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 .
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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 15 April 2026 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, 6-10, and 14-15 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.
Regarding Claim 1, the limitation “GL related data” recited in line 31 of the claim is unclear if it is the same or different “GL related data” as recited in line 4 of the claim. It is being interpreted that the limitation “GL related data” recited in line 6 of the claim is the same “GL related data” recited in line 4 of the claim.
Regarding Claim 9, the limitation “GL related data” recited in line 31 of the claim is unclear if it is the same or different “GL related data” as recited in line 4 of the claim. It is being interpreted that the limitation “GL related data” recited in line 6 of the claim is the same “GL related data” recited in line 4 of the claim.
Claims not explicitly rejected above are rejected due to their dependence on the above claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 8-10, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Segman’606 (U.S. Patent Application 20200043606 – previously cited) as evidenced by NDIC'2008 (Continuous Glucose Monitoring – previously cited), in view of Mayou et. al.'871 (U.S. Patent Application 20130035871 – cited by applicant), further in view of Wynbrandt et. al.’859 (U.S. Patent 10321859 - cited by applicant), further in view of Rosman et. al.'682 (U.S. Patent Application 20070128682 – previously cited), further in view of Masciotti et. al.'314 (U.S. Patent Publication - 20190142314), and further in view of Duke et. al.'980 (U.S. Patent Publication 20170251980).
Regarding Claims 1 and 9, Segman’606 discloses a diabetes analysis system for analysis and interpretation of data related to glucose level (GL) in blood, the system is to be applied to determine a treatment recommendation to a patient (Paragraph [0072] - The system and method involve recording in a memory of the device, momentary glucose levels of the patient at multiple times of each day of an initial time period…The recording may include optional information associated with the recent temporal glucose reading including one or more of the following information: date, time recent food eating, recent physical activity, diabetes treatment, body temperature, blood pressure, pulse, drugs including specialized drugs, associated with a recent temporal glucose reading), the analysis system comprises:
a measurement device configured to determine GL related data from measurements of interstitial fluid in subcutaneous tissue (Paragraph [0070] - The use of continuous glucose monitors, which have been shown to improve outcomes, is an appealing alternative; Paragraph [0072] - The system and method utilize an invasive or noninvasive glucose measuring device that measures continuously or at discrete times or intervals (uniform or non-uniform)). A continuous glucose monitoring device obtains samples from the interstitial fluid by being placed invasively and subcutaneously as evidenced by NDIC'2008 (Continuous Glucose Monitoring) (Page 3 Column 2 Bullet Point 3 - Continuous glucose monitoring (CGM) systems use a tiny sensor inserted under the skin to check glucose levels in tissue fluid);
an input module configured to receive GL related data from measurements of interstitial fluid in subcutaneous tissue and prepare the GL related data (Paragraph [0073] - Using the one or more processors, in some embodiments a quantitative assessment of at least one of (or at least two of or at least three of) the following current momentary biomarkers is determined during the measurement period from the momentary glucose levels recorded; Paragraph [0077] - An initial stage of measuring blood or tissue glucose levels), wherein the system further comprises:
a hypoglycemia classification module configured to identify an underlying cause of hypoglycemia (Paragraph [0075] - Due to the complexity of diabetes, it is beneficial to diagnose the different stages and subtypes of the disease, together with specific treatments that addresses the individual metabolic profiles and disease stages; Paragraph [0088] - Beyond their metabolic variability, different patients have different daily routines, eating habits, physical activity and medication schedules. The glucose histogram, as well as other visualizations of glycemic behavior, can help us understand how the glucose levels behave during the day to day. This allows us to focus on the most problematic times of the day (such as times during hypoglycemic events) and target them specifically with behavioral and pharmacological interventions; Paragraph [0097] - The recording, in some embodiments, includes optional information associated (probably causes) with the recent temporal glucose reading (such as a hypoglycemic event). The optional information includes one or more of the following information: date, time, time of recent food eating, physical activity, diabetes treatment, body temperature, blood pressure, pulse, and drugs in general or drugs (including specialized drugs) associated with the recent temporal glucose reading; Paragraph [131] - In other embodiments, method 100 comprises outputting at least one of (i) a type of diabetes, an existence of hyperglycemia, an existence of hypoglycemia, and a degree of severity of the diabetes, (ii) a tentative diagnostic suggestion and (iii) a tentative treatment suggestion); and
a cluster identification module configured to receive GL values and arrange them as numbers of GL samples in a time series that exist in specific GL intervals, denoted bins, to determine a GL histogram profile (Paragraph [0088] - The large dispersion of the measures is also noteworthy, given that glycemic variability has been shown to be an independent risk factor for diabetes complications and a target for diabetes care. FIG. 6C shows a density plot of the same dataset, binned into 15 minute slots, in what some may find is a more visually intuitive way. This plot focuses us into four clear clusters of glucose level… FIG. 6D shows a histogram with the different times of day color coded, allowing us to get a rough sense of the dispersion of values during the day; Paragraph [0098] - FIG. 6C shows the same data as 6A, with time binned into 15 minute slots and the glucose levels binned into 8 steps; Paragraph [0106] - According to one non-limiting example of how glucose variability is defined, the one or more processors determine the average rate of change of the glucose level during the measurement period and for example this may be determined by taking the total change in momentary glucose levels over the total time period).
