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 Objections
Claim 1 is objected to because of the following informalities: there is a typographical error in line 18 of the claim. The term “disfunction” should recite “dysfunction”. Appropriate correction is required.
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 1-2, 5-6, and 8-15 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. The instant application’s specification provides support for determining a presence, level, or severity of diabetes of someone with kidney dysfunction (Paragraph [0054] and [0082] - configured to diagnose, stage, treat, and assess risks of diabetes, as well as predict the likelihood of experiencing atypical glucose trends and/or atypical glucose trends associated with kidney disease…to perform analytics thereon to (1) automatically detect abnormal patterns associated with various glucose metrics, (2) assess the presence and severity of diabetes, (3) risk stratify patients to identify those patients with a high risk of diabetes) but does not provide support for "determine a level of kidney dysfunction based on the glucose pattern by identifying a glucose profile for the patient corresponding to the level of kidney dysfunction" as recited in the claim. Additionally, the specification fails to provide a description of how a glucose profile is identified, and there is no description of identifying a glucose profile that corresponds to a level of kidney dysfunction. Rather, the specification states a patient’s kidney disease progression can be determined based on a set point threshold and knowledge that a patient has a kidney disease (Paragraph [0086] - previously diagnosed with (1) no diabetes and kidney disease, (2) no diabetes and varying stages of kidney disease, (3) varying stages of diabetes and no kidney disease, or (4) varying stages of diabetes and varying stages of kidney disease. The training data may be stored in historical records database 112 and may be accessible to training server system 140 over one or more networks (not shown) for training the machine learning model(s); Paragraph [0197] - decision support engine 114 may use a set point metric to determine a patient’s kidney disease stage. For example, as kidney disease worsens, there is increased variability in glucose measurements, which may result in less glucose level time in range, specifically within a range of a set point. As glucose levels within a range of the set point become less frequent, decision support engine 114 may determine a user’s kidney disease is progressing). Determining a set point metric indicative of kidney disease stage progression based on previous knowledge that the user has kidney disease is not the same as using a glucose pattern/profile to determine a level or stage of kidney dysfunction. The examiner notes that the instant application’s specification appears to describe identifying kidney disease progression – a timeline - based on various threshold measurement amounts whereas the claim recites determining certain stages or levels of kidney dysfunction based on observed glucose patterns/profiles.
Claims not explicitly rejected above are rejected due to their dependence on the above claims with regard to Claim 1.
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, 5-6, and 8-15 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 “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” recited in lines 14-17 is indefinite. This limitation is unclear due to the type of glucose pattern being recited in a redundant manner. More specifically, the limitation recites “determine a glucose pattern by determining a rate at which the peak glucose value returns back to the baseline value” and also recites “the glucose pattern comprising at least one glucose clearance rate”. Is the “a rate at which the peak glucose value returns back to the baseline glucose value within the first period of time” the same as the later recited “at least one glucose clearance rate”? If they are the same, why is the claim reciting these elements in a redundant fashion? Are these two different glucose patterns? If they are two different glucose patterns, how do these two comparisons of rates differ? For this examination, this limitation is being interpreted to mean “determine a glucose pattern as a glucose clearance rate comprising a rate at which the peak glucose value returns back to the baseline glucose value”.
Additionally, the limitation “determine a level of kidney dysfunction based on the glucose pattern by identifying a glucose profile for the patient corresponding to the level of kidney dysfunction” recited in lines 18-20 of the claim are indefinite. How exactly is the level of kidney dysfunction being determined? Is it based on the glucose pattern? Is it based on identifying a glucose profile? Are the glucose pattern and glucose profile the same or are they linked? If the glucose pattern and profile are linked, how are they linked? If the glucose profile is its own thing, how is this identified?
Claims not explicitly rejected above are rejected due to their dependence on Claim 1.
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 Publication 20160328990 – previously cited), in view of Schleicher et. al.'625 (U.S. Patent 11197625 – previously cited) as evidenced by Robinson et. al.’533 (U.S. Patent Publication 20170079533), further in view of Liao et. al.'2012 (Insulin Resistance in Patients with Chronic Kidney Disease), and further in view of Whaley-Connell et. al.’2017 (Insulin Resistance in Kidney Disease: Is There a Distinct Role Separate from That of Diabetes or Obesity) as evidenced by Otvos et. al.'267 (U.S. Patent Publication 20150127267).
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 dysfunction 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 further discloses identifying trends regarding peaks in glucose measurements, analyzing patterns of glucose variability including rate of change, and determining how glucose concentration patterns differ from baseline values in order to understand how the body is processing sugar (Paragraph [0029] - The determined pattern may be selected from the group consisting of: overnight lows, postprandial spikes, a type of discriminated fault, a pattern of high analyte variability, or a consistent pattern of weekly highs or lows. The method may further include determining a baseline analyte concentration pattern for the user, and the determined pattern may be a consistent variation from the baseline pattern; Paragraph [0362] - In contrast, if the postprandial peak is higher but generally follows the same curve, then systems and methods may numerically infer that there is a problem with insulin secretion). Simpson et. al.'990 further discloses identifying a rate of change of an analyte - glucose (Paragraph [0459] - The rate of change of the analyte) (emphasis added), but fails to disclose receiving 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 processing 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.
To best to the Examiner’s understanding, Schleicher et. al.'625 teaches using an oral glucose tolerance test configured to measure an increase – peak – in glucose followed by its decline to baseline (Column 3 Lines 49-60 - An oral glucose tolerance test (OGTT) is a medical test that measures the body's ability to metabolize glucose…Before conducting an OGTT, a patient may fast for up to 8-12 hours to reach a baseline (fasting) level of blood glucose. The fasting glucose level can be measured by taking a blood sample prior to the beginning of the test. The patient then consumes a glucose solution. After the patient consumes the glucose solution, a series of blood samples are taken to monitor the patient's blood glucose level over time in response to taking the glucose solution; Column 4 Lines 8-13 - an automated device or system may perform the OGTT more accurately, precisely, or uniformly (e.g., by taking measurements at a greater number of specified time points, by timing the measurements more accurately, by performing the blood draws more uniformly, or according to some other metric)). Schleicher et. al.'625 further teaches receiving and processing glucose data indicative of clearance rate (Column 7 Lines 7-10 - by measuring the level of blood glucose over time following the administration of a glucose solution, it may be possible to measure the body's glucose 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 a glucose tolerance test indicative of glucose clearance rate as a way for subjects to understand their experienced glucose variability after certain events and understand the rate at which glucose spikes return to baseline values as seen in Schleicher et. al.'625 and as evidenced by Robinson et. al.'533 that teaches oral glucose tolerance tests are gold standard practices to measure how quickly a peak in glucose returns to baseline (Paragraph [0005] - entire paragraph - The OGTT is the clinical gold standard for diagnosis of diabetes despite various drawbacks. After presenting in a fasting state, the patient is administered an oral dose of glucose solution (75 to 100 grams of dextrose) which typically causes blood glucose levels to rise in the first hour and return to baseline within three hours as the body produces insulin to normalize glucose levels…Current ADA guidelines dictate a diagnosis of diabetes if the two-hour post-load blood glucose value is greater than 200 mg/dl on two separate OGTTs administered on different days).
Simpson et. al.’990 further fails to disclose determining a level of kidney dysfunction based on the glucose pattern by identifying a glucose profile for the patient corresponding to the level of kidney dysfunction. Liao et. al.’2012 teaches glucose tolerance test measurements are reflective of insulin resistance (Page 2 Paragraph 3 - The oral glucose tolerance test (OGTT) