, starting pg. 10 of Remarks:
Claim Rejections - 35 U.S.C. § 103
Claims 6-9, 11-16, 19-21, and 23-25 are rejected under 35 U.S.C. § 103 as being unpatentable over US 2018/0197628 to Wei et al. ("Wei") in view of Pub. No. US 2011/0098548 to Budiman et al. ("Budiman") in view of Pub. No. US 2007/0112298 to Mueller, JR et al. ("Mueller") and in further view of Pub. No. US 2013/0277233 to Blythe ("Blythe"). Applicant respectfully traverses.
Claim 6 has been amended to recite "after receipt of a predetermined number of meal indications at a mealtime associated with the time of receipt, generate an average meal insulin response based on the delivered meal boluses and blood glucose data for the mealtime associated with the time of receipt." Wei, Budiman, Mueller, and Blythe, alone or in combination, do not disclose, teach, or suggest this limitation.
From Applicant’s amendment above…
>>”Claim 6 has been amended to recite "after receipt of a predetermined number of meal indications at a mealtime associated with the time of receipt, generate an average meal insulin response based on the delivered meal boluses and blood glucose data for the mealtime associated with the time of receipt."”<<
From Applicant’s specification:
“After receipt of a predetermined number of meal indications, the algorithm or AP application may generate an average meal insulin response, wherein the predetermined number of meal indications may be nine or the like and the average meal insulin response is an estimated average meal bolus for delivery after each meal (270).” [0023]
Therefore, after receipt of 9 meal indications, the average meal insulin response is an average meal bolus after each meal.
From Applicant’s amendment above…
>>”Claim 6 has been amended to recite "after receipt of a predetermined number of meal indications at a mealtime associated with the time of receipt, generate an average meal insulin response based on the delivered meal boluses and blood glucose data for the mealtime associated with the time of receipt."”<<
Applicant adds at a mealtime associated with the time of receipt, where Claim 6 earlier teaches “obtain a time of receipt of the input to the meal button related to ingestion of the meal;…”
Therefore, mealtime is associated with time of receipt of input to a meal button, the predetermined number of meal indications are used.
Respectfully, it is unclear where this is taught. The specification does not teach a mealtime associated with a time of receipt to generate an average insulin response.
From Applicant’s amendment above…
>>”Claim 6 has been amended to recite "after receipt of a predetermined number of meal indications at a mealtime associated with the time of receipt, generate an average meal insulin response based on the delivered meal boluses and blood glucose data for the mealtime associated with the time of receipt."”<<
From Applicant’s specification cited above, the average meal insulin response is an estimate of the average meal bolus for delivery after each meal. It is not based on blood glucose data associated with the time of receipt (time of input to meal button).
Respectfully, it is unclear where this is taught. Further, the specification teaches “… and the average meal insulin response is an estimated average meal bolus for delivery after each meal (270).” [0023]
The above does not teach the average meal insulin response is based on delivered meal boluses and blood glucose data at the time of receipt.
I. Generating an Average Meal Insulin Response
The Office cites Wei as allegedly disclosing generating an average meal insulin response, characterizing Wei's "glycemic central tendency (e.g., an average or median glycemic response)" for the same or similar meal as teaching this limitation. However, Wei does not disclose, teach, or suggest generating an average meal insulin response for a mealtime associated with a time of receipt. Wei's glycemic central tendency is computed for a specific meal or menu item, e.g., a particular food the user has consumed on repeated occasions, not for a mealtime associated with a time of receipt. For instance, Wei describes the average glycemic response to a given meal may be statistically calculated over the number of times the meal was consumed. (Wei [0098]). Thus Wei's averaging is organized around specific, individually identified menu items, not around mealtimes associated with a time of receipt of a meal indication.
Respectfully, from Applicant’s specification, mealtime associated with time of receipt is not taught, only the after meal bolus.
Moreover, Wei's average glycemic response is derived solely from observed glucose data. Wei does not disclose generating an average meal insulin response based on both delivered meal boluses and blood glucose data. Wei's system empirically observes glucose responses to a given meal and may use those observations to estimate an insulin bolus, but it does not combine delivered meal bolus data with blood glucose data to generate an average meal insulin response. (Wei at [0187], [0239]). Accordingly, Wei fails to disclose this limitation. Budiman fails to cure the deficiency of Wei.
Applicant’ claim and specification regarding predetermined days and average are about an insulin response, not about somehow combining bolus (insulin) and blood glucose data. That would not be just an “insulin response” but an “insulin response” and a “glucose response.”
Budiman is cited by the Office for teaching that insulin therapy is specified by a carbohydrate ratio and a total daily dose of insulin. (Budiman at [0141]). However, Budiman does not disclose, teach, or suggest generating an average meal insulin response based on delivered meal boluses and blood glucose data for a mealtime associated with the time of receipt. Budiman's disclosure is directed to specifying insulin therapy parameters, not to accumulating meal indications at a mealtime and generating an average meal insulin response therefrom. Mueller fails to cure the deficiencies of Wei and Budiman.
Based on the claim amendments, Budiman is withdrawn.
Mueller is cited by the Office for teaching suspension of basal insulin delivery to prevent hypoglycemia following a meal bolus. (Mueller at [0048]). However, Mueller does not disclose, teach, or suggest generating an average meal insulin response based on delivered meal boluses and blood glucose data for a mealtime associated with the time of receipt. Mueller's disclosure is directed to time-shifting basal insulin to redistribute already-determined insulin amounts around a meal event, not to accumulating meal indications at a mealtime and generating an average meal insulin response therefrom. Blythe fails to cure the deficiencies of Wei, Budiman, and Mueller.
Blythe is cited by the Office for teaching a predetermined number of meals. (Blythe at [0095]). However, Blythe does not disclose, teach, or suggest generating an average meal insulin response based on delivered meal boluses and blood glucose data for a mealtime associated with the time of receipt. Blythe's disclosure is directed to a statistical validation methodology for glucose threshold alerting, not to generating an average meal insulin response for a mealtime. Accordingly, none of Wei, Budiman, Mueller, and Blythe, alone or in combination, disclose, teach, or suggest this limitation.
II. Predetermined Number of Meal Indications
Further, the Office admits that Wei, Budiman, and Mueller do not teach a
predetermined number of meal indications and looks to Blythe to cure this deficiency. (Office Action at page 25). However, Blythe does not disclose, teach, or suggest a predetermined number of meal indications at a mealtime associated with a time of receipt. Blythe's disclosure is directed to a statistical validation methodology for glucose threshold alerting, in which a calculated sample size "m" of pre- and post-prandial glucose concentration measurement pairs is used to determine whether a glucose differential threshold has been exceeded with statistical significance. (Blythe at [0008], [0095]). Blythe's number is a sample size parameter for controlling statistical power in glucose alert determinations and bears no relation to accumulating a predetermined number of meal indications at a particular mealtime for the purpose of generating an average meal insulin response.
Respectfully, Applicant does not teach the above of predetermined meal indications associated with a time of receipt.
Blythe was only used to teach predetermined number of meals. Wei alone teaches associating receipt meal indications with time of receipt.
Wei teaches:
Fig. 4A and logging meals with current analyte level at meal time…
“FIG. 4A depicts an example embodiment of a screen 402 for logging meals and associated meal information, for example, as would be used at 302 of method 300 (see FIG. 3A). Screen 402 can include indications 404 and 406 of analyte levels determined from data received from analyte sensor 104 (not shown). Indication 404 is a numerical display of the user's current or last received analyte level (e.g. mg/dL). Indication 406 is a graphical display depicting a trace or curve of the user's analyte level (y axis) over time (x axis) against a shaded region 407 indicative of normal or desired analyte level range. Such an indication 406 can show data over any desired time period (e.g., eight hours or other periods).” [0129]
“Screen 402 also includes a Log Meal selectable button or field 412. Selection of the Log Meal button 412 can direct the user to screen 420 depicted in FIG. 4B. If the device on which the meal monitor application is operating includes a camera, then screen 420 can include a selectable Take or Select Photo button or field 422 that, upon selection, can open a camera application with access to the device's camera to capture a photograph or image of the meal for storage and association therewith…” [0132]
Example of Fig. 4B teaches “Time” related to the input of the meal button…
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Figs. 4A and 4B with entry time…
“Although not all variations are depicted, it is recognized that all aspects of meal information described herein can be input via one or more screens similar to those described with respect to FIGS. 4A and 4B, including, but not limited to, the entry of time of meal information, meal portion information, nutritional content, representative meal icons, and so forth.” [0134]
Therefore, Wei teaches a button and time of meal, as well as collecting analyte level at that time.
From Wei:
“High glucose levels are primarily driven by the consumption of food. The level of post-prandial glucose is related to the amount of carbohydrates and other meal components consumed by the individual, as well as to the individual's physiological response to meals. Each individual's glycemic response can now be tracked and better understood by in vivo analyte (e.g., glucose) monitoring devices. However, a challenge for analysis of this influx of data is to represent the data in a meaningful manner that enables efficient action.” [0005]
Therefore, Wei teaches time associated with analyte (glucose) collection, and this happening at the time of the meal.
Further, Blythe's sample size "m" is not predetermined. Rather, Blythe's sample size "m" is dynamically calculated using the equation m = trunc[K(s/A)2] + 1, where "s" is the standard deviation of the patient's own pre- and post-prandial glucose measurements, estimated from the data as it is collected. (Blythe at [0074], [0080]- [0081]). Because the standard deviation "s" changes each time a new measurement pair is acquired, the calculated sample size "m" is not fixed in advance but rather fluctuates as additional data is gathered. Blythe's own Example 2 confirms this: the value of "m" changes from day to day, for instance, m = 7 on day 2, m = 10 on day 3, m = 7 on day 4, m = 6 on day 5, and m = 11 on day 8, as the running standard deviation of the patient's measurements evolves. (Blythe at Table 2). Blythe expressly acknowledges this variability, stating that "the number of measurement results comprising the sample size 'm'" is intended to "vary" in order to maintain fixed statistical power and significance. (Blythe at [0095], [0100]). Blythe's dynamically computed, continuously varying sample size "m" does not disclose, teach, or suggest such a predetermined number of meal indications.
The Examiner respectfully disagrees. Blythe teaches control of statistical power to determine an average. This is based on number of samples. This predetermines the sample size.
While this is an apparent improvement over just providing a predetermined number, they also teach this approach.
From Blythe…
“While there is no fixed rule for the sample size "n", from FIG. 2A, it can be seen that for a sample size of n=20 (proximate the area noted by arrow 102) or more should provide convergence in the intervals. However, normal use of such statistical calculations would result in associated type 1 and type 2 errors i.e. providing the patient with a false negative results or a false positive result.” [0071]
Therefore, using a predetermined number is not a novel concept, Blythe just builds on this.
Accordingly, Blythe fails to cure the deficiencies of Wei, Budiman, and Mueller with respect to this limitation.
Blythe alone teaches using a predetermined number for samples
III. No Motivation to Combine Blythe with Wei
The Office alleges that it would have been obvious to combine Blythe with Wei on the ground that Blythe teaches statistical power when determining sample size for accuracy of results, and that the combined references would benefit by using an appropriate sample size that provides meaningful average results. (Office Action at page 25). Applicant respectfully submits this rationale is insufficient for several reasons.
Wei is directed to meal monitoring, meal logging, and glucose response tracking for the purpose of providing medication dose recommendations and refining insulin bolus titration over time. Blythe, by contrast, is directed to a statistical validation methodology for glucose threshold alerting, in which the system determines whether a patient's pre- and post-prandial glucose differential has exceeded a predetermined threshold with statistical significance. (Blythe at [0008], [0095]). Blythe does not generate insulin dose recommendations, does not accumulate meal indications for the purpose of computing an average meal insulin response, and does not address the problem of estimating or refining meal bolus amounts. These references address fundamentally different technical problems: Wei addresses the problem of how to monitor meals and refine insulin dosing recommendations based on accumulated meal and glucose data, whereas Blythe addresses the problem of how to ensure that a glucose threshold alert is statistically reliable before it is presented to the user. A person of ordinary skill in the art seeking to improve Wei's meal insulin response averaging would not have looked to Blythe's glucose alert validation framework for guidance, because Blythe does not concern itself with insulin dose computation or meal bolus averaging in any respect.
Wei and Blythe are analogous prior art. They are both directed to measuring glucose and insulin response.
Further, the Office's stated rationale for combining Blythe with Wei is that Blythe "teaches statistical power when determining sample size for accuracy of results" and that "[t]he combined references benefit by using an appropriate sample size that provides meaningful average results." (Office Action at page 25). This rationale is conclusory. The Office merely restates what Blythe independently teaches and then asserts, without explanation, that the combined references would "benefit" from that teaching. The Office does not identify any problem recognized in Wei that would have prompted a person of ordinary skill in the art to look to Blythe's statistical power framework, nor does the Office explain how Blythe's glucose threshold alerting methodology would have been adapted to Wei's meal insulin response averaging in a predictable manner. Therefore Applicant respectfully submits the proposed combination of Wei and Blythe lacks a sufficient motivation to combine and is improper.
Wei teaches calculating various average values including glycemic response and average post meal response in the past.