Segman’606 fails to disclose an input module configured to prepare the GL related data by at least removing numerical outliers. Mayou et. al.'871 teaches preparing GL related data by removing outliers (Paragraph [0022] - an analyte value filter that filters out analyte data points that have an analyte concentration value falling outside of a predetermined analyte level or range of analyte values; Paragraph [0100] - The terms "smoothed data" and "filtered data" as used herein are broad terms and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to data that has been modified to make it smoother and more continuous and/or to remove or diminish outlying points). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Segman’606 as evidenced by NDIC'2008 (Continuous Glucose Monitoring) to include filtering out data that constitute as numerical outliers in order to create smoother and more continuous data as seen in Mayou et. al.’871.
Segman’606 further fails to disclose wherein said system further comprises: a hypoglycemia identification module configured to identify hypoglycemic events by performing a computer-implemented automatic search of said received GL related data, wherein all uninterrupted glucose levels less than a predetermined level in a same time series will be considered as one hypoglycemic event. Wynbrandt et. al.’859 teaches a hypoglycemia identification module configured to identify hypoglycemic events by performing a computer-implemented automatic search of said received GL related data, wherein all uninterrupted glucose levels less than a predetermined level, in a same time series, will be considered as one hypoglycemic event (Column 21 Lines 22-26 - In some implementations, a plurality of blood glucose readings that are below the predefined hypoglycemic event threshold comprise (608) a single hypoglycemic event when the timestamps of the blood glucose readings are within a two hour time period). It would be obvious to one of ordinary skill in the art to have modified Segman’606 as evidenced by NDIC'2008 (Continuous Glucose Monitoring) in view of Mayou et. al.’871 to include a hypoglycemia identification module that consider all glucose levels within a predetermined time series as one hypoglycemic event instead of separate to better understand and identify hypoglycemic patterns for a user as seen in Wynbrandt et. al.’859 (Column 22 Lines 28-30 - further analyze (642) the hypoglycemic events and the obtained feedback to identify one or more patterns of hypoglycemic events).
Segman’606 further fails to disclose a hypoglycemia classification module configured to analyze, for each identified hypoglycemic event, the GL related data during a predetermined first time period preceding the hypoglycemic event, to determine a glucose level during the first time period. Wynbrandt et. al.’859 teaches a hypoglycemia classification module configured to analyze, for each identified hypoglycemic event, the glucose level related data during a predetermined first time period, preceding the hypoglycemic event, to determine the glucose level during a first time period (Column 19 Lines 28-31 - The hypo day report 575 displays contextual information from each hypoglycemic event, plus the corresponding data for 12 hours before and after the episode in some implementations; Column 22 Lines 18-22 - In some implementations, the provided report includes (638) a graphical presentation of information regarding blood glucose levels before and after one or more hypoglycemic events). It would have been obvious to one of ordinary skill in the art to have modified the time period in which glucose levels were monitored before and after meals as seen in Segman’606 (Paragraph [0072]) as evidenced by NDIC'2008 (Continuous Glucose Monitoring) in view of Mayou et. al.’871 to include a hypoglycemia classification module that analyzes glucose data during a certain time period based on the occurrence of the hypoglycemic event to better understand hypoglycemic-related incidents as seen in Wynbrandt et. al.’859.
