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.
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, 5-6, 8-10, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Simpson et. al.'990 (U.S. Patent Application 20160328990 – previously cited), in view of Schleicher et. al.'625 (U.S. Patent 11197625), and further in view of Ouyang et. al.’015 (U.S. Patent Application 20200241015).
Regarding Claim 1, Simpson et. al.’990 discloses a continuous analyte sensor configured to generate analyte measurements associated with analyte levels of a patient (Paragraph [0002] - The present embodiments relate to continuous analyte monitoring, and, in particular, to control of operation of an analyte monitor upon changes in available data in a continuous analyte monitoring system), the analyte measurement comprising at least glucose measurements (Paragraph [0149] - the analyte for measurement by the sensor heads, devices, and methods is glucose); and
a sensor electronics module coupled to the continuous analyte sensor and configured to receive and process the analyte measurements (Paragraph [0150] - The term “calibration” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to the process of determining the relationship between sensor data and corresponding reference data, which can be used to convert sensor data into meaningful values substantially equivalent to the reference data; Paragraph [0157] - For example, one or more electrodes can be used to detect the amount of glucose in a sample and convert that information into a signal, e.g., an electrical or electromagnetic signal; the signal can then be transmitted to an electronic circuit – sensor electronics module);
a memory comprising executable instructions (Paragraph [0568] - The computer readable medium may be a hard drive or solid state storage having instructions that, when run, are loaded into random access memory); and
one or more processors in data communication with the memory and configured to execute the executable instructions (Paragraph [0556] - the instructions being executable by one or more processors to perform the operations described herein) to:
receive and process glucose data associated with glucose measurements from the sensor electronics module for a period of time including a baseline glucose value (Paragraph [0182] - The systems and methods can define such as a baseline profile, and may construct the same from patterns using appropriate pattern recognition software. Then, the systems and methods may detect patterns or events representing deviations from the same in subsequent periods of measurement);
determine a level of an analyte indicative of kidney disfunction based on the glucose pattern by identifying a glucose profile for the patient corresponding to the level of kidney dysfunction (Paragraph [0003] - In the diabetic state, the patient or user suffers from high blood sugar, which can cause an array of physiological derangements associated with the deterioration of small blood vessels, for example, kidney failure; Paragraph [0149] - In some embodiments, the analyte for measurement by the sensor heads, devices, and methods is glucose. However, other analytes are contemplated as well, including…creatine kinase; creatine kinase); and
generate decision support output for managing glucose levels based on the glucose profile, the decision support output comprising at least one of (Paragraph [0342] - The outputs may be as noted above, indicating a degree of success or failure of the drug and regimen on, e.g., the user's glucose profile, and may also include a modification of the program, i.e., a new or modified perturbation):
a recommendation for treatment (Paragraph [0064] - displaying a user interface, the user interface including one or more graphical elements representing respective programs, the one or more programs configured to guide a user in treating diabetes); or
a recommendation for prevention of dysglycemic events based on the determined glucose pattern (Paragraph [0174] - the perturbation to a biological system will often include a program followed by a user in the treatment of a disease, e.g., diabetes, but may also be employed to prevent (or reverse) such diseases, e.g., when a user is prediabetic or non-diabetic).
Simpson et. al.’990 fails to disclose receive glucose data associated with the glucose measurements from the sensor electronics module for a first period of time, the glucose data including an initial peak glucose value and a subsequent baseline glucose value, the baseline glucose value within a glucose concentration range corresponding to glucose homeostasis for the patient and process the glucose data to determine a glucose pattern for the patient by determining a rate at which the peak glucose value returns back to the baseline glucose value within the first period of time, the glucose pattern comprising at least one glucose clearance rate for the patient based on the glucose data. Schleicher et. al.'625 teaches receiving and processing glucose data indicative of clearance rate (Column 18 Lines 49-54 - the device 400 determines a health state of the user based on the detected amounts of analyte in the first and second amounts of blood and provides an indication of the determined health state via the user interface 490 (e.g., uses temporal glucose measurements to provide an indication of a glucose clearance rate)). 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 Simpson et. al.’990 to include processing glucose clearance rate in order to gather more informative baseline values of a subject as seen in Schleicher et. al.'625.