From Wei…
“As with the meal monitor application, the EBA can include the functionality to generate one or more reports for the user to assist in interpreting and understanding the collected data. FIG. 16 depicts an example embodiment of a meal history report 1600. Report 1600 provides an easy way to reference a summary of the collected data for a particular menu item. A first section 1602 of report 1600 can include a picture 1605 of the meal as a thumbnail (clicking on the thumbnail can cause a full size photo to be displayed), alongside an average dose 1606 for this meal (which in some embodiments can be weighted to count more recent doses more heavily than older doses), and an icon 1608 indicating the average post meal response in the past, where a checkmark indicates the post-prandial data is generally in a target range, a downward arrow indicates post-prandial data is generally beneath a target range, and an upward arrow indicates post-prandial data is generally above the target range. A variety of known methods for evaluating hypoglycemia risk can be used for this purpose.” [0229]
Therefore, average dose for the meal and can be weighted based on older doses and average post meal response in the past. Wei alone is interested in averages based on prior data. Wei also recognizes the problem of using older data versus more recent data, therefore the sample data is important to Wei.
For at least the above reasons, claim 6 is patentable over Wei, Budiman, Mueller, and Blythe, alone or in combination. Claim 13 recites similarly to that of claim 6 and is patentable over Wei, Budiman, Mueller, and Blythe, alone or in combination for the same reasons given above. Each of claims 7-9, 11-12, 14-16, 19-21, and 23-25 depend on claims 6 and 13 and are also patentable over Wei, Budiman, Mueller, and Blythe, alone or in combination due at least to their dependencies on claims 6 and 13.
Applicant respectfully requests withdrawal of the present rejection of claims 6-9, 11-16, 19-21, and 23-25 under 35 U.S.C. § 103.
The rejection is respectfully modified but maintained for the claim amendments.
Claims 10, 17, and 22
Claims 10, 17, and 22 are rejected under 35 U.S.C. § 103 as being unpatentable over Wei, Budiman, Mueller JR, and Blythe in further view of Pub. No. US 2008/0172026 to Blomquist ("Blomquist"). Applicant respectfully traverses.
Claims 10, 17, and 22 depend on claims 6 and 13. As noted above, claims 6 and 13 are patentable over Wei, Budiman, Mueller JR, and Blythe, alone or in combination because none of the references disclose, teach, or suggest a processor function of "after receipt of a predetermined number of meal indications at a mealtime associated with the time of receipt, generate an average meal insulin response based on the delivered meal boluses and blood glucose data for the mealtime associated with the time of receipt." Blomquist fails to cure this deficiency.
The rejection is respectfully modified but maintained for the claim amendments.
Blomquist is directed to recording and displaying aggregate insulin delivery data, including the amount of insulin broken down by meal bolus, correction bolus, and basal delivery as daily totals and average daily percentages for a predetermined number of days. (Blomquist at [0057]-[0060]). Blomquist's disclosure is limited to retrospective data aggregation and historical reporting of insulin delivery statistics. Blomquist does not disclose accumulating a predetermined number of meal indications at a mealtime associated with a time of receipt, nor does it disclose generating an average meal insulin response based on delivered meal boluses and blood glucose data for such a mealtime. Accordingly, Blomquist fails to cure this deficiency.
Thus claims 6 and 13 and therefore claims 10, 17, and 22 are patentable over Blomquist, Wei, Budiman, and Mueller JR, alone or in combination. Applicant respectfully requests withdrawal of the present rejection of claims 10, 17, and 22 under 35 U.S.C. § 103.
The rejection is respectfully modified but maintained for the claim amendments.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 6-17 and 19-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 6 recites
“… and after receipt of a predetermined number of meal indications at a mealtime associated with the time of receipt, generate an average meal insulin response based on the delivered meal boluses and blood glucose data for the mealtime associated with the time of receipt” where no teaching of predetermined meal indications associated with the time of receipt and average meal insulin response based on delivered meal boluses and blood glucose data associated with the time of receipt can be found in the written disclosure.
From Applicant’s specification:
“After receipt of a predetermined number of meal indications, the algorithm or AP application may generate an average meal insulin response, wherein the predetermined number of meal indications may be nine or the like and the average meal insulin response is an estimated average meal bolus for delivery after each meal (270).” [0023]
Therefore, 1) there is no association with predetermined number of meal indications associated with a time of receipt; 2) “meal insulin response” is an estimate of average meal bolus and does not include blood glucose data; and 3) there is no teaching of blood glucose data associated with time of receipt. Claim 13 has a similar problem.
Claims 7-12, 14-17, and 19-25 are further rejected as they depend from their respective claims 6 and 13.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6-17 and 19-25 are 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 6 recites “… and after receipt of a predetermined number of meal indications at a mealtime associated with the time of receipt, generate an average meal insulin response based on the delivered meal boluses and blood glucose data for the mealtime associated with the time of receipt” It is indefinite as to generate an average meal insulin response based on both meal boluses and blood glucose data. Bolus is an injected insulin, where glucose measures blood sugar. It is indefinite as to how an average insulin response is generated that includes both insulin and glucose. For examination purposes, this is interpreted as some type of average blood response to glucose or insulin or both. Claim 13 has a similar problem.
Claims 7-12, 14-17, and 19-25 are further rejected as they depend from their respective claims 6 and 13.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 6-9, 11-16, 19-21, and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2018/0197628 to Wei et al. in view of Pub. No. US 2007/0112298 to Mueller, JR et al. and in further view of Pub. No. US 2013/0277233 to Blythe et al.
Regarding claim 6
A non-transitory computer readable medium embodied with programming code executable by a processor, and the processor when executing the programming code is operable to perform functions for treatment of diabetes, including functions to:
Wei et al. teaches:
Treatment program including insulin delivery (diabetes treatment)…
“An output issued at 320 can supply information directly to the user's medication or treatment program on the same device or a different device in communication with the device executing the meal monitor application. This information can be used by the treatment program in determining whether a modification to a user's treatment profile (e.g., a basal insulin delivery schedule or a bolus dose) is warranted or to prompt the user as to whether a change to the user's treatment profile should be implemented.” [0159]
receive a meal indication related to ingestion of a meal in response to an input to a meal button presented on a graphical user interface;
Wei et al. continues to teach:
User to define (receive an indication) related to consumables (ingestions) of a meal…
“The software can provide a mechanism for the user to define consumables (e.g., a type of food, type of drink, or portion thereof), in any fashion that is convenient to the user. These consumables will be referred to generally herein as a meal or meals, and these terms are used broadly to denote all types of food and drink.” [0073]
Fig. 4A, and “LOG MEAL” (input to a meal button)…
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Fig. 4A is a graphical user interface…
“FIGS. 4A-E depict example embodiments of graphical user interface (GUI) visual arrangements or screens. These screens can be displayed on any of the embodiments of reader device 120, drug delivery device 160, or personal computer system 170 described herein.” [0128]
A selectable button and selection (input) of the button…
“Screen 402 also includes a Log Meal selectable button or field 412. Selection of the Log Meal button 412 can direct the user to screen 420 depicted in FIG. 4B. If the device on which the meal monitor application is operating includes a camera, then screen 420 can include a selectable Take or Select Photo button or field 422 that, upon selection, can open a camera application with access to the device's camera to capture a photograph or image of the meal for storage and association therewith. Selection of button 422 can also give the user the option to select a photo from a gallery of photos previously used to log meals, or from a gallery of photos stored in the device's general purpose photo gallery. If the user captures or otherwise selects a photo of the meal, then that photo can then be displayed on screen 420. In some embodiments, selection of Log Meal button 412 of FIG. 4A can cause the meal monitor application to immediately open the device camera application to allow the user to take a photo and thereby further streamline the image entry process.” [0132]
“The meal monitor application can be programmed to store the information about any and all prior meals and associated meal information previously consumed by the user. That information can be presented to the user as options from which the user can select to identify a particular meal or aspect thereof that was more recently consumed. For example, when the user is prompted to enter meal information, the meal monitor application can present a list of options from which the user can choose. This list can be ordered such that the most commonly consumed or selected meals are presented first or at the top of the list, with remaining meals presented in order of decreasing frequency of consumption (e.g., the most commonly consumed meal is presented first, the second most commonly consumed meal is presented second, and so forth with the least commonly consumed meal presented at the end).” [0113]
obtain a time of receipt of the input to the meal button related to ingestion of the meal;
Example of Fig. 4B teaches “Time” related to the input of the meal button…
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Figs. 4A and 4B with entry time…
“Although not all variations are depicted, it is recognized that all aspects of meal information described herein can be input via one or more screens similar to those described with respect to FIGS. 4A and 4B, including, but not limited to, the entry of time of meal information, meal portion information, nutritional content, representative meal icons, and so forth.” [0134]
Log (note) time period and meal consumed…
“The meal monitor application can allow an individual to log information about each meal that the individual consumes (i.e., each “meal event”), including a photo of the meal. The meal monitor application can associate analyte data from the same general time period where the user's log entry indicated that a meal was consumed.” [0075]
determine a meal bolus to be delivered based on the received meal indication;
{
From Applicant’s specification…
Process of using a user’s insulin needs to estimate user’s mealtime insulin needs and determine a meal bolus based on the meal indication…
“FIG. 2 shows a flow chart of an example of a process that utilizes a user's total daily insulin needs to generate an estimate of a user's mealtime insulin needs. The process 200 may be implemented using an algorithm or an AP application. At step 210, the algorithm executed by the AID system or an artificial pancreas (AP) application may receive a meal indication related to ingestion of a meal. The received indication may be by a user interaction with a meal button (e.g., a physical button) on a medical device or a user interface of a personal diabetes management device or a smartphone (e.g., a physical button or a “soft button” presented on a graphical user interface presented on a touchscreen). A time of receipt of the meal indication may be noted by the algorithm or AP application. For example, upon receipt of the indication of the meal, the algorithm or AP application may obtain a time of day and date from a clock executed by a processor, from GPS signals or the like (220). The algorithm or AP application may receive an estimated amount of carbohydrates in the meal indicated by the user (230). The received meal indication and time of receipt of the meal indication may be maintained in a log stored in a memory by the algorithm or the AP application. For example, the algorithm or the AP application may maintain a log of information related to each respective received meal indication (240). Examples of the received meal indication may include receipt of the meal indication, noted times of receipt of each respective received meal indication, estimated amount of carbohydrates in the meal associated with the meal indication, and a blood glucose measurement closest in time to the respective noted time of receipt of each respective received meal indication. At 250, the algorithm or AP application may determine a meal bolus to be delivered based on the received meal indication, wherein the determined meal bolus may be calculated by the algorithm or received via a user interface. The algorithm or AP application may, at 260, output instructions to deliver the determined meal bolus, wherein the output instructions may cause the generation of a prompt on a personal diabetes management device, may cause a medical device to deliver the calculated meal bolus, or both.” [0022]
After number of meal indications, generate a meal insulin response…
“After receipt of a predetermined number of meal indications, the algorithm or AP application may generate an average meal insulin response, wherein the predetermined number of meal indications may be nine or the like and the average meal insulin response is an estimated average meal bolus for delivery after each meal (270).” [0023]
Calculate an estimate of user’s average insulin need for each meal…
“In the example of FIG. 3, using the estimate of TDI1 made at 310, the algorithm or AP application may calculate an estimate of the user's average insulin needs for each meal (320). The calculation of the user's average insulin needs may consider that the calculated TDI incorporates the sum of all the user's insulin needs, including both their basal and bolus insulin deliveries. For example, 50% of TDI1 may be estimated to constitute the sum of insulin needs for all meal events, here the factor 50% accounts for the underlying assumption that half of the user's TDI needs correspond to the user's basal requirements and the other half accounts for the user's meal bolus requirements. This proportion may, for example, vary widely, such as 25% or 75%, depending on the user's living patterns (for example, increased activity level may mean lower basal requirements in proportion to TDI or the like) and meal patterns (for example, the ingestion of smaller meals, or partaking in fewer meals, may need higher basal requirements in proportion to TDI or the like).” [0026]
Meal bolus dosage is approximately 1/6 of TDI…
“In a specific example, a user's total daily insulin may be considered to contain 3 meal events per day (e.g., breakfast, lunch and dinner), meaning that an initial estimate of the meal coverage needs (i.e., a meal bolus dosage) may be set as approximately 1/3rd of the sum of all meal insulin needs (i.e. total bolus dosage amounts averaged over 3 meals), or approximately 1/6th of the user's TDI (i.e., the insulin delivered by the meal boluses accounts for approximately 4 hours out of the 24 hour period covered by the TDI).” [0027]
From Applicant’s specification on meal bolus based on (constrained to) estimated mealtime dose of insulin of 1/6 TDI…
“In an example of open-loop use in which a user injects himself or herself and delivers insulin, the estimated dose (i.e., amount) of insulin recommended for delivery by a personal diabetes management device executing the algorithm may be constrained to limit an estimated mealtime dose of insulin to no greater than I/6th of the user's TDI.” [0028]
Therefore, the meal bolas is 1/6 the total daily insulin value.
Determining a meal bolus using proportions of TDI (meal estimate) and
A meal estimate is recalculated
“In this example when determining a meal bolus, such as in step 250 of FIG. 2, an element of adaptivity is preserved by assessing the insulin needs per meal as proportions of the user's TDI instead of actual units of insulin. For example, a meal estimate may be recalculated after each meal button interaction by a user, thus converging over time to a value that represents the user's true meal insulin needs for each meal based on the button interaction corresponding to the respective meal.” [0032]
The above teaches using proportions of TDI.