Although Segman’606 discloses identifying glucose levels during measurement periods (Paragraph [0109] - The glucose burden captures the average glucose level during the measurement period) and identifying hypoglycemic events (Paragraph [0118] - a curvilinear probability density function generated from a histogram… represents an actual labile type 1 diabetic, who suffers from several episodes of both hypo- and hyper-glycemia; Paragraph [131] - In other embodiments, method 100 comprises outputting at least one of (i) a type of diabetes, an existence of hyperglycemia, an existence of hypoglycemia, and a degree of severity of the diabetes, (ii) a tentative diagnostic suggestion and (iii) a tentative treatment suggestion), Segman’606 further fails to disclose wherein the hypoglycemia classification module is configured to determine a type of hypoglycemia event, based upon the glucose level during a first time period, by applying a computer-implemented pattern search procedure on a predetermined hypoglycemic classification scheme including different types of hypoglycemia. Mayou et. al.'871 teaches identifying hypoglycemic patterns and assigning those patterns with different levels (or different types) of severity (Paragraph [0184] - Patterns that may be detected include, but are not limited to, a hyperglycemic pattern, hypoglycemic pattern, patterns associated with a time of day or week, a weighted scoring for different patterns based on frequency, sequence, and severity. Patterns may also be based on a custom sensitivity of a user, a transition from a hypoglycemic to hyperglycemic pattern, an amount of time spent in a severe event, and a combination of glucose change and time information; Paragraph [0235] - A predetermined number of the highest priority pattern sets for display to the user may be selected; Paragraph [0236] - At block 608, the selected patterns are outputted for display or further processing. For example, the patterns may be displayed on a user interface, such as one of the user interfaces described in FIGS. 9-11. In some implementations, alerts can be triggered based on the selected patterns instead of or in addition to displaying the selected patterns. And in some implementations, the selected patterns are processed further to modify some other process, such as a medication administration routine (e.g., insulin administration routine) and the like). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the histograms within the system of Segman’606 as evidenced by NDIC'2008 (Continuous Glucose Monitoring) to include assigning hypoglycemic events with various levels or types of severity in order to alert a user of more severe patterns and monitor how a user’s body responds to treatment for each type of hypoglycemic event as a way to better understand and combat future hypoglycemic events as seen in Mayou et. al.’871.
Segman’606 further fails to disclose the hypoglycemia classification module is further configured to provide, based on the underlying cause of hypoglycemia, a treatment recommendation to a patient for glucose control. Wynbrandt et. al.’859 teaches a hypoglycemia classification module is further configured to provide, based on the underlying cause of hypoglycemia, a treatment recommendation to a patient for glucose control (Paragraph [0023] - In some implementations, the information requested includes one or more of: symptoms associated with the respective hypoglycemic event, perceived causes of the respective hypoglycemic event, and treatment for the respective hypoglycemic event). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Segman’606 as evidenced by NDIC'2008 (Continuous Glucose Monitoring) in view of Mayou et. al.’871 to include recommending a treatment based on a provided cause of a hypoglycemic event in order to recommend a treatment that is more specific to the type of hypoglycemic event that a user is experiencing as seen in Wynbrandt et. al.’859.
Segman’606 further fails to disclose a hypoglycemia recoil classification module configured to analyze, for each identified hypoglycemic event, the GL related data during a predetermined second time period following the hypoglycemic event, to determine a glucose level during the second time period, wherein the hypoglycemia recoil classification module is configured to determine a type of hypoglycemia recoil, based upon the glucose level during the second time period, by applying a computer-implemented pattern search procedure on a predetermined hypoglycemic recoil classification scheme including different types of hypoglycemia recoil. Rosman et. al.'682 teaches identifying glycemic rebound events following a hypoglycemic episode using modeling (Paragraph [0057] – entire paragraph - identify hypoglycemic episodes by time domains to identify likely antecedent life events such as exercise and meals, as well as the relationship to periodic life events such as sleep. Factors to be identified include duration of hypoglycemic episodes and any subsequent hyperglycemic rebounds; Paragraph [0058] – entire paragraph - To quantify glycemic episodes associated with specific life events, mathematical modeling of glucose levels is performed). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Segman’606 as evidenced by NDIC'2008 (Continuous Glucose Monitoring), in view of Mayou et. al.’871, and further in view of Wynbrandt et. al.’859 to include monitoring glucose levels after a hypoglycemic event in order to monitor and/or classify a rebound event as a way of understanding a user’s glucose patterns so that future events can be avoided and/or the user can better learn how to treat glycemic events as seen in Rosman et. al.’682.