Simpson et. al.’990 further fails to disclose determine a level of kidney disfunction based on the glucose pattern by identifying a glucose profile for the patient corresponding to the level of kidney dysfunction. Ouyang et. al.'015 teaches using creatinine to understand levels of kidney function (Paragraph [0024] - The creatinine sensors disclosed herein may be advantageous for monitoring creatinine levels (and kidney function) in any individual potentially at risk for kidney damage or failure, but they may be particularly beneficial for diabetic individuals due to the prevalence of diabetic neuropathy. Although it may be beneficial to monitor creatinine levels alone, it is also possible for a diabetic individual to monitor both their glucose and creatinine levels to afford improved health outcomes, particularly given that glucose monitoring is already performed routinely by diabetic individuals). 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 Simpson et. al.’990 in view of Schleicher et. al.'625 to include processing creatinine levels in order to understand levels of kidney function within a subject and provide earlier health care intervention to prevent possible kidney damage as seen in Ouyang et. al.'015 (Paragraph [0020] - Creatinine, for example, may be an analyte of particular interest for monitoring in individuals susceptible to kidney failure, particularly in diabetic individuals at risk for diabetic neuropathy; Paragraph [0021] - Analysis of creatinine levels with the analyte sensors disclosed herein may provide an individual or health care provider a more accurate representation of kidney function over an extended period of time…By analyzing creatinine levels according to the present disclosure, earlier health care intervention may be possible to limit potential kidney damage and improve overall health outcomes for an individual).
Regarding Claim 5, Simpson et. al.'990 in view of Schleicher et. al.'625, and further in view of Ouyang et. al.’015 discloses the system outlined in Claim 1 above. Simpson et. al.’990 further discloses one or more non-analyte sensors (Paragraph [0191] - Other activity monitors may include, e.g., heart rate monitors, pulse meters, and so on), wherein the processor is further configured to:
receive non-analyte sensor data generated for the patient using one or more non-analyte sensors, wherein the determined glucose pattern is further based on the non-analyte sensor data (Paragraph [0187] - a monitoring device 21 may receive data from a sensor and transmitter 22 and may be connected via a local network hotspot 25 (or using a cellular network) to a network or other cloud-based source of data 24; Paragraph [0190] - Data about one or more analyte levels may be accompanied by other data useful in the evaluation step (step 14). Such data may include activity data, including data about the intensity and duration of activity; Paragraph [0289] - for such patients, a kit, system, or package may be provided with a glucose sensor and transmitter, along with optional components such as a heart rate monitor (e.g., using an on-skin sensor); Paragraph [0374] - Data may then be tracked (step 508). The tracked data may include, e.g., activity data (via accelerometer or GPS or other systems noted above), calorie data, meal data, analyte data such as glucose, lactate, or lactic acid, metabolism data, heart rate data, as well as other types of data, and combinations of the above). Simpson et. al.'990 fails to disclose the determined likelihood of at least one atypical glucose trend is further based on the non-analyte sensor data.
Regarding Claim 6, Simpson et. al.'990 in view of Schleicher et. al.'625, and further in view of Ouyang et. al.’015 discloses the system outlined in Claim 5 above. Simpson et. al.’990 additionally discloses wherein the one or more non-analyte sensors comprise at least one of an insulin pump, a haptic sensor, an ECG sensor, a heart rate monitor, a blood pressure sensor, a respiratory sensor, a peritoneal dialysis machine, or a hemodialysis machine (Paragraph [0191] - Other activity monitors may include, e.g., heart rate monitors, pulse meters, and so on).