}
Entering a meal menu item and carbohydrates estimated, where bolus is determined for that meal…
“Although not shown, when entering the description of a meal menu item, the user can also enter a corresponding bolus dose that would be administered before or after consumption of the meal menu item, e.g., the meal bolus amount. This can be the amount that the patient typically takes upon consumption of the meal. Alternatively, the carbohydrates can be estimated for that meal and, based on the patient's carb ratio, a bolus calculator can be used to determine the bolus amount for that meal. However, any mechanism may be used to determine the bolus amount to enter for each meal. There are many tools available that can be integrated with, or used in conjunction with, the EBA that can help determine these meal bolus amounts, such as a bolus calculator, a food library that provides nutritional information, etc. In some embodiments, one or more of those tools are linked or otherwise made accessible here to allow the patient to make that calculation. Conveniently, this activity can be done all at once and with the assistance of an HCP or nutritionist.” [0201]
Example of estimate (determine) bolus for a meal…
“As such, it is feasible to empirically observe a patient's average analytic (e.g., glucose) response to a given meal. The empirically observed glucose response may be used to estimate insulin bolus for a given meal. In addition, the patient's empirically determined glucose response to a given meal may also be used to augment their diabetes management by steering them away from certain meals and towards others which permit better glucose management.” [0187]
determine a period of time for a suspension of delivery of basal insulin based on the meal bolus;
Determine whether modification of basal insulin delivery schedule is warranted…
“An output issued at 320 can supply information directly to the user's medication or treatment program on the same device or a different device in communication with the device executing the meal monitor application. This information can be used by the treatment program in determining whether a modification to a user's treatment profile (e.g., a basal insulin delivery schedule or a bolus dose) is warranted or to prompt the user as to whether a change to the user's treatment profile should be implemented.” [0159]
See Suspension below.
output an instruction causing delivery of the determined meal bolus and the suspension of delivery of basal insulin through actuation of a pump mechanism of a drug delivery device; and
{
From Applicant’s specification on “suspension of delivery of basal insulin…
“In addition, due to utilizing an estimated mealtime bolus Mest,n, an automated insulin delivery algorithm may be enabled to suspend delivery of basal insulin for approximately 1. 5 hours (i.e. approximately 90 minutes), which results in l.5·1/48 ~ 3% of possible insulin deficiency accumulation (where the 48 factor corresponds to 50% of the user's TDI accounting for the user's basal needs, which is then converted as an hourly rate per day, or l/2· 1/48). The suspension of basal insulin delivery for the approximately 1. 5 hours may compensate for a proportion of any excess insulin delivered above basal that may be caused by the delivery of the estimated meal bolus. This enables the system to compensate for a discrepancy in the meal estimates by an amount equal to suspension of basal delivery by approximately 1.5 hours, reducing the severity of adverse impacts to the user's blood glucose measurements due to the discrepancy.” [0031]
Therefore, delivery of basal is suspended for mealtime bolus insulin.
}
Providing (output) recommendation (instruction) of dose related to meal (meal bolus)…
“The present subject matter broadly relates to systems, devices, and methods for, among others, the collection of information about analyte levels of certain individuals and information about meals that those individuals consume, and providing a medication dose recommendation based on the collected information.” [0002]
Output of information (instruction) for a modification of basal insulin delivery schedule or bolus dose…
“An output issued at 320 can supply information directly to the user's medication or treatment program on the same device or a different device in communication with the device executing the meal monitor application. This information can be used by the treatment program in determining whether a modification to a user's treatment profile (e.g., a basal insulin delivery schedule or a bolus dose) is warranted or to prompt the user as to whether a change to the user's treatment profile should be implemented.” [0159]
Where recommendation includes bolus for each meal…
“When used with an analyte monitoring system 100, these embodiments can capture, categorize, and index glucose responses to the meals and meal-time insulin doses (administered to compensate for the meal) and thus provide the user with additional data from which the user's insulin dosages can be perfected or “fine-tuned.” In addition, over time, the example embodiments can provide recommendations as to the titration of the bolus amount for each meal.” [0193]
Drug delivery using a pump…
“Drug delivery device 160 is capable of injecting or infusing a drug, such as but not limited to insulin, into the body of the individual wearing sensor control device 102. Like reader device 120, the drug delivery device can include processing circuitry, non-transitory memory containing instructions executable by the processing circuitry, wireless or wired communication circuitry, and a user interface including one or more of a display, touchscreen, keyboard, an input button or instrument, and the like. Drug delivery device 160 can include a drug reservoir, a pump, an infusion tube, and an infusion cannula configured for at least partial implantation into the user's body. The pump can deliver insulin from the reservoir, through the tube, and then through the cannula into the user's body. Drug delivery device 160 can include instructions, executable by the processor, to control the pump and the amount of insulin delivered. These instructions can also cause calculation of insulin delivery amounts and durations (e.g., a bolus infusion and/or a basal infusion profile) based on analyte level measurements obtained directly or indirectly from sensor control device 102. Alternatively, calculations of insulin delivery amounts and durations, and the control of the pump, can be performed by reader device 120 directly. The drug delivery device can be configured to communicate directly with reader device 120 in the form of a closed loop or semi-closed loop system. Alternatively, the drug delivery device can include the functionality of reader device 120 described herein, or vice versa, to arrive at one integrated reader and drug delivery device.” [0052]
See Suspension below.
receive blood glucose data from a glucose monitor corresponding to a post prandial period of insulin delivery of the drug delivery device; and
Example of collect (receive) analyte responses (glucose readings) for meal-related responses (post prandial period)…
“In many embodiments, the individual's meal-related analyte responses collected by an analyte monitoring system, such as an in vivo analyte monitoring system, can be compared with or linked to meal information to discover common consistencies (or inconsistencies) along with trends therein based on related historical glucose readings and associated algorithms, variables, weights, comparisons, and trends.” [0012]
after receipt of a predetermined number of meal indications at a mealtime associated with the time of receipt, generate an average meal insulin response based on the delivered meal boluses and blood glucose data for the mealtime associated with the time of receipt.
Average glycemic (insulin) response for same or similar meal, where a number of meals is used to determine the average response…
“If the user consumes the same or a similar meal on repeated occasions, then a glycemic central tendency (e.g., an average or median glycemic response) can be determined for that meal and that central tendency can be displayed. For example, if the peak of the glycemic response is the metric of choice, then when multiple identical meals have been recorded by the system, the median of the peak values can represent the glycemic response metric for this meal. Other forms of glucose response metrics such as a glucose trace or a parametric fit to the glucose trace, may be used. Alternatively, the glycemic response of every meal can be displayed regardless of whether each meal is the same or similar to another meal on the list or in the database. In yet another embodiment, the meal monitor application may generate a glycemic response as representative of all similar meals; for instance, if the glycemic response is displayed as a trace, the trace representative of all the similar meals may be made up of hourly medians of all of the individual glycemic responses, where time is relative to the start of the meal.” [0080] Inherent with average response is response per number of meals (glycemic responses/number of meals).
Meal consumed multiple times in the past (frequency of past meal selections, predetermined number) and average value of glycemic response for that meal (therefore, average based on the frequency, predetermined number) of meals….
“In other embodiments, when a meal event is detected, a list of all previous meals can be displayed to the user (or accessed by a virtual selectable button labelled, for instance, “previous meals”). The user can be permitted to select one meal as corresponding to the detected meal event. The list of all previous meals can be sortable by frequency of past selections of that meal and/or by magnitude or severity of the glycemic response to that meal, which can be an average or median value if that meal has been consumed multiple times in the past. The list may be ordered any number of ways, but the preferred embodiment is to order first by the most frequently selected meals, and next by the most recent meals. Each meal can be associated with a unique identifying code. Meal names that are entered by the user with text that is not identical but that resemble each other can be assigned the same code. If the same meal is entered and selected one or more times, that unique identifying code will be stored multiple times associated with the different times for this same meal. When displayed on a list sorted by glucose response magnitude, the meal monitor application can detect when meals are repeated by detecting that they have the same unique code. The meal monitor application can create a list of all the glucose responses for each repeated meal and determine the mean or median of these responses and associate this with the unique code. When this is done for all of the unique codes, then the final list for display can be generated for all of the unique meals including meals entered only once, and repeated meals, and the list can be sorted by the glucose response magnitude associated with each unique code.” [0119]
Estimate bolus based on glucose response for a given meal (therefore, an average response for a meal result in an average bolus for that meal)…
“As such, it is feasible to empirically observe a patient's average analytic (e.g., glucose) response to a given meal. The empirically observed glucose response may be used to estimate insulin bolus for a given meal. In addition, the patient's empirically determined glucose response to a given meal may also be used to augment their diabetes management by steering them away from certain meals and towards others which permit better glucose management.” [0187]
Statistically calculate average glycemic (insulin) response over number of times meal was consumed (therefore predetermined based on number of time meal was consumed)…
“The effect of different meals on a patient's glucose response may be compared by using the GRR and coding the glucose traces (e.g., by color or pattern) for each menu item as shown in FIG. 17C. The coding enables the HCP or patient to visually compare the patient's glucose response, and provide input to meal selection and menu planning to optimize the patient's glycemic control. The average glycemic response to a given meal may be statistically calculated over the number of times the meal was consumed.” [0239]
Analyte data (insulin needs) and meal based on N days…
“In this embodiment of method 330, the meal monitor application runs the meal event detector on the analyte data from a prior time range (e.g., N days, hours, minutes) using a first set of sensitivity settings (e.g., analyte data magnitude thresholds, duration thresholds, rate of change thresholds, or others) at 332. Here, method 330 examines the analyte data over the previous N days, where N can be any desired value. Next, a determination is made at 334 whether the number of meal events detected over those N days is less than or equal to a maximum, which in this embodiment is (the baseline value added to the variation value) multiplied by N. In an example where N is three, the baseline value is three meals/day, and the variation value is one meal/day, then the determination at 334 would evaluate whether the number of meal events detected is less than or equal to twelve (e.g., the maximum).” [0098]
See Predetermined Number below.
See Time below.
Suspension
The combined references teach insulin and modification of basal schedule. They also teach hypoglycemic events. They do not teach suspension.
Mueller, JR et al. also in the business of insulin and hypoglycemic events teaches:
Prevent hypoglycemia by suspension of basin insulin after bolus…
“…Effectively controlling blood glucose levels after consumption of high GI foods may be carried out by adding time-shifted basal insulin to the meal bolus delivered to cover the anticipated carbohydrate intake. Conversely, a suspension (and/or reduction) in basal insulin, corresponding to the amount of time-shifted basal insulin added to the bolus, prevents hypoglycemia one to two hours after the high GI food is ingested. By adding the basal insulin to the presently delivered bolus, more insulin becomes immediately available to the user. The corresponding suspension (and/or reduction) in basal insulin prevents hypoglycemia in the hours subsequent to delivery of the bolus. In particular embodiments, consumption of high GI foods necessitates delivery of time-shifted insulin because high GI foods cause fast and high blood glucose responses. In these embodiments, fast and high blood glucose responses require more insulin to be delivered on the front end (immediately prior to consumption) and less insulin needed on the back end (one to two hours after consumption); a process that is carried out by utilizing time-shifted basal insulin.” [0048]
Example of a period of time…
“In particular embodiments, the external infusion device may include the capability to deliver time-shifted basal insulin. Time-shifting basal insulin is the process by which a portion of basal insulin (i.e., a two, three, or four hour block of basal insulin) is added to a bolus or current basal rate. To more clearly describe the time-shifting process, a standard plot of insulin delivery from an external infusion device is shown in FIG. 3. In FIG. 3, between the hours of 12:00 pm and 10:00 pm, the external infusion device continuously delivers insulin at a rate 300 of 1 Unit/hour (basal rate). At 2:00 pm, a three unit bolus 310 is delivered to the user to prepare for an upcoming meal (meal bolus). In contrast, FIG. 4 shows a similar graph of insulin delivery from an external infusion device using time-shifted basal insulin. In this figure, the basal rate 400 from 12:00 pm to 3:00 pm is 1 Unit/hour. Again, a three unit bolus 410 is delivered at 2:00 pm. However, as shown in FIG. 4, a block of basal insulin 420 that was to be delivered between the hours of 3:00 pm to 6:00 pm has been time-shifted and added to the bolus 410 for immediate delivery at 2:00 pm. Accordingly, in this example, basal insulin delivery is suspended between the hours of 3:00 pm and 6:00 pm to account for the insulin added to the bolus.” [0046]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to suspend basal insulin as taught by Mueller, JR et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Motivation is provided by Mueller who teaches avoiding hypoglycemia. Further, it would be obvious to a person skilled in the art to avoid hypoglycemia and it would be further by a person skilled in the art to suspend basal delivery after bolus insulin is given.
Predetermined Number
The combined references teach insulin and similar meals and number of times meal was consumed. They do explicitly teach predetermined number of meals.