Segman’606 further fails to disclose a cluster identification module configured to receive GL values and arranged them as the numbers of GL samples in a time series that exist in specific GL intervals, denoted bins, to determine a GL histogram profile indicating a duration of time that a patient spends in said specific GL intervals during a predetermined time period and configured to determine a rate of change of glucose level (dGL) histogram profile of a patient during said predetermined time period. Masciotti et. al.’314 teaches obtaining total durations of subjects experiencing GL and arranging them as numbers of readings that occur at those GL intervals (Paragraph [0039] - calculating the cumulative amount of time that the measured glucose of the individual was within the glucose sub-range based on (i) the aggregate number of glucose measurements within that sub-range and (ii) the periodic time interval between glucose measurements (e.g., 1, 2, 5, 10, 15 or 20 minutes). In these embodiments, the value indicative of the cumulative amount of time that the measured glucose of the individual was within the sub-range may be a calculation of the cumulative amount of time within the sub-range (e.g., the aggregate number of glucose measurements within that sub-range multiplied by the periodic time interval between glucose measurements)). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Segman’606 as evidenced by NDIC'2008 (Continuous Glucose Monitoring), in view of Mayou et. al.’871, further in view of Wynbrandt et. al.’859, and further in view of Rosman et. al.’682 to include GL histogram profiles indicative of durations of time a patient spends in specific GL intervals in order to understand a degree of fluctuation of a user’s glucose measurements and establish glucose ranges indicative of a user’s individual, typical response as seen in Masciotti et. al.’314 (Paragraph [0065] – entire paragraph - indicates the amount of time an individual has spent at each of the plurality of glucose ranges…provide the individual an additional indication of the degree of fluctuation of his or her glucose measurements).
Segman’606 further fails to disclose wherein said cluster identification module is configured to apply a computer-implemented procedure to compare said GL and dGL histogram profiles to sets of a predetermined number of different GL type profiles (A-H) and dGL type profiles (a- h), respectively, to determine which GL type profile and dGL type profile that essentially corresponds to the determined GL histogram profile and dGL histogram profile, respectively, and to combine said thus determined GL and dGL type profiles to a classification (A-H, a-h) and insert the classification in a classification space, wherein each position in said classification has designated treatment schemes. Duke et. al.'980 teaches diagnosing glycemic events based on clustering glucose data and rate of change of glucose data that correspond glucose measurements to proper databases (Column 10 Lines 52-67 - The clustering algorithm 200, as discussed in greater details hereafter, is based on both the collected unsupervised glucose data as well as a rate of change of the collected unsupervised glucose data…Therefore, there is a need for an efficient and automated way of analyzing the CGM dataset for better analysis and optimizing therapy. The clustering algorithm 200 works in a hierarchical manner and helps physician identify underlying patterns in the dataset and their similarity to other members (days) in the dataset). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Segman’606 as evidenced by NDIC'2008 (Continuous Glucose Monitoring), in view of Mayou et. al.’871, further in view of Wynbrandt et. al.’859, further in view of Rosman et. al.’682, and further in view of Masciotti et. al.’314 to include dynamic time warping that accommodates for temporal shifts in glucose measurements through creating penalized values based on clusters between glucose measurements and rate of change of glucose in order to more accurately correlate glucose measurements to similar matrices stored in a database as profiles and reduce medical error via presenting the same cluster upon repetition as seen in Duke et. al.’980 (Paragraph [0042] - The CGM profile (or insulin profile) may also include any relevant data tags or multi-analyte data, and the generated and/or received CGM profile (or insulin profile) may be stored in the database 130 and/or memory 135 until needed by the processor 120 for an unsupervised daily monitoring profile clustering process discussed hereafter in a later section; Paragraph [0056] - If failing to account for the varying temporal response 1000, e.g., along sections 1002 and 1004, a similar looking CGM trace would be penalized by the distance metric described above which simply uses difference in glucose values and instantaneous rate of change information, as depicted by FIG. 10B. It is therefore beneficial to further modify the above distance metric of equation (4) in the algorithm 200 during process 204 to accommodate for temporal shifts in the CGM profile traces via dynamic time warping; Paragraph [0057] - the goals of dynamic time warping 1100 is to find a best alignment between time-series A and time-series B; Paragraph [0058] - It is to be appreciated that during the aligning process the dynamic warping function temporally compresses or elongates curves locally, which for better result, the inventors have the algorithm 200 add a penalty to the total distance between the two curves of the time series; Paragraph [0059] - output glucose measurement data from the similarity matrix process 204; Paragraph [0060] - Due to its deterministic nature hierarchical clustering yields consistent labeling, i.e. cluster members do not migrate from one cluster to another on repeated runs…If the clustering algorithm 200 was not deterministic in nature, the HCP and the patient could potentially end up looking at different cluster members within the same labeled cluster which would result in confusion and potentially induce medical error (in correct therapy)).