Regarding Claim 8, Simpson et. al.’990 in view of Schleicher et. al.'625, and further in view of Ouyang et. al.’015 discloses the system outlined in Claim 1 above. Simpson et. al.’990 further discloses a decision support output further comprises: an alert of an adverse glycemic event (Paragraph [0077] - the embodiments are directed towards a method of alerting a user to a pattern, and providing a program to address the pattern; Paragraph [0230] - one or more threshold levels shown, e.g., a threshold 94 for hypoglycemia and a threshold 92 for hyperglycemia. A textual indication 96 is displayed, indicating conveniently to the user a present summary status).
Regarding Claim 9, Simpson et. al.’990 in view of Schleicher et. al.'625, and further in view of Ouyang et. al.’015 discloses the system outlined in Claim 1 above. Simpson et. al.’990 further discloses one or more processors in data communication with the memory and configured to execute the executable instructions (Paragraph [0551] - For example, “determining” may include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, “determining” may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like; Paragraph [0568] - instructions are laid out on computer readable media, generally non-transitory, and these instructions are sufficient to allow a processor in the computing device to implement the method of the invention. The computer readable medium may be a hard drive or solid state storage having instructions that, when run, are loaded into random access memory) to:
receive glucose data associated with the glucose measurements from the sensor electronics module (Paragraph [0524] - Transmission of sensor data to a remote computer system can be performed wirelessly or alternatively via a tether that provides an electrical connection between the sensor and the sensor electronics unit);
predict a glucose response of the patient according to the glucose profile (Paragraph [0073] - The trend graph may include a desired analyte concentration value or range of values over the time period. The desired analyte concentration value or range of values may be based on a modeled, ideal, or predicted analyte concentration value or range of values); and
generate a diabetes disease prediction based on comparing the predicted glucose response to a predefined glucose response (Paragraph [0237] – entire paragraph; Paragraph [0470] - Prediabetics may be enabled to determine their diabetic state and to reverse a trend toward diabetes. For example, by maintaining or losing weight while producing the same or less amounts of insulin, a trend toward diabetes, as measured by pancreas function, may be halted or even reversed).
Regarding Claim 10, Simpson et. al.’990 in view of Schleicher et. al.'625, and further in view of Ouyang et. al.’015 discloses the system outlined in Claim 9 above. Simpson et. al.’990 further discloses the processor is further configured to generate one or more recommendations for treatment based, at least in part, on the diabetes disease prediction (Paragraph [0182] - For example, from the baseline pattern, the system may detect a pattern of overnight lows. However, if the user starts having hyperglycemic excursions on, e.g., Sunday afternoons, either as measured sua sponte or as measured with respect to the measured baseline profile, then the system may start to detect an unhealthy pattern as may be caused by, e.g., watching football and consuming unhealthy snacks. The same may then be used as a pattern to address with a program; Paragraph [0363] - In this way the systems and methods can automatically and computationally calculate a treatment recommendation. The systems and methods can further provide and display a reason for the recommendation).
Regarding Claim 13, Simpson et. al.’990 in view of Schleicher et. al.'625, and further in view of Ouyang et. al.’015 discloses the system outlined in Claim 9 above. Simpson et. al.’990 further discloses the diabetes disease prediction is indicative of a risk of developing diabetes or a current diabetes diagnosis of the patient (Paragraph [0230] - The user interface 26′ also shows a mid-day analyte trace graph 70, in which an analyte level 88 is plotted with respect to time, with one or more threshold levels shown, e.g., a threshold 94 for hypoglycemia and a threshold 92 for hyperglycemia. A textual indication 96 is displayed, indicating conveniently to the user a present summary status).
Regarding Claim 14, Simpson et. al.’990 in view of Schleicher et. al.'625, and further in view of Ouyang et. al.’015 discloses the system outlined in Claim 9 above. Simpson et. al.’990 further discloses the diabetes disease prediction is generated using a model trained based on population data including records of historical patients with varying stages of diabetes (Paragraph [0359] – entire paragraph; Paragraph [0360] - In some implementations, access may be used to public domain human computational models (e.g., the Oral Minimal model) to tie the differences to underlying changes in the biology).