Blythe et al. also in the business of insulin teaches;
“In one aspect, a method of alerting a user with a diabetes management device that the user's blood glucose data around a meal event has exceeded a predetermined differential threshold is provided. The method can be achieved by: collecting, with the diabetes management device, a plurality of pre and post-prandial pairs (N) of glucose concentration measurements about a particular meal event; calculating, with a microprocessor of the diabetes management device, a plurality of differential value (D) based on a difference between the collected plurality of pre and post-prandial pairs of glucose concentrations about the particular meal event; determining, with the microprocessor, in the event that the number of N pairs of pre and post prandial measurements for the particular meal event are equal to or greater than a calculated sample size (m); ascertaining with at least one statistical test as to whether the threshold value (.DELTA.) has been exceeded with an acceptable level of certainty; and upon the ascertaining that the threshold value has been exceeded with an acceptable level of certainty, outputting to the user that for the particular meal, the differential value D for the number of pairs of glucose measurements has been exceeded the threshold value (.DELTA.).” [0008]
Each measurement flagged with date and time…
“Each individual measurement made by a patient may be stored in the memory of the DMU 10, and may be flagged with the corresponding date and time, and with additional flagging such as pre-meal and post-meal as it may be useful to look at how blood glucose varies around a given meal time, on average, over several consecutive days. Certain time periods may be programmed into the software of the meter to ensure that each measurement falls within a particular slot, for example before breakfast, after breakfast, before lunch, after lunch, before dinner or after dinner. It is an aspect of the present invention to provide messaging to the patient regarding the management of their condition, the specific messages depending upon the patient's measured data. However, according to the preferred embodiments, delivery of such messages to the patient may be dependent on there being a sufficient number of measured results stored within the memory of the device e.g. glucose monitoring meter, to be able to provide messaging with a certain level of confidence. In an example embodiment the required level of confidence e.g. 95% or 99% may be achieved by using a fixed significance level as well as a fixed level of statistical power, as will be described in more detail in relation to the following figures.” [0062]
“Further, as a user gains control of their diabetes with time, the magnitude of the pre/post-prandial average shift in glucose that they wish to detect should diminish. Given that the significance level (.alpha.) controls the number of false positives, and the statistical power (1-.beta.) controls the number of false negatives, then the statistical power of the test i.e. the probability of the user obtaining a false negative, would vary if a fixed sample size were to be used. Providing a patient with a false negative could be potentially dangerous in that they could take action such as injecting themselves with insulin, which may be an inappropriate action to take if the result or message provided to them was incorrect. It is the intent of applicant therefore to fix the significance level and statistical power of a statistical test analyzing the measured data, and allowing the number of measurements including the sample size, used to determine the result provided to the patient, to vary.” [0065]
“In general terms, the DMU 10 monitors pre- and post-prandial glucose results, for each mealtime, for a period until the standard deviation (SD) of the difference D between each pair of pre- and post-prandial measured results can be estimated with reasonable confidence. The 95% Confidence Interval for a SD is provided by:
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where .sigma. is the true SD, [0068] n is the sample size, [0069] s is the estimated (measured value) SD, and [0070] .chi. values refer to tabulated values of statistical distributions.” [0066] – [0070]
Example of n=20 (predetermined number) for …
“While there is no fixed rule for the sample size "n", from FIG. 2A, it can be seen that for a sample size of n=20 (proximate the area noted by arrow 102) or more should provide convergence in the intervals. However, normal use of such statistical calculations would result in associated type 1 and type 2 errors i.e. providing the patient with a false negative results or a false positive result.” [0071]
“Applicants therefore propose, in this disclosure, to implement a method of testing pre-/post prandial glucose shifts, on a meal-by-meal basis, which will always maintain a fixed statistical power and significance.” [0072]
Modifying the number of measurement results (meal indication) based on control statistical power (predetermined number) to determine an average…
“The methods described herein can be used to control the statistical power regardless of how variable an individual's readings may be (how large the SD is). As a patient gains control of their condition with time, then the magnitude of the pre-/post-prandial average glucose shift to be detected will diminish, however the statistical power i.e. the control of false negatives can still be maintained by modifying the number of measurement results comprising the sample size `m`. It is likely that the size of sample size `m` may vary in relation to the relative size of the predefined threshold value .DELTA. to be detected i.e. a smaller pre-/post-prandial average glucose shift may require a smaller sample size `N` to detect the difference whilst still meeting the required confidence interval.” [0095]
“An advantage of the present invention includes reducing the possibility of a patient being provided with a false negative result i.e. there is a significant difference between a patient's pre- and post-prandial glucose concentration averages, but the message provided to the patient says there isn't. Such a result could be misleading and potentially dangerous to the patient, should they take action guided by the result provided. The present invention therefore minimizes the risk of such events occurring by controlling the significance value .alpha. and the statistical power (1-.beta.), whilst allowing the number of pre-/post-prandial glucose measurements considered by the statistical calculation (i.e. the sample size) to vary in order to detect a significant difference from a predefined threshold value .DELTA. within the required confidence interval.” [0112]
It would have been obvious to one of ordinary skill in the art before the effective filing data to include in the method and system of the combined references the ability to use a predetermined number of meals as taught by Blythe et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Blythe et al. who teaches convergence using a sample size of 20. Blythe teaches the known use of a predetermined number of samples and time associated with measurements and the benefits of using such information. The combined references benefit as they are also in the business of providing measurements with time and providing bolus insulin. Further it would be obvious that using a statistical power also results in a predetermined number of meals as the sample size is an important consideration for achieving a result with no or reduced false-positives.
The combined references teach both predetermined sample size of 20 and statistical power. They do not explicitly teach using the predetermined sample size. However, one of ordinary skill in the art would recognize that predetermined sample size was available for use as convergence is provided.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that various methods including predetermined sample size (number of meals) could be used for providing averages. This would have been known work in the field of endeavor prompting variations of it in the same field based on convergence considerations as a known method and sample size considerations to reduce false-positives and would provide predictable results.
Time
The combined references teach time with meal. They do teach time with average.
Blythe et al. also in the business of time with meal teaches:
Insulin for regulating glucose levels (insulin response)…
“Referring to FIG. 1A, a therapeutic dosing device can also be a pump 48 that includes a housing 50, a backlight button 52, an up button 54, a cartridge cap 56, a bolus button 58, a down button 60, a battery cap 62, an OK button 64, and a display 66. Pump 48 can be configured to dispense medication such as, for example, insulin for regulating glucose levels.” [0061]
Each measurement may be flagged with date and time, and average based on time…
“Each individual measurement made by a patient may be stored in the memory of the DMU 10, and may be flagged with the corresponding date and time, and with additional flagging such as pre-meal and post-meal as it may be useful to look at how blood glucose varies around a given meal time, on average, over several consecutive days. Certain time periods may be programmed into the software of the meter to ensure that each measurement falls within a particular slot, for example before breakfast, after breakfast, before lunch, after lunch, before dinner or after dinner. It is an aspect of the present invention to provide messaging to the patient regarding the management of their condition, the specific messages depending upon the patient's measured data. However, according to the preferred embodiments, delivery of such messages to the patient may be dependent on there being a sufficient number of measured results stored within the memory of the device e.g. glucose monitoring meter, to be able to provide messaging with a certain level of confidence. In an example embodiment the required level of confidence e.g. 95% or 99% may be achieved by using a fixed significance level as well as a fixed level of statistical power, as will be described in more detail in relation to the following figures.” [0062]
It would have been obvious to one of ordinary skill in the art before the effective filing data to include in the method and system of the combined references the ability to have a time associated with meals and various measurements as taught by Blythe et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Blythe et al. who teaches the importance of calculating averages over days an at particular times to provide useful information. The combined references benefit as they are also in the business of providing measurements with time and providing bolus insulin.
Regarding claim 13
A device, comprising:
a processor;
Wei et al. teaches:
Processor…
“The processing of data and the execution of software within system 100 can be performed by one or more processors of reader device 120, computer system 170, and/or sensor control device 102. For example, raw data measured by sensor 104 can be algorithmically processed into a value that represents the analyte level and that is readily suitable for display to the user, and this can occur in sensor control device 102, reader device 120, or computer system 170. This and any other information derived from the raw data can be displayed in any of the manners described above (with respect to display 122) on any display residing on any of sensor control device 102, reader device 120, or computer system 170. The information may be utilized by the user to determine any necessary corrective actions to ensure the analyte level remains within an acceptable and/or clinically safe range.” [0056]
a user interface coupled to the processor; and
Fig. 1, ref. 122 teaches user interface…
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a memory storing programming code, and
Execution of software (programming code) within system (therefore memory storing)…
“The processing of data and the execution of software within system 100 can be performed by one or more processors of reader device 120, computer system 170, and/or sensor control device 102. For example, raw data measured by sensor 104 can be algorithmically processed into a value that represents the analyte level and that is readily suitable for display to the user, and this can occur in sensor control device 102, reader device 120, or computer system 170. This and any other information derived from the raw data can be displayed in any of the manners described above (with respect to display 122) on any display residing on any of sensor control device 102, reader device 120, or computer system 170. The information may be utilized by the user to determine any necessary corrective actions to ensure the analyte level remains within an acceptable and/or clinically safe range.” [0056]
Fig. 2A, ref. 225 teaches memory in Application’s Processor…
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operable to store data, wherein the programming code are executable by the processor; and
a transceiver operable to receive a signal containing information usable by the
programming code and transmit a signal containing information usable by or generated by the programming code;
Fig. 2A, ref. 228 and 232 teaches RF Transceiver and WiFi, NFC, etc. (other transceivers)…
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wherein the processor when executing the programming code is operable to control delivery of insulin for treating diabetes, and to perform functions, including functions to:
Fig. 1, ref. 120, 143, and 160 teach reader device for controlling drug delivery device…
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Where the reader can control the pump (delivery of insulin)…
“…. Alternatively, calculations of insulin delivery amounts and durations, and the control of the pump, can be performed by reader device 120 directly. The drug delivery device can be configured to communicate directly with reader device 120 in the form of a closed loop or semi-closed loop system. Alternatively, the drug delivery device can include the functionality of reader device 120 described herein, or vice versa, to arrive at one integrated reader and drug delivery device.” [0052]
receive, via an input to a meal button presented on the user interface, a meal indication related to ingestion of a meal;
Wei et al. continues to teach:
User to define (receive an indication) related to consumables (ingestions) of a meal…
“The software can provide a mechanism for the user to define consumables (e.g., a type of food, type of drink, or portion thereof), in any fashion that is convenient to the user. These consumables will be referred to generally herein as a meal or meals, and these terms are used broadly to denote all types of food and drink.” [0073]
Fig. 4A, and “LOG MEAL” (input to a meal button)…
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“Screen 402 also includes a Log Meal selectable button or field 412. Selection of the Log Meal button 412 can direct the user to screen 420 depicted in FIG. 4B. If the device on which the meal monitor application is operating includes a camera, then screen 420 can include a selectable Take or Select Photo button or field 422 that, upon selection, can open a camera application with access to the device's camera to capture a photograph or image of the meal for storage and association therewith. Selection of button 422 can also give the user the option to select a photo from a gallery of photos previously used to log meals, or from a gallery of photos stored in the device's general purpose photo gallery. If the user captures or otherwise selects a photo of the meal, then that photo can then be displayed on screen 420. In some embodiments, selection of Log Meal button 412 of FIG. 4A can cause the meal monitor application to immediately open the device camera application to allow the user to take a photo and thereby further streamline the image entry process.” [0132]
obtain a time of receipt of the input to the user interface providing indication of the meal;
Example of Fig. 4B teaches “Time” related to the input of the meal button…
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Figs. 4A and 4B with entry time…
“Although not all variations are depicted, it is recognized that all aspects of meal information described herein can be input via one or more screens similar to those described with respect to FIGS. 4A and 4B, including, but not limited to, the entry of time of meal information, meal portion information, nutritional content, representative meal icons, and so forth.” [0134]
Log (note) time period and meal consumed…
“The meal monitor application can allow an individual to log information about each meal that the individual consumes (i.e., each “meal event”), including a photo of the meal. The meal monitor application can associate analyte data from the same general time period where the user's log entry indicated that a meal was consumed.” [0075]
determine a meal bolus to be delivered based on the received meal indication;
Entering a meal menu item and carbohydrates estimated, where bolus is determined for that meal…
“Although not shown, when entering the description of a meal menu item, the user can also enter a corresponding bolus dose that would be administered before or after consumption of the meal menu item, e.g., the meal bolus amount. This can be the amount that the patient typically takes upon consumption of the meal. Alternatively, the carbohydrates can be estimated for that meal and, based on the patient's carb ratio, a bolus calculator can be used to determine the bolus amount for that meal. However, any mechanism may be used to determine the bolus amount to enter for each meal. There are many tools available that can be integrated with, or used in conjunction with, the EBA that can help determine these meal bolus amounts, such as a bolus calculator, a food library that provides nutritional information, etc. In some embodiments, one or more of those tools are linked or otherwise made accessible here to allow the patient to make that calculation. Conveniently, this activity can be done all at once and with the assistance of an HCP or nutritionist.” [0201]
Example of estimate (determine) bolus for a meal…
“As such, it is feasible to empirically observe a patient's average analytic (e.g., glucose) response to a given meal. The empirically observed glucose response may be used to estimate insulin bolus for a given meal. In addition, the patient's empirically determined glucose response to a given meal may also be used to augment their diabetes management by steering them away from certain meals and towards others which permit better glucose management.” [0187]
determine a period of time for a suspension of delivery of basal insulin based on the meal bolus;
Determine whether modification of basal insulin delivery schedule is warranted…
“An output issued at 320 can supply information directly to the user's medication or treatment program on the same device or a different device in communication with the device executing the meal monitor application. This information can be used by the treatment program in determining whether a modification to a user's treatment profile (e.g., a basal insulin delivery schedule or a bolus dose) is warranted or to prompt the user as to whether a change to the user's treatment profile should be implemented.” [0159]
See Suspension below.