Regarding Claims 2 and 10, Segman’606 as evidenced by NDIC'2008 (Continuous Glucose Monitoring), in view of Mayou et. al.'871, further in view of Wynbrandt et. al.’859, further in view of Rosman et. al.'682, further in view of Masciotti et. al.'314, and further in view of Duke et. al.'980 discloses the system outlined in Claim 1 above, but fails to disclose wherein the hypoglycemia identification module is further configured to determine a duration of the hypoglycemic event, based upon time series of data of an identified hypoglycemic event and a severity of the event which is based upon a lowest recorded glucose value. Masciotti et. al.’314 teaches obtaining total durations of subjects experiencing hypoglycemic events (Paragraph [0037] - in some non-limiting embodiments, an expected range may be divided into three sub-ranges. The three sub-ranges may correspond to a hypoglycemic sub-range (e.g., less than 70 mg/dL or less than 80 mg/dL); Paragraph [0039] - calculating the cumulative amount of time that the measured glucose of the individual was within the glucose sub-range based on (i) the aggregate number of glucose measurements within that sub-range and (ii) the periodic time interval between glucose measurements (e.g., 1, 2, 5, 10, 15 or 20 minutes). In these embodiments, the value indicative of the cumulative amount of time that the measured glucose of the individual was within the sub-range may be a calculation of the cumulative amount of time within the sub-range (e.g., the aggregate number of glucose measurements within that sub-range multiplied by the periodic time interval between glucose measurements)). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Segman’606 as evidenced by NDIC'2008 (Continuous Glucose Monitoring), in view of Mayou et. al.’871, further in view of Wynbrandt et. al.’859, and further in view of Rosman et. al.’682 to include GL histogram profiles indicative of durations of time a patient spends in specific GL intervals in order to understand a degree of fluctuation of a user’s glucose measurements as seen in Masciotti et. al.’314 (Paragraph [0065] – entire paragraph - indicates the amount of time an individual has spent at each of the plurality of glucose ranges…provide the individual an additional indication of the degree of fluctuation of his or her glucose measurements).
Furthermore, Wynbrandt et. al.’859 teaches a severity of the event which is based upon the lowest recorded glucose value (Column 12 Lines 19-22 - In some implementations, the hypo events are displayed in order based on the severity of the hypoglycemic event, from lowest blood glucose level to highest). It would be obvious to one of ordinary skill in the art to have modified using the shape of the curvilinear probability density function of Segman’606 (Paragraphs [0108] and [0125]) as evidenced by NDIC'2008 (Continuous Glucose Monitoring), in view of Mayou et. al.'871, further in view of Wynbrandt et. al.’859, further in view of Rosman et. al.'682, further in view of Masciotti et. al.'314, and further in view of Duke et. al.'980 to include identifying a severity event based on the lowest level of glucose values in order to better prioritize certain readings as seen in Wynbrandt et. al.’859 (Column 12 Lines 6-28).