Regarding Claim 15, Simpson et. al.’990 in view of Schleicher et. al.'625, and further in view of Ouyang et. al.’015 discloses the system outlined in Claim 10 above. Simpson et. al.’990 further discloses the recommendations for treatment include at least one of: a lifestyle recommendation, a medication recommendation, or a medical intervention recommendation (Paragraph [0337] - The systems and methods can also employ “recommender” systems, which can determine from lifestyle preferences; Paragraph [0363] - In this way the systems and methods can automatically and computationally calculate a treatment recommendation. The systems and methods can further provide and display a reason for the recommendation. It should be noted that the calculated drug regimens thus provide a personalized drug regimen for the user, and one that is identified as being optimized and personalized).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Simpson et. al.'990 (U.S. Patent Application 20160328990 – previously cited), in view of Schleicher et. al.'625 (U.S. Patent 11197625), further in view of Ouyang et. al.’015 (U.S. Patent Application 20200241015), as applied to Claim 1 above, further in view of Say et. al.'509 (U.S. Patent 6565509 – previously cited), and further in view of Javey et. al.'539 (U.S. Patent Application 20180263539 – cited by applicant).
Regarding Claim 2, Simpson et. al.'990 in view of Schleicher et. al.'625, and further in view of Ouyang et. al.’015 discloses the system outlined in Claim 1 above. Simpson et. al.’990 further discloses wherein the continuous analyte sensor comprises: a substrate (Paragraph [0509] - the exemplary embodiments illustrated in the figures involve circumferentially-extending membrane systems, the membranes described herein may be applied to any planar or non-planar surface, for example, the substrate-based sensor structure) and a working electrode with readings of a voltage across the electrode (Paragraph [0152] - a raw data stream (or just “raw” data) measured in counts is directly related to a voltage (e.g., converted by an A/D converter), which is directly related to current from the working electrode). Simpson et. al.’990 fails to disclose a working electrode disposed on the substrate or a reference electrode disposed on the substrate, wherein the analyte measurements generated by the continuous analyte sensor correspond to an electromotive force at least in part based on a potential difference generated between the working electrode and the reference electrode. Say et. al.'509 teaches a working and reference electrode disposed on a substrate (Column 7 lines 37-38 - The working electrode or electrodes 58 are formed using conductive traces 52 disposed on the substrate 50; Column 7 lines 38-42 - The counter electrode 60 and/or reference electrode 62, as well as other optional portions of the sensor 42, such as a temperature probe 66 (see FIG. 8), may also be formed using conductive traces 52 disposed on the substrate 50). It would have been obvious to one of ordinary skill in the art to have modified the system of Simpson et. al.'990 in view of Schleicher et. al.'625, and further in view of Ouyang et. al.’015 to include the working and reference electrode disposed on a substrate in order to encompass an entire monitoring system onto one device and material as seen in Say et. al.’509.
Javey et. al.’539 teaches generating sensor data corresponding to a potential difference between working and reference electrodes (Paragraph [0088] - the difference in potential of the floating ISE working and shared electrodes directly is measured. To this end, the signal conditioning paths of the potentiometric-based sensors included a voltage buffer interfacing the respective working and reference electrodes, followed by a differential amplifier to effectively implement an instrumentation amplifier configuration…With this approach the voltage sensing and current sensing paths are electrically isolated. Furthermore, the differential sensing stage also helped with minimizing the unwanted common-mode interferences which would have otherwise degraded the fidelity of the sensor readings). It would have been obvious to one of ordinary skill in the art to have modified the system of Simpson et. al.'990 in view of Schleicher et. al.'625, further in view of Ouyang et. al.’015, and further in view of Say et. al.’509 to include measuring a potential difference between a working and reference electrode in order to isolate the readings and therefore obtain more accurate readings while minimizing potential interfering signals as seen in Javey et. al.’539.