output an instruction causing delivery of the determined meal bolus and suspension of delivery of basal insulin through actuation of a pump mechanism of a drug delivery device; and
Providing (output) recommendation (instruction) of dose related to meal (meal bolus)…
“The present subject matter broadly relates to systems, devices, and methods for, among others, the collection of information about analyte levels of certain individuals and information about meals that those individuals consume, and providing a medication dose recommendation based on the collected information.” [0002]
Where recommendation includes bolus for each meal…
“When used with an analyte monitoring system 100, these embodiments can capture, categorize, and index glucose responses to the meals and meal-time insulin doses (administered to compensate for the meal) and thus provide the user with additional data from which the user's insulin dosages can be perfected or “fine-tuned.” In addition, over time, the example embodiments can provide recommendations as to the titration of the bolus amount for each meal.” [0193]
Drug delivery using a pump…
“Drug delivery device 160 is capable of injecting or infusing a drug, such as but not limited to insulin, into the body of the individual wearing sensor control device 102. Like reader device 120, the drug delivery device can include processing circuitry, non-transitory memory containing instructions executable by the processing circuitry, wireless or wired communication circuitry, and a user interface including one or more of a display, touchscreen, keyboard, an input button or instrument, and the like. Drug delivery device 160 can include a drug reservoir, a pump, an infusion tube, and an infusion cannula configured for at least partial implantation into the user's body. The pump can deliver insulin from the reservoir, through the tube, and then through the cannula into the user's body. Drug delivery device 160 can include instructions, executable by the processor, to control the pump and the amount of insulin delivered. These instructions can also cause calculation of insulin delivery amounts and durations (e.g., a bolus infusion and/or a basal infusion profile) based on analyte level measurements obtained directly or indirectly from sensor control device 102. Alternatively, calculations of insulin delivery amounts and durations, and the control of the pump, can be performed by reader device 120 directly. The drug delivery device can be configured to communicate directly with reader device 120 in the form of a closed loop or semi-closed loop system. Alternatively, the drug delivery device can include the functionality of reader device 120 described herein, or vice versa, to arrive at one integrated reader and drug delivery device.” [0052]
See Suspension below.
receive blood glucose data from a glucose monitor corresponding to a post prandial period of insulin delivery of the drug delivery device; and
Example of collect (receive) analyte responses (glucose readings) for meal-related responses (post prandial period)…
“In many embodiments, the individual's meal-related analyte responses collected by an analyte monitoring system, such as an in vivo analyte monitoring system, can be compared with or linked to meal information to discover common consistencies (or inconsistencies) along with trends therein based on related historical glucose readings and associated algorithms, variables, weights, comparisons, and trends.” [0012]
after receipt of a predetermined number of meal indications at a mealtime associated with the time of receipt, generate an average meal insulin response based on the delivered meal boluses and blood glucose data for the mealtime associated with the time of receipt.
Average glycemic (insulin) response for same or similar meal, where a number of meals is used to determine the average response…
“If the user consumes the same or a similar meal on repeated occasions, then a glycemic central tendency (e.g., an average or median glycemic response) can be determined for that meal and that central tendency can be displayed. For example, if the peak of the glycemic response is the metric of choice, then when multiple identical meals have been recorded by the system, the median of the peak values can represent the glycemic response metric for this meal. Other forms of glucose response metrics such as a glucose trace or a parametric fit to the glucose trace, may be used. Alternatively, the glycemic response of every meal can be displayed regardless of whether each meal is the same or similar to another meal on the list or in the database. In yet another embodiment, the meal monitor application may generate a glycemic response as representative of all similar meals; for instance, if the glycemic response is displayed as a trace, the trace representative of all the similar meals may be made up of hourly medians of all of the individual glycemic responses, where time is relative to the start of the meal.” [0080] Inherent with average response is response per number of meals (glycemic responses/number of meals).
Meal consumed multiple times in the past (frequency of past meal selections, predetermined number) and average value of glycemic response for that meal (therefore, average based on the frequency, predetermined number) of meals….
“In other embodiments, when a meal event is detected, a list of all previous meals can be displayed to the user (or accessed by a virtual selectable button labelled, for instance, “previous meals”). The user can be permitted to select one meal as corresponding to the detected meal event. The list of all previous meals can be sortable by frequency of past selections of that meal and/or by magnitude or severity of the glycemic response to that meal, which can be an average or median value if that meal has been consumed multiple times in the past. The list may be ordered any number of ways, but the preferred embodiment is to order first by the most frequently selected meals, and next by the most recent meals. Each meal can be associated with a unique identifying code. Meal names that are entered by the user with text that is not identical but that resemble each other can be assigned the same code. If the same meal is entered and selected one or more times, that unique identifying code will be stored multiple times associated with the different times for this same meal. When displayed on a list sorted by glucose response magnitude, the meal monitor application can detect when meals are repeated by detecting that they have the same unique code. The meal monitor application can create a list of all the glucose responses for each repeated meal and determine the mean or median of these responses and associate this with the unique code. When this is done for all of the unique codes, then the final list for display can be generated for all of the unique meals including meals entered only once, and repeated meals, and the list can be sorted by the glucose response magnitude associated with each unique code.” [0119]
Estimate bolus based on glucose response for a given meal (therefore, an average response for a meal result in an average bolus for that meal)…
“As such, it is feasible to empirically observe a patient's average analytic (e.g., glucose) response to a given meal. The empirically observed glucose response may be used to estimate insulin bolus for a given meal. In addition, the patient's empirically determined glucose response to a given meal may also be used to augment their diabetes management by steering them away from certain meals and towards others which permit better glucose management.” [0187]
Statistically calculate average glycemic response over number of times meal was consumed (therefore predetermined based on number of time meal was consumed)…
“The effect of different meals on a patient's glucose response may be compared by using the GRR and coding the glucose traces (e.g., by color or pattern) for each menu item as shown in FIG. 17C. The coding enables the HCP or patient to visually compare the patient's glucose response, and provide input to meal selection and menu planning to optimize the patient's glycemic control. The average glycemic response to a given meal may be statistically calculated over the number of times the meal was consumed.” [0239]
Analyte data (insulin needs) and meal based on N days…
“In this embodiment of method 330, the meal monitor application runs the meal event detector on the analyte data from a prior time range (e.g., N days, hours, minutes) using a first set of sensitivity settings (e.g., analyte data magnitude thresholds, duration thresholds, rate of change thresholds, or others) at 332. Here, method 330 examines the analyte data over the previous N days, where N can be any desired value. Next, a determination is made at 334 whether the number of meal events detected over those N days is less than or equal to a maximum, which in this embodiment is (the baseline value added to the variation value) multiplied by N. In an example where N is three, the baseline value is three meals/day, and the variation value is one meal/day, then the determination at 334 would evaluate whether the number of meal events detected is less than or equal to twelve (e.g., the maximum).” [0098]
See Predetermined Number below.
See Time below.
Suspension
The combined prior art teaches insulin. They also teach hypoglycemic events. They do not teach suspension.
Mueller, JR et al. also in the business of insulin and hypoglycemic events teaches:
Prevent hypoglycemia by suspension of basin insulin after bolus…
“…Effectively controlling blood glucose levels after consumption of high GI foods may be carried out by adding time-shifted basal insulin to the meal bolus delivered to cover the anticipated carbohydrate intake. Conversely, a suspension (and/or reduction) in basal insulin, corresponding to the amount of time-shifted basal insulin added to the bolus, prevents hypoglycemia one to two hours after the high GI food is ingested. By adding the basal insulin to the presently delivered bolus, more insulin becomes immediately available to the user. The corresponding suspension (and/or reduction) in basal insulin prevents hypoglycemia in the hours subsequent to delivery of the bolus. In particular embodiments, consumption of high GI foods necessitates delivery of time-shifted insulin because high GI foods cause fast and high blood glucose responses. In these embodiments, fast and high blood glucose responses require more insulin to be delivered on the front end (immediately prior to consumption) and less insulin needed on the back end (one to two hours after consumption); a process that is carried out by utilizing time-shifted basal insulin.” [0048]
Example of a period of time…
“In particular embodiments, the external infusion device may include the capability to deliver time-shifted basal insulin. Time-shifting basal insulin is the process by which a portion of basal insulin (i.e., a two, three, or four hour block of basal insulin) is added to a bolus or current basal rate. To more clearly describe the time-shifting process, a standard plot of insulin delivery from an external infusion device is shown in FIG. 3. In FIG. 3, between the hours of 12:00 pm and 10:00 pm, the external infusion device continuously delivers insulin at a rate 300 of 1 Unit/hour (basal rate). At 2:00 pm, a three unit bolus 310 is delivered to the user to prepare for an upcoming meal (meal bolus). In contrast, FIG. 4 shows a similar graph of insulin delivery from an external infusion device using time-shifted basal insulin. In this figure, the basal rate 400 from 12:00 pm to 3:00 pm is 1 Unit/hour. Again, a three unit bolus 410 is delivered at 2:00 pm. However, as shown in FIG. 4, a block of basal insulin 420 that was to be delivered between the hours of 3:00 pm to 6:00 pm has been time-shifted and added to the bolus 410 for immediate delivery at 2:00 pm. Accordingly, in this example, basal insulin delivery is suspended between the hours of 3:00 pm and 6:00 pm to account for the insulin added to the bolus.” [0046]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to suspend basal insulin as taught by Mueller, JR et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Motivation is provided by Mueller who teaches avoiding hypoglycemia. Further, it would be obvious to a person skilled in the art to avoid hypoglycemia and it would be further by a person skilled in the art to suspend basal delivery after bolus insulin is given.
Predetermined Number
The combined references teach insulin and similar meals and number of times meal was consumed. They do explicitly teach predetermined number of meals.
Blythe et al. also in the business of insulin teaches;
“In one aspect, a method of alerting a user with a diabetes management device that the user's blood glucose data around a meal event has exceeded a predetermined differential threshold is provided. The method can be achieved by: collecting, with the diabetes management device, a plurality of pre and post-prandial pairs (N) of glucose concentration measurements about a particular meal event; calculating, with a microprocessor of the diabetes management device, a plurality of differential value (D) based on a difference between the collected plurality of pre and post-prandial pairs of glucose concentrations about the particular meal event; determining, with the microprocessor, in the event that the number of N pairs of pre and post prandial measurements for the particular meal event are equal to or greater than a calculated sample size (m); ascertaining with at least one statistical test as to whether the threshold value (.DELTA.) has been exceeded with an acceptable level of certainty; and upon the ascertaining that the threshold value has been exceeded with an acceptable level of certainty, outputting to the user that for the particular meal, the differential value D for the number of pairs of glucose measurements has been exceeded the threshold value (.DELTA.).” [0008]
Each measurement flagged with date and time…
“Each individual measurement made by a patient may be stored in the memory of the DMU 10, and may be flagged with the corresponding date and time, and with additional flagging such as pre-meal and post-meal as it may be useful to look at how blood glucose varies around a given meal time, on average, over several consecutive days. Certain time periods may be programmed into the software of the meter to ensure that each measurement falls within a particular slot, for example before breakfast, after breakfast, before lunch, after lunch, before dinner or after dinner. It is an aspect of the present invention to provide messaging to the patient regarding the management of their condition, the specific messages depending upon the patient's measured data. However, according to the preferred embodiments, delivery of such messages to the patient may be dependent on there being a sufficient number of measured results stored within the memory of the device e.g. glucose monitoring meter, to be able to provide messaging with a certain level of confidence. In an example embodiment the required level of confidence e.g. 95% or 99% may be achieved by using a fixed significance level as well as a fixed level of statistical power, as will be described in more detail in relation to the following figures.” [0062]
“Further, as a user gains control of their diabetes with time, the magnitude of the pre/post-prandial average shift in glucose that they wish to detect should diminish. Given that the significance level (.alpha.) controls the number of false positives, and the statistical power (1-.beta.) controls the number of false negatives, then the statistical power of the test i.e. the probability of the user obtaining a false negative, would vary if a fixed sample size were to be used. Providing a patient with a false negative could be potentially dangerous in that they could take action such as injecting themselves with insulin, which may be an inappropriate action to take if the result or message provided to them was incorrect. It is the intent of applicant therefore to fix the significance level and statistical power of a statistical test analyzing the measured data, and allowing the number of measurements including the sample size, used to determine the result provided to the patient, to vary.” [0065]
“In general terms, the DMU 10 monitors pre- and post-prandial glucose results, for each mealtime, for a period until the standard deviation (SD) of the difference D between each pair of pre- and post-prandial measured results can be estimated with reasonable confidence. The 95% Confidence Interval for a SD is provided by:
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where .sigma. is the true SD, [0068] n is the sample size, [0069] s is the estimated (measured value) SD, and [0070] .chi. values refer to tabulated values of statistical distributions.” [0066] – [0070]
Example of n=20 (predetermined number) for …
“While there is no fixed rule for the sample size "n", from FIG. 2A, it can be seen that for a sample size of n=20 (proximate the area noted by arrow 102) or more should provide convergence in the intervals. However, normal use of such statistical calculations would result in associated type 1 and type 2 errors i.e. providing the patient with a false negative results or a false positive result.” [0071]
“Applicants therefore propose, in this disclosure, to implement a method of testing pre-/post prandial glucose shifts, on a meal-by-meal basis, which will always maintain a fixed statistical power and significance.” [0072]
Modifying the number of measurement results (meal indication) based on control statistical power (predetermined number) to determine an average…
“The methods described herein can be used to control the statistical power regardless of how variable an individual's readings may be (how large the SD is). As a patient gains control of their condition with time, then the magnitude of the pre-/post-prandial average glucose shift to be detected will diminish, however the statistical power i.e. the control of false negatives can still be maintained by modifying the number of measurement results comprising the sample size `m`. It is likely that the size of sample size `m` may vary in relation to the relative size of the predefined threshold value .DELTA. to be detected i.e. a smaller pre-/post-prandial average glucose shift may require a smaller sample size `N` to detect the difference whilst still meeting the required confidence interval.” [0095]
“An advantage of the present invention includes reducing the possibility of a patient being provided with a false negative result i.e. there is a significant difference between a patient's pre- and post-prandial glucose concentration averages, but the message provided to the patient says there isn't. Such a result could be misleading and potentially dangerous to the patient, should they take action guided by the result provided. The present invention therefore minimizes the risk of such events occurring by controlling the significance value .alpha. and the statistical power (1-.beta.), whilst allowing the number of pre-/post-prandial glucose measurements considered by the statistical calculation (i.e. the sample size) to vary in order to detect a significant difference from a predefined threshold value .DELTA. within the required confidence interval.” [0112]
It would have been obvious to one of ordinary skill in the art before the effective filing data to include in the method and system of the combined references the ability to use a predetermined number of meals as taught by Blythe et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Blythe et al. who teaches convergence using a sample size of 20. Blythe teaches the known use of a predetermined number of samples and time associated with measurements and the benefits of using such information. The combined references benefit as they are also in the business of providing measurements with time and providing bolus insulin. Further it would be obvious that using a statistical power also results in a predetermined number of meals as the sample size is an important consideration for achieving a result with no or reduced false-positives.