Regarding Claims 8 and 15, Segman’606 as evidenced by NDIC'2008 (Continuous Glucose Monitoring), in view of Mayou et. al.'871, further in view of Wynbrandt et. al.’859, further in view of Rosman et. al.'682, further in view of Masciotti et. al.'314, and further in view of Duke et. al.'980 discloses the system outlined in Claim 1 above. Segman’606 further discloses the diabetes analysis system according to claim 1, wherein said GL related data comprises data from continuous glucose measurements (CGM) and/or flash glucose measurements (FGM) (Paragraph [0070] - use of continuous glucose monitors, which have been shown to improve outcomes, is an appealing alternative; Paragraph [0072] - The system and method utilize an invasive or noninvasive glucose measuring device that measures continuously or at discrete times or intervals).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Segman’606 (U.S. Patent Application 20200043606 – previously cited) as evidenced by NDIC'2008 (Continuous Glucose Monitoring – previously cited), in view of Mayou et. al.'871 (U.S. Patent Application 20130035871 – cited by applicant), further in view of Wynbrandt et. al.’859 (U.S. Patent 10321859 - cited by applicant), further in view of Rosman et. al.'682 (U.S. Patent Application 20070128682 – previously cited), further in view of Masciotti et. al.'314 (U.S. Patent Publication - 20190142314), further in view of Duke et. al.'980 (U.S. Patent Publication 20170251980), as applied to Claim 1, and further in view of Davis et. al.’751 (U.S. Patent Application 20170220751 – previously cited).
Regarding Claim 6, Segman’606 as evidenced by NDIC'2008 (Continuous Glucose Monitoring), in view of Mayou et. al.'871, further in view of Wynbrandt et. al.’859, further in view of Rosman et. al.'682, further in view of Masciotti et. al.'314, and further in view of Duke et. al.'980 discloses the system outlined in Claim 1 above. Segman’606 fails to disclose comprising a prandial event module comprising a prandial event filter configured to detect meals for patient without knowledge on bolus insulin and carbohydrate registrations, wherein the prandial event filter is adapted determine a prandial event by identifying predefined curve shapes of said GL related data, and to classify said identified prandial events by applying a prandial classification based on pattern recognition, and wherein a basal insulin pressure is identified based upon said classified prandial event. Davis et. al.’751 teaches comprising a prandial event module comprising a prandial event filter configured to detect meals for patient wherein the prandial event filter is adapted determine a prandial event by identifying predefined curve shapes of said GL related data, and to classify said identified prandial events by applying a prandial classification based on pattern recognition (Paragraph [0222] - Derived data may also include data determined about recognized patterns, where the patterns are recognized by analysis of historical data over time. Exemplary recognized patterns include patterns of nightly hypoglycemia, postprandial highs, post exercise lows, or the like, and wherein a basal insulin pressure is identified based upon said classified prandial event (Paragraph [0215] - Typical and atypical ranges of insulin sensitivity can also be determined. Insulin sensitivity can be measured by, e.g., measuring a basal rate at the same time each day, and seeing how glucose values change with respect to various events including meals, exercise, stress, or the like, and further by performing the same test with various changes in basal rate). It would have been obvious to one of ordinary skill in the art to have modified taking glucose levels pre and post meals as done in Segman’606 (Paragraph [0072]) as evidenced by NDIC'2008 (Continuous Glucose Monitoring), in view of Mayou et. al.'871, further in view of Wynbrandt et. al.’859, further in view of Rosman et. al.'682, further in view of Masciotti et. al.'314, and further in view of Duke et. al.'980 to include detecting and identifying prandial events as seen in Davis et. al.’751 to get a better understanding of treatment plans and doses of insulin (Paragraph [0305]).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Segman’606 (U.S. Patent Application 20200043606 – previously cited), as evidenced by NDIC'2008 (Continuous Glucose Monitoring – previously cited), in view of Mayou et. al.'871 (U.S. Patent Application 20130035871 – cited by applicant), further in view of Wynbrandt et. al.’859 (U.S. Patent 10321859 - cited by applicant), further in view of Rosman et. al.'682 (U.S. Patent Application 20070128682 – previously cited), further in view of Masciotti et. al.'314 (U.S. Patent Publication - 20190142314), further in view of Duke et. al.'980 (U.S. Patent Publication 20170251980), as applied to Claim 1, and further in view of and further in view of Kovatchev et. al.’892 (U.S. Patent Application 20050214892 – previously cited).