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Simpson et. al.'990 (U.S. Patent Application 20160328990 – previously cited), in view of Schleicher et. al.'625 (U.S. Patent 11197625), further in view of Ouyang et. al.’015 (U.S. Patent Application 20200241015), as applied to Claim 1 above, and further in view of Kiani et. al.'729 (U.S. Patent Application 20210236729 – previously cited).
Regarding Claim 11, Simpson et. al.'990 in view of Schleicher et. al.'625, and further in view of Ouyang et. al.’015 discloses the system outlined in Claim 9 above. Simpson et. al.’990 further discloses one or more non-analyte sensors (Paragraph [0191] - Other activity monitors may include, e.g., heart rate monitors, pulse meters, and so on), wherein the processor is further configured to:
receive non-analyte sensor data generated for the patient using one or more non-analyte sensors (Paragraph [0187] - a monitoring device 21 may receive data from a sensor and transmitter 22 and may be connected via a local network hotspot 25 (or using a cellular network) to a network or other cloud-based source of data 24; Paragraph [0289] - for such patients, a kit, system, or package may be provided with a glucose sensor and transmitter, along with optional components such as a heart rate monitor (e.g., using an on-skin sensor); Paragraph [0374] - Data may then be tracked (step 508). The tracked data may include, e.g., activity data (via accelerometer or GPS or other systems noted above), calorie data, meal data, analyte data such as glucose, lactate, or lactic acid, metabolism data, heart rate data, as well as other types of data, and combinations of the above). Simpson et. al.'990 fails to disclose wherein the diabetes disease prediction is further generated based on the non-analyte sensor data.
Kiani et. al.'729 teaches using non-analyte sensor data to predict an atypical glucose trend – also known as a diabetes disease (Paragraph [0502] - if a pattern of declining heart rate pattern with 10 beats per minute fluctuation 70 minutes prior and optionally a slight increase in sweat 70 minutes prior, the system may predict a longer term risk of predicted hypoglycemic event and notify the patient or user of a predicted hypoglycemic event). It would have been obvious to one of ordinary skill in the art to have modified the system of Simpson et. al.'990 to include using non-analyte sensor data in order to predict an atypical glucose trend because a declining heart rate can indicate a hypoglycemic event as seen in Kiani et. al.’729.
Regarding Claim 12, Simpson et. al.'990 in view of Schleicher et. al.'625, and further in view of Ouyang et. al.’015 discloses the system outlined in Claim 11 above. Simpson et. al.’990 further discloses wherein the one or more non-analyte sensors comprise at least one of an insulin pump, a haptic sensor, an ECG sensor, a heart rate monitor, a blood pressure sensor, a respiratory sensor, a peritoneal dialysis machine, or a hemodialysis machine (Paragraph [0191] - Other activity monitors may include, e.g., heart rate monitors, pulse meters, and so on).
Response to Arguments
Applicant's arguments filed 09 December 2025 have been fully considered and they are not entirely persuasive.
Applicant’s amendments have overcome the prior 35 U.S.C. 112(f) interpretations.
Applicant’s amendments have overcome the prior 35 U.S.C. 112(b) rejections.
Application’s amendments have overcome the prior 35 U.S.C. 101 rejections.
Claims 1-2, 5-6, and 8-15 are rejected under 35 U.S.C. 103 as necessitated by amendments, as discussed in Paragraphs 3-5 above. It is noted that Claims 1, 8-10, and 13-15 that were once rejected under 35 U.S.C. 102 are now rejected under 35 U.S.C. 103 as necessitated by amendments, as discussed in Paragraph 3 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
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/SARAH ANN WESTFALL/Examiner, Art Unit 3791
/ETSUB D BERHANU/Primary Examiner, Art Unit 3791