The combined references teach both predetermined sample size of 20 and statistical power. They do not explicitly teach using the predetermined sample size. However, one of ordinary skill in the art would recognize that predetermined sample size was available for use as convergence is provided.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that various methods including predetermined sample size (number of meals) could be used for providing averages. This would have been known work in the field of endeavor prompting variations of it in the same field based on convergence considerations as a known method and sample size considerations to reduce false-positives and would provide predictable results.
Time
The combined references teach time with meal. They do teach time with average.
Blythe et al. also in the business of time with meal teaches:
Insulin for regulating glucose levels (insulin response)…
“Referring to FIG. 1A, a therapeutic dosing device can also be a pump 48 that includes a housing 50, a backlight button 52, an up button 54, a cartridge cap 56, a bolus button 58, a down button 60, a battery cap 62, an OK button 64, and a display 66. Pump 48 can be configured to dispense medication such as, for example, insulin for regulating glucose levels.” [0061]
Each measurement may be flagged with date and time, and average based on time…
“Each individual measurement made by a patient may be stored in the memory of the DMU 10, and may be flagged with the corresponding date and time, and with additional flagging such as pre-meal and post-meal as it may be useful to look at how blood glucose varies around a given meal time, on average, over several consecutive days. Certain time periods may be programmed into the software of the meter to ensure that each measurement falls within a particular slot, for example before breakfast, after breakfast, before lunch, after lunch, before dinner or after dinner. It is an aspect of the present invention to provide messaging to the patient regarding the management of their condition, the specific messages depending upon the patient's measured data. However, according to the preferred embodiments, delivery of such messages to the patient may be dependent on there being a sufficient number of measured results stored within the memory of the device e.g. glucose monitoring meter, to be able to provide messaging with a certain level of confidence. In an example embodiment the required level of confidence e.g. 95% or 99% may be achieved by using a fixed significance level as well as a fixed level of statistical power, as will be described in more detail in relation to the following figures.” [0062]
It would have been obvious to one of ordinary skill in the art before the effective filing data to include in the method and system of the combined references the ability to have a time associated with meals and various measurements as taught by Blythe et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Blythe et al. who teaches the importance of calculating averages over days an at particular times to provide useful information. The combined references benefit as they are also in the business of providing measurements with time and providing bolus insulin.
Regarding claims 7 and 14
(claim 7) The non-transitory computer readable medium of claim 6, wherein the received meal indication includes receipt of the meal indication, a noted time of receipt of each respective received meal indication, estimated amount of carbohydrates in the meal associated with the meal indication, or a blood glucose measurement closest in time to the respective noted time of receipt of each respective received meal indication.
[No Patentable Weight is given to contingent or alternative language of “a noted time…, estimated amount…, or a blood glucose measurement… “ as only one is required]
Wei et al. teaches:
Example of Fig. 4B teaches “Time” related to the input of the meal button…
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Figs. 4A and 4B with entry time…
“Although not all variations are depicted, it is recognized that all aspects of meal information described herein can be input via one or more screens similar to those described with respect to FIGS. 4A and 4B, including, but not limited to, the entry of time of meal information, meal portion information, nutritional content, representative meal icons, and so forth.” [0134]
Log (note) time period and meal consumed…
“The meal monitor application can allow an individual to log information about each meal that the individual consumes (i.e., each “meal event”), including a photo of the meal. The meal monitor application can associate analyte data from the same general time period where the user's log entry indicated that a meal was consumed.” [0075]
Example of depicts (receipt) menu (meal display indication)…
“When one or more meal images have been captured and associated with a meal, the user can choose to view menu items by image as opposed to (or in addition to) a textual description of the meal. FIG. 14A depicts an example embodiment of home screen 1000 with a selectable field 1402 that allows the user to toggle between the text-only menu display of FIG. 14A and an image-only menu display of FIG. 14B. In FIG. 14B, each menu item is shown using only that menu item's image as a series of tiles 1404 arranged in rows and columns. The addition of new meal images further populates the display and the user can have the option of scrolling up or down as necessary. In this embodiment, each image tile 1404 is displayed with a textual indicator 1406 of the number of previous instances where that meal has been logged.” [0223]
Regarding claim 8
The non-transitory computer readable medium of claim 6, wherein the output instructions causes the generation of a prompt on a user interface of a personal diabetes management device, causes a medical device to deliver the calculated meal bolus, or both.
Wei et al. teaches:
Example of prompting on a user interface…
“If no meal information has been entered that can be associated with the detected event, then at 310, the user is prompted to enter the meal information. Example embodiments of user interfaces for prompting the user to log a meal or meal information are described with respect to FIGS. 4C-E. If no meal event has occurred, the user can decline or ignore the prompt. Otherwise, the meal information can be entered at 312.” [0105]
Regarding claim 15
The device of claim 13, wherein the outputted instruction causes a prompt to be generated on a personal diabetes management device.
Wei et al. teaches:
Example of prompting on a user interface…
“If no meal information has been entered that can be associated with the detected event, then at 310, the user is prompted to enter the meal information. Example embodiments of user interfaces for prompting the user to log a meal or meal information are described with respect to FIGS. 4C-E. If no meal event has occurred, the user can decline or ignore the prompt. Otherwise, the meal information can be entered at 312.” [0105]
Regarding claims 9 and 16
(claim 9) The non-transitory computer readable medium of claim 6, wherein the predetermined number of meal indications is at least nine and the average meal insulin response is an estimated average meal bolus for delivery after each meal.
Wei et al. teaches:
Average glycemic (insulin) response for same or similar meal, where a number of meals is used to determine the average response…
“If the user consumes the same or a similar meal on repeated occasions, then a glycemic central tendency (e.g., an average or median glycemic response) can be determined for that meal and that central tendency can be displayed. For example, if the peak of the glycemic response is the metric of choice, then when multiple identical meals have been recorded by the system, the median of the peak values can represent the glycemic response metric for this meal. Other forms of glucose response metrics such as a glucose trace or a parametric fit to the glucose trace, may be used. Alternatively, the glycemic response of every meal can be displayed regardless of whether each meal is the same or similar to another meal on the list or in the database. In yet another embodiment, the meal monitor application may generate a glycemic response as representative of all similar meals; for instance, if the glycemic response is displayed as a trace, the trace representative of all the similar meals may be made up of hourly medians of all of the individual glycemic responses, where time is relative to the start of the meal.” [0080] Inherent with average response is response per number of meals (glycemic responses/number of meals).
Estimate bolus based on glucose response for a given meal (therefore, an average response for a meal result in an average bolus for that meal)…
“As such, it is feasible to empirically observe a patient's average analytic (e.g., glucose) response to a given meal. The empirically observed glucose response may be used to estimate insulin bolus for a given meal. In addition, the patient's empirically determined glucose response to a given meal may also be used to augment their diabetes management by steering them away from certain meals and towards others which permit better glucose management.” [0187]
Predetermined Number
Predetermined Number
The combined references teach insulin and similar meals and number of times meal was consumed. They do explicitly teach predetermined number of meals.
Blythe et al. also in the business of insulin teaches;
“In one aspect, a method of alerting a user with a diabetes management device that the user's blood glucose data around a meal event has exceeded a predetermined differential threshold is provided. The method can be achieved by: collecting, with the diabetes management device, a plurality of pre and post-prandial pairs (N) of glucose concentration measurements about a particular meal event; calculating, with a microprocessor of the diabetes management device, a plurality of differential value (D) based on a difference between the collected plurality of pre and post-prandial pairs of glucose concentrations about the particular meal event; determining, with the microprocessor, in the event that the number of N pairs of pre and post prandial measurements for the particular meal event are equal to or greater than a calculated sample size (m); ascertaining with at least one statistical test as to whether the threshold value (.DELTA.) has been exceeded with an acceptable level of certainty; and upon the ascertaining that the threshold value has been exceeded with an acceptable level of certainty, outputting to the user that for the particular meal, the differential value D for the number of pairs of glucose measurements has been exceeded the threshold value (.DELTA.).” [0008]
Each measurement flagged with date and time…
“Each individual measurement made by a patient may be stored in the memory of the DMU 10, and may be flagged with the corresponding date and time, and with additional flagging such as pre-meal and post-meal as it may be useful to look at how blood glucose varies around a given meal time, on average, over several consecutive days. Certain time periods may be programmed into the software of the meter to ensure that each measurement falls within a particular slot, for example before breakfast, after breakfast, before lunch, after lunch, before dinner or after dinner. It is an aspect of the present invention to provide messaging to the patient regarding the management of their condition, the specific messages depending upon the patient's measured data. However, according to the preferred embodiments, delivery of such messages to the patient may be dependent on there being a sufficient number of measured results stored within the memory of the device e.g. glucose monitoring meter, to be able to provide messaging with a certain level of confidence. In an example embodiment the required level of confidence e.g. 95% or 99% may be achieved by using a fixed significance level as well as a fixed level of statistical power, as will be described in more detail in relation to the following figures.” [0062]
“Further, as a user gains control of their diabetes with time, the magnitude of the pre/post-prandial average shift in glucose that they wish to detect should diminish. Given that the significance level (.alpha.) controls the number of false positives, and the statistical power (1-.beta.) controls the number of false negatives, then the statistical power of the test i.e. the probability of the user obtaining a false negative, would vary if a fixed sample size were to be used. Providing a patient with a false negative could be potentially dangerous in that they could take action such as injecting themselves with insulin, which may be an inappropriate action to take if the result or message provided to them was incorrect. It is the intent of applicant therefore to fix the significance level and statistical power of a statistical test analyzing the measured data, and allowing the number of measurements including the sample size, used to determine the result provided to the patient, to vary.” [0065]
“In general terms, the DMU 10 monitors pre- and post-prandial glucose results, for each mealtime, for a period until the standard deviation (SD) of the difference D between each pair of pre- and post-prandial measured results can be estimated with reasonable confidence. The 95% Confidence Interval for a SD is provided by:
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where .sigma. is the true SD, [0068] n is the sample size, [0069] s is the estimated (measured value) SD, and [0070] .chi. values refer to tabulated values of statistical distributions.” [0066] – [0070]
Example of n=20 (predetermined number) for …
“While there is no fixed rule for the sample size "n", from FIG. 2A, it can be seen that for a sample size of n=20 (proximate the area noted by arrow 102) or more should provide convergence in the intervals. However, normal use of such statistical calculations would result in associated type 1 and type 2 errors i.e. providing the patient with a false negative results or a false positive result.” [0071]
“Applicants therefore propose, in this disclosure, to implement a method of testing pre-/post prandial glucose shifts, on a meal-by-meal basis, which will always maintain a fixed statistical power and significance.” [0072]
Modifying the number of measurement results (meal indication) based on control statistical power (predetermined number) to determine an average…
“The methods described herein can be used to control the statistical power regardless of how variable an individual's readings may be (how large the SD is). As a patient gains control of their condition with time, then the magnitude of the pre-/post-prandial average glucose shift to be detected will diminish, however the statistical power i.e. the control of false negatives can still be maintained by modifying the number of measurement results comprising the sample size `m`. It is likely that the size of sample size `m` may vary in relation to the relative size of the predefined threshold value .DELTA. to be detected i.e. a smaller pre-/post-prandial average glucose shift may require a smaller sample size `N` to detect the difference whilst still meeting the required confidence interval.” [0095]
“An advantage of the present invention includes reducing the possibility of a patient being provided with a false negative result i.e. there is a significant difference between a patient's pre- and post-prandial glucose concentration averages, but the message provided to the patient says there isn't. Such a result could be misleading and potentially dangerous to the patient, should they take action guided by the result provided. The present invention therefore minimizes the risk of such events occurring by controlling the significance value .alpha. and the statistical power (1-.beta.), whilst allowing the number of pre-/post-prandial glucose measurements considered by the statistical calculation (i.e. the sample size) to vary in order to detect a significant difference from a predefined threshold value .DELTA. within the required confidence interval.” [0112]
It would have been obvious to one of ordinary skill in the art before the effective filing data to include in the method and system of the combined references the ability to use a predetermined number of meals as taught by Blythe et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Blythe et al. who teaches convergence using a sample size of 20. Blythe teaches the known use of a predetermined number of samples and time associated with measurements and the benefits of using such information. The combined references benefit as they are also in the business of providing measurements with time and providing bolus insulin. Further it would be obvious that using a statistical power also results in a predetermined number of meals as the sample size is an important consideration for achieving a result with no or reduced false-positives.