Regarding Claims 7 and 14, Segman’606 as evidenced by NDIC'2008 (Continuous Glucose Monitoring), in view of Mayou et. al.'871, further in view of Wynbrandt et. al.’859, further in view of Rosman et. al.'682, further in view of Masciotti et. al.'314, and further in view of Duke et. al.'980 discloses the system outlined in Claim 1 above. Segman’606 fails to disclose comprising an estimate hemoglobin (HbA1c) module, wherein said input module is configured to receive HbA1c related data and said estimate HbA1c module is configured to determine estimated HbA1c data based upon said HbA1c related data, and to apply said estimated HbA1c data to a diabetes analysis module that is configured to combine said determined estimated HbA1c data to at least one of the determined type of hypoglycemia event, type of hypoglycemia recoil, and determined GL and dGL type profiles. Kovatchev et. al.’892 teaches comprising an estimate hemoglobin (HbA1c) module, wherein said input module is configured to receive HbA1c related data and said estimate HbA1c module is configured to determine estimated HbA1c data based upon said HbA1c related data, and to apply said estimated HbA1c data to a diabetes analysis module that is configured to combine said determined estimated HbA1c data to at least one of the determined type of hypoglycemia event, type of hypoglycemia recoil, and determined GL and dGL type profiles (Paragraph [0116] - the invention enhances existing home BG monitoring devices by producing and displaying: 1) estimated categories for HbA1c, 2) estimated probability for SH in the subsequent six months, and 3) estimated short-term risk of hypoglycemia (i.e. for the next 24 hours). The latter may include warnings, such as an alarm, that indicates imminent hypoglycemic episodes). It would have been obvious to one of ordinary skill in the art to have modified comparing known levels of HbA1c to histogram profiles as seen in Segman’606 (Paragraph [0085]) as evidenced by NDIC'2008 (Continuous Glucose Monitoring), in view of Mayou et. al.'871, further in view of Wynbrandt et. al.’859, further in view of Rosman et. al.'682, further in view of Masciotti et. al.'314, and further in view of Duke et. al.'980 to include estimating hemoglobin (HbA1c) and combine it to the already existing profiles as seen in Kovatchev et. al.’892 to enhance the understanding of a person’s glucose levels (Paragraph [0116] - These three components can also be integrated to provide continuous information about the glycemic control of individuals with diabetes, and to enhance the monitoring of their risk of hypoglycemia).
Response to Arguments
Applicant's arguments filed 15 April 2026 have been fully considered and they are not entirely persuasive.
Applicant’s amendments have overcome the prior claim rejections under 35 U.S.C. 112b, but an additional 112b rejection has been addressed in Paragraph 4 above.
Claims 1-2, 6-10, and 14-15 are rejected under 35 U.S.C. 103 with additional references cited as necessitated by amendments, as discussed in Paragraphs 5-7 above. The examiner has noted that they have considered the applicant’s remarks pertaining to the amendments overcoming the previous prior art of record and has found that the reasons provided by the applicant would overcome the previous prior art of record. However, the examiner has provided additional references that do not put this invention in condition for allowance. The examiner believes that the teachings of Masciotti et. al.’314, pertaining to involving GL histogram profiles with numbers indicative of the amount of GL measurements that occur for specific durations of time corresponding to the amount of time a patient spends in specific GL intervals, provide obvious modifications to the prior art of record. The examiner further believes that the teachings of Duke et. al.'980, pertaining to creating clusters between glucose measurements and rate of change of glucose in order to more accurately correlate glucose measurements to similar matrices stored in a database as profiles and reduce medical error via presenting the same cluster upon repetition, provide obvious modifications to the prior art of record. These combinations are addressed in further detail in Paragraph 5 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARAH ANN WESTFALL whose telephone number is (571) 272-3845. The examiner can normally be reached Monday-Friday 7:30am-4:30pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Robertson can be reached at (571) 272-5001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SARAH ANN WESTFALL/Examiner, Art Unit 3791
/ETSUB D BERHANU/Primary Examiner, Art Unit 3791
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