The combined references teach both predetermined sample size of 20 and statistical power. They do not explicitly teach using the predetermined sample size. However, one of ordinary skill in the art would recognize that predetermined sample size was available for use as convergence is provided.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that various methods including predetermined sample size (number of meals) could be used for providing averages. This would have been known work in the field of endeavor prompting variations of it in the same field based on convergence considerations as a known method and sample size considerations to reduce false-positives and would provide predictable results.
Regarding claim 11
The non-transitory computer readable medium of claim 6, wherein the non-transitory computer readable medium is further embodied with programming code executable by a processor, and the processor when executing the programming code is operable to perform functions, including functions to:
output instructions to deliver the determined meal bolus, wherein the instructions are based on a mealtime for which the determined meal bolus is to be delivered.
Wei et al. teaches:
Recommendations (output instructions) for bolus amount for each meal…
“When used with an analyte monitoring system 100, these embodiments can capture, categorize, and index glucose responses to the meals and meal-time insulin doses (administered to compensate for the meal) and thus provide the user with additional data from which the user's insulin dosages can be perfected or “fine-tuned.” In addition, over time, the example embodiments can provide recommendations as to the titration of the bolus amount for each meal.” [0193]
Regarding claim 12
The non-transitory computer readable medium of claim 11, wherein the non-transitory computer readable medium is further embodied with programming code executable by a processor, and the processor when executing the programming code is operable to perform functions, including functions to:
in response to the outputted instruction, generate a prompt on a personal diabetes management device.
Wei et al. teaches:
Example of modification to bolos dose and prompt user to change treatment profile…
“An output issued at 320 can supply information directly to the user's medication or treatment program on the same device or a different device in communication with the device executing the meal monitor application. This information can be used by the treatment program in determining whether a modification to a user's treatment profile (e.g., a basal insulin delivery schedule or a bolus dose) is warranted or to prompt the user as to whether a change to the user's treatment profile should be implemented.” [0159]
Regarding claim 19
The device of claim 13, wherein the drug delivery device is further operable to:
output a signal indicating an amount of insulin delivered by the pump mechanism in response to the received, outputted instruction.
Wei et al. teaches:
Example of drug delivery device and feedback (output signal of amount of insulin delivered)….
“Drug delivery device 160 is capable of injecting or infusing a drug, such as but not limited to insulin, into the body of the individual wearing sensor control device 102. Like reader device 120, the drug delivery device can include processing circuitry, non-transitory memory containing instructions executable by the processing circuitry, wireless or wired communication circuitry, and a user interface including one or more of a display, touchscreen, keyboard, an input button or instrument, and the like. Drug delivery device 160 can include a drug reservoir, a pump, an infusion tube, and an infusion cannula configured for at least partial implantation into the user's body. The pump can deliver insulin from the reservoir, through the tube, and then through the cannula into the user's body. Drug delivery device 160 can include instructions, executable by the processor, to control the pump and the amount of insulin delivered. These instructions can also cause calculation of insulin delivery amounts and durations (e.g., a bolus infusion and/or a basal infusion profile) based on analyte level measurements obtained directly or indirectly from sensor control device 102. Alternatively, calculations of insulin delivery amounts and durations, and the control of the pump, can be performed by reader device 120 directly. The drug delivery device can be configured to communicate directly with reader device 120 in the form of a closed loop or semi-closed loop system. Alternatively, the drug delivery device can include the functionality of reader device 120 described herein, or vice versa, to arrive at one integrated reader and drug delivery device.” [0052] Inherent with closed loop is feedback to the reader device.
Regarding claim 20
The device of claim 13, further comprises:
a blood glucose sensor communicatively coupled to the processor wherein the blood glucose sensor is operable to:
Wei et al. teaches:
Fig. 1, ref. 102 (sensor) and 120 (reader with processor)…
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In vivo analyte (glucose) monitoring…
“Variants of devices 102 and 120, as well as other components of an in vivo-based analyte monitoring system that are suitable for use with the system, device, and method embodiments set forth herein, are described in U.S. Patent Application Publ. No. 2011/0213225 (the '225 Publication), which is incorporated by reference herein in its entirety for all purposes.” [0047]
measure a blood glucose value at a predetermined time interval; and
Sensors for measuring glucose in blood stream…
“A number of systems have been developed for the automatic monitoring of the analyte(s), like glucose, in bodily fluid such as in the blood stream, in interstitial fluid (“ISF”), dermal fluid of the dermal layer, or in other biological fluid. Some of these systems are configured so that at least a portion of a sensor is positioned below a skin surface of a user, e.g., in a blood vessel or in the subcutaneous tissue of a user, to obtain information about at least one analyte of the body.” [0038]
According to a schedule (predetermined time interval)…
“As such, these systems can be referred to as “in vivo” monitoring systems. In vivo analyte monitoring systems include “Continuous Analyte Monitoring” systems (or “Continuous Glucose Monitoring” systems) that can transmit data from a sensor control device to a reader device continuously without prompting, e.g., automatically according to a schedule. In vivo analyte monitoring systems also include “Flash Analyte Monitoring” systems (or “Flash Glucose Monitoring” systems or simply “Flash” systems) that can transfer data from a sensor control device in response to a scan or request for data by a reader device, such as with a Near Field Communication (NFC) or Radio Frequency Identification (RFID) protocol. In vivo analyte monitoring systems can also operate without the need for finger stick calibration.” [0039]
provide measured blood glucose values to the processor.
Transmit (provide) data (values) to a reader device (therefore to a processor)…
“As such, these systems can be referred to as “in vivo” monitoring systems. In vivo analyte monitoring systems include “Continuous Analyte Monitoring” systems (or “Continuous Glucose Monitoring” systems) that can transmit data from a sensor control device to a reader device continuously without prompting, e.g., automatically according to a schedule. In vivo analyte monitoring systems also include “Flash Analyte Monitoring” systems (or “Flash Glucose Monitoring” systems or simply “Flash” systems) that can transfer data from a sensor control device in response to a scan or request for data by a reader device, such as with a Near Field Communication (NFC) or Radio Frequency Identification (RFID) protocol. In vivo analyte monitoring systems can also operate without the need for finger stick calibration.” [0039]
Example of processes and displays…
“In vivo monitoring systems can also include a device that receives sensed analyte data from the sensor control device and processes and/or displays that sensed analyte data, in any number of forms, to the user. This device, and variations thereof, can be referred to as a “reader device” (or simply a “reader”), “handheld electronics” (or a handheld), a “portable data processing” device or unit, a “data receiver,” a “receiver” device or unit (or simply a receiver), or a “remote” device or unit, to name a few. Other devices such as personal computers have also been utilized with or incorporated into in vivo and in vitro monitoring systems.” [0042]
Regarding claim 21
The non-transitory computer readable medium of claim 11, wherein the non-transitory computer readable medium is further embodied with programming code executable by a processor, and the processor when executing the programming code is operable to perform functions, including functions to:
in response to the outputted instruction, cause a medical device to deliver the determined meal bolus.
Wei et al. teaches:
Example of drug delivery device….
“Drug delivery device 160 is capable of injecting or infusing a drug, such as but not limited to insulin, into the body of the individual wearing sensor control device 102. Like reader device 120, the drug delivery device can include processing circuitry, non-transitory memory containing instructions executable by the processing circuitry, wireless or wired communication circuitry, and a user interface including one or more of a display, touchscreen, keyboard, an input button or instrument, and the like. Drug delivery device 160 can include a drug reservoir, a pump, an infusion tube, and an infusion cannula configured for at least partial implantation into the user's body. The pump can deliver insulin from the reservoir, through the tube, and then through the cannula into the user's body. Drug delivery device 160 can include instructions, executable by the processor, to control the pump and the amount of insulin delivered. These instructions can also cause calculation of insulin delivery amounts and durations (e.g., a bolus infusion and/or a basal infusion profile) based on analyte level measurements obtained directly or indirectly from sensor control device 102. Alternatively, calculations of insulin delivery amounts and durations, and the control of the pump, can be performed by reader device 120 directly. The drug delivery device can be configured to communicate directly with reader device 120 in the form of a closed loop or semi-closed loop system. Alternatively, the drug delivery device can include the functionality of reader device 120 described herein, or vice versa, to arrive at one integrated reader and drug delivery device.” [0052]
Regarding claim 23
The device of claim 13, wherein the processor when executing the programming code is further operable to perform functions, including further functions to:
update a user interface with an estimated meal insulin need based on the meal categories, the meal categories categorized based on the generated dataset, or both.
Wei et al. teaches:
Categories of meals…
“Turning now to operation of the EBA on smart phone reader 120, FIGS. 10A-C depict example embodiments of user interface screens 1010, 1020, and 1030 displayable to the user via the smartphone interface. FIG. 10A depicts an example embodiment of a home screen 1000. Near the top of home screen 1000 are a series of selectable tabs 1002 corresponding to the various categories or types of meals and exercise. The active tab can be determined by the app based on the current time-of-day. For example, if the current time-of-day is between 2 am and 10 am, then the breakfast tab is active. If the patient desires a different tab then that tab can be selected, e.g., for a different meal that the patient is about to consume.” [0207]
“Then the user can then select the meal that the user is about to consume to initiate the process of determining the appropriate bolus, which in this example is “Milk and cheerios.” Selection of a meal can initiate the process of collecting or retrieving the user's recent glucose data. For example, if system 100 is configured as a flash or on demand system, then a notification can be displayed to the user via screen 1010 that prompts the user to scan sensor control device 102 to retrieve the current glucose data. The collection of that data can be managed through the sensor interface app 804 and provided to the EBA as described with respect to FIG. 8. Alternatively, the EBA can include the scan routines, interface with the reader's scan transmitter hardware and software, and perform the scan itself with or without the assistance of app 804.” [0209]
Recommended bolus insulin…
“An example embodiment of insulin logging screen 1020 is depicted in FIG. 10C. Here, a date and time stamp 1021 of the meal description (“Milk and cheerios”) is included at top. Below that is a first section 1022 of screen 1020 where the user's current glucose level and current trend arrow is shown followed by the recommended meal bolus insulin amount, which in this example is 2.0 units of rapid-acting insulin. This bolus insulin amount can be either the meal bolus amount that was originally entered with this meal during initial setup (or during the first time this meal is logged) or the last confirmed meal bolus amount injected or infused for this meal.” [0211]
Example of update based on user edits…
“The sum total of these components results in the “Total Dose” recommended by the EBA. If the user edits any one of these three component fields then the total dose amounts will automatically update to reflect the user edit. In some embodiments, the original EBA recommendation will remain displayed in section 1022, while in other embodiments the dose recommendation in section 1022 will update to reflect any user changes within section 1026.” [0217]
Regarding claim 24
The device of claim 13, wherein the determined meal bolus may be calculated or received via a user interface coupled to the processor.
Wei et al. teaches:
Computer system for processing data…
“The processing of data and the execution of software within system 100 can be performed by one or more processors of reader device 120, computer system 170, and/or sensor control device 102. For example, raw data measured by sensor 104 can be algorithmically processed into a value that represents the analyte level and that is readily suitable for display to the user, and this can occur in sensor control device 102, reader device 120, or computer system 170. This and any other information derived from the raw data can be displayed in any of the manners described above (with respect to display 122) on any display residing on any of sensor control device 102, reader device 120, or computer system 170. The information may be utilized by the user to determine any necessary corrective actions to ensure the analyte level remains within an acceptable and/or clinically safe range.” [0056]
Regarding claim 25
The non-transitory computer readable medium of claim 6, wherein the determined meal bolus is calculated by the processor or received via a user interface.
[No Patentable Weight is given to contingent or alternative language of “received via a user interface. Only one limitation is required.]
Wei et al. teaches:
Computer system for processing data…
“The processing of data and the execution of software within system 100 can be performed by one or more processors of reader device 120, computer system 170, and/or sensor control device 102. For example, raw data measured by sensor 104 can be algorithmically processed into a value that represents the analyte level and that is readily suitable for display to the user, and this can occur in sensor control device 102, reader device 120, or computer system 170. This and any other information derived from the raw data can be displayed in any of the manners described above (with respect to display 122) on any display residing on any of sensor control device 102, reader device 120, or computer system 170. The information may be utilized by the user to determine any necessary corrective actions to ensure the analyte level remains within an acceptable and/or clinically safe range.” [0056]
Claims 10, 17, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (9) above in further view of Pub. No. US 2008/0172026 to Blomquist.
Regarding claims 10 and 17
(claim 10) The non-transitory computer readable medium of claim 6, wherein the non-transitory computer readable medium is further embodied with programming code executable by a processor, and the processor when executing the programming code is operable to perform further functions, including, when determining the meal bolus to be delivered functions to:
Wei et al. teaches:
Estimate (determining) insulin bolus for a given meal…
“As such, it is feasible to empirically observe a patient's average analytic (e.g., glucose) response to a given meal. The empirically observed glucose response may be used to estimate insulin bolus for a given meal. In addition, the patient's empirically determined glucose response to a given meal may also be used to augment their diabetes management by steering them away from certain meals and towards others which permit better glucose management.” [0187]
estimate a user’s total daily insulin needs based on a basal insulin profile;
Wei et al. teaches:
Calculation (estimate) of insulin (total insulin) including basal infusion profile…
“Drug delivery device 160 is capable of injecting or infusing a drug, such as but not limited to insulin, into the body of the individual wearing sensor control device 102. Like reader device 120, the drug delivery device can include processing circuitry, non-transitory memory containing instructions executable by the processing circuitry, wireless or wired communication circuitry, and a user interface including one or more of a display, touchscreen, keyboard, an input button or instrument, and the like. Drug delivery device 160 can include a drug reservoir, a pump, an infusion tube, and an infusion cannula configured for at least partial implantation into the user's body. The pump can deliver insulin from the reservoir, through the tube, and then through the cannula into the user's body. Drug delivery device 160 can include instructions, executable by the processor, to control the pump and the amount of insulin delivered. These instructions can also cause calculation of insulin delivery amounts and durations (e.g., a bolus infusion and/or a basal infusion profile) based on analyte level measurements obtained directly or indirectly from sensor control device 102. Alternatively, calculations of insulin delivery amounts and durations, and the control of the pump, can be performed by reader device 120 directly. The drug delivery device can be configured to communicate directly with reader device 120 in the form of a closed loop or semi-closed loop system. Alternatively, the drug delivery device can include the functionality of reader device 120 described herein, or vice versa, to arrive at one integrated reader and drug delivery device.” [0052]
Analyte data (insulin needs) based on days…
“In this embodiment of method 330, the meal monitor application runs the meal event detector on the analyte data from a prior time range (e.g., N days, hours, minutes) using a first set of sensitivity settings (e.g., analyte data magnitude thresholds, duration thresholds, rate of change thresholds, or others) at 332. Here, method 330 examines the analyte data over the previous N days, where N can be any desired value. Next, a determination is made at 334 whether the number of meal events detected over those N days is less than or equal to a maximum, which in this embodiment is (the baseline value added to the variation value) multiplied by N. In an example where N is three, the baseline value is three meals/day, and the variation value is one meal/day, then the determination at 334 would evaluate whether the number of meal events detected is less than or equal to twelve (e.g., the maximum).” [0098]
Where measurement can be done per day…
“Method 330 can be repeated at regular intervals. In one example embodiment, method 330 is performed once per day using data from the prior N days as measured from midnight to midnight to prevent the settings from changing multiple times per day.
“Method 330 can be repeated at regular intervals. In one example embodiment, method 330 is performed once per day using data from the prior N days as measured from midnight to midnight to prevent the settings from changing multiple times per day.” [0102]
“High glucose levels are primarily driven by the consumption of food. The level of post-prandial glucose is related to the amount of carbohydrates and other meal components consumed by the individual, as well as to the individual's physiological response to meals. Each individual's glycemic response can now be tracked and better understood by in vivo analyte (e.g., glucose) monitoring devices. However, a challenge for analysis of this influx of data is to represent the data in a meaningful manner that enables efficient action.” [0005]
calculate an initial estimate of the user’s average insulin need for each meal for a user;
Average dose for this (each) meal…
“As with the meal monitor application, the EBA can include the functionality to generate one or more reports for the user to assist in interpreting and understanding the collected data. FIG. 16 depicts an example embodiment of a meal history report 1600. Report 1600 provides an easy way to reference a summary of the collected data for a particular menu item. A first section 1602 of report 1600 can include a picture 1605 of the meal as a thumbnail (clicking on the thumbnail can cause a full size photo to be displayed), alongside an average dose 1606 for this meal (which in some embodiments can be weighted to count more recent doses more heavily than older doses), and an icon 1608 indicating the average post meal response in the past, where a checkmark indicates the post-prandial data is generally in a target range, a downward arrow indicates post-prandial data is generally beneath a target range, and an upward arrow indicates post-prandial data is generally above the target range. A variety of known methods for evaluating hypoglycemia risk can be used for this purpose.” [0229] Inherent with average dose is calculating the average dose.
receive information related to blood glucose measurement data of the user and insulin delivered to the user for a period of time after delivery of the meal bolus to the user;
Capture (receive) glucose responses (measurement data after meal) for insulin doses that compensate for the meal (therefore, meal bolus)…
“When used with an analyte monitoring system 100, these embodiments can capture, categorize, and index glucose responses to the meals and meal-time insulin doses (administered to compensate for the meal) and thus provide the user with additional data from which the user's insulin dosages can be perfected or “fine-tuned.” In addition, over time, the example embodiments can provide recommendations as to the titration of the bolus amount for each meal.” [0193]
calculate a proportion of a sum of a post prandial insulin delivery patterns versus a user’s total daily insulin;
Insulin doses administered (calculate) compensate for the meal (portion of a sum of insulin)…
“When used with an analyte monitoring system 100, these embodiments can capture, categorize, and index glucose responses to the meals and meal-time insulin doses (administered to compensate for the meal) and thus provide the user with additional data from which the user's insulin dosages can be perfected or “fine-tuned.” In addition, over time, the example embodiments can provide recommendations as to the titration of the bolus amount for each meal.” [0193]
See Insulin Below
generate a dataset of average insulin needs per meal using the calculated proportion;
Empirically observe (generate) average response (therefore, insulin need) per meal…
“As such, it is feasible to empirically observe a patient's average analytic (e.g., glucose) response to a given meal. The empirically observed glucose response may be used to estimate insulin bolus for a given meal. In addition, the patient's empirically determined glucose response to a given meal may also be used to augment their diabetes management by steering them away from certain meals and towards others which permit better glucose management.” [0187]
Average dose for this (each) meal…
“As with the meal monitor application, the EBA can include the functionality to generate one or more reports for the user to assist in interpreting and understanding the collected data. FIG. 16 depicts an example embodiment of a meal history report 1600. Report 1600 provides an easy way to reference a summary of the collected data for a particular menu item. A first section 1602 of report 1600 can include a picture 1605 of the meal as a thumbnail (clicking on the thumbnail can cause a full size photo to be displayed), alongside an average dose 1606 for this meal (which in some embodiments can be weighted to count more recent doses more heavily than older doses), and an icon 1608 indicating the average post meal response in the past, where a checkmark indicates the post-prandial data is generally in a target range, a downward arrow indicates post-prandial data is generally beneath a target range, and an upward arrow indicates post-prandial data is generally above the target range. A variety of known methods for evaluating hypoglycemia risk can be used for this purpose.” [0229] ] Inherent with average dose is calculating the average dose.
See Insulin Below
categorize the generated dataset into meal categories; and
Categorize responses to the meals (therefore meal categories)…
“When used with an analyte monitoring system 100, these embodiments can capture, categorize, and index glucose responses to the meals and meal-time insulin doses (administered to compensate for the meal) and thus provide the user with additional data from which the user's insulin dosages can be perfected or “fine-tuned.” In addition, over time, the example embodiments can provide recommendations as to the titration of the bolus amount for each meal.” [0193]
Another example of categorize meals…
“In other embodiments, the EBA can be configured for use by non-diabetic (ND) and/or non-insulin using patients (NIUP). In these embodiments, the EBA can provide meal glucose response data in terms of “Glucose Loading” and meals could be ranked by their respective glucose loading and displayed accordingly in the meal history screen 1600. In these embodiments the home screen 1000 can be configured to show the cumulative daily total glucose loading to advise the user how much glucose loading allowance is left based on a preset restriction or baseline, for any given moment of a given day. ND and NIUP users could use these embodiments of the EBA as a diet management tool. When enough meals have been categorized, the EBA can provide meal recommendations for different “Glucose Loading” allowances based on different types of restrictions that the users may want to set (e.g., a per day restriction, a per meal restriction, or a per meal of the day (breakfast, lunch, or dinner) restriction).” [0236]
update estimated meal insulin needs for each respective meal category of the meal categories based on the categorizing of the generated dataset into the meal categories.
Example of adding (update) insulin for a meal category…
“As mentioned with respect to FIG. 10A, home screen 1000 can include a selectable field 1006 that allows the user to add a new meal or activity. FIG. 12A depicts an example embodiment of home screen 1000 where the breakfast category of meals has not yet been populated with a menu item. Selection of field 1006 causes the display of a menu item entry screen 1200 as depicted in FIG. 12B. Screen 1200 can include a first data entry field 1202 in which the name or description of the meal to be added can be provided. Screen 1200 can also include a second data entry field 1204 where the meal bolus amount can be entered. (In other embodiments, the type of medication or insulin can also be specified.) As described earlier, the determination this meal bolus amount can occur through collaboration of the user with his or her HCP or through the use of a bolus calculator.” [0219]
Insulin
The combined references teach insulin. They do not teach average insulin using proportion of total daily insulin.
Blomquist also in the business of insulin teaches:
Aggregate insulin (total daily insulin) and delivered insulin as a meal bolus (proportion of a sum) for carbohydrates consumed (post prandial insulin)…
“(1) The aggregate insulin delivered by the pump 100 as well as the amount of insulin broken down by insulin delivered as a meal bolus, insulin delivered to counteract estimated carbohydrates consumed by the user (if the carbohydrate estimator is used), delivered as a correction bolus, and delivered according to basal delivery protocols. In various embodiments, the pump 100 will record delivery according to basal delivery protocols as a total for all basal delivery protocols, or if the pump 100 is programmed with multiple delivery basal protocols, the delivered insulin can be broken down by each basal protocol used by the pump 100. In one possible embodiment, this data is stored as a daily total and an average daily total for a predetermined number of days. Additionally, in various embodiments, the average data can be recorded as actual average values or the average data can be calculated from the daily totals when requested for display or upon other requests.” [0057]
Average daily percentage, where insulin is used for carbohydrates (meals)…
“(2) The amount of insulin delivered by the pump 100 according to a basal protocol as a percent of the total insulin delivered by the pump 100. In one possible embodiment, this data is stored as a daily percentage and an average daily percentage for a predetermined number of days. Additionally, in various embodiments, the average data can be recorded as actual average values or the average data can be calculated from the daily totals when requested for display or upon other requests. (3) The date, time, and amount of each bolus delivered. (4) The 500-Rule factor, which is used to estimate the grams of carbohydrates that are covered by each unit of insulin.” [0058] – [0060]
It would have been obvious to one of ordinary skill in the art at the time of filing to include in the method and system of the combined references the ability to use total insulin and proportion for an average as taught by Blomquist since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Blomquist who teaches the benefits of using aggregate and percentages for insulin meal amounts.
Regarding claim 22
The non-transitory computer readable medium of claim 10, wherein the non-transitory computer readable medium is further embodied with programming code executable by a processor, and the processor when executing the programming code is operable to perform functions, including functions to:
update a user interface and/or estimated meal insulin needs based on the meal categories.
[No Patentable Weight is given to contingent or alternative language of “update a user interface. Only one limitation is required.]
Wei et al. teaches:
Example of excursions and analyte violations (therefore meal insulin need)…
“Detection of a meal event can include detection of analyte episodes or excursions outside a desired acceptable (e.g., medically recommended) target range in the user, who can be informed by the software that one or both has been detected. Examples of analyte excursions include violation of a low glucose threshold, violation of a high glucose threshold, violation of a rate of change (e.g., increase or decrease) threshold, violation of a glucose median threshold, violation of a glucose variability threshold, and the like.” [0082]
“When used with an analyte monitoring system 100, these embodiments can capture, categorize, and index glucose responses to the meals and meal-time insulin doses (administered to compensate for the meal) and thus provide the user with additional data from which the user's insulin dosages can be perfected or “fine-tuned.” In addition, over time, the example embodiments can provide recommendations as to the titration of the bolus amount for each meal.” [0193]
Conclusion
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684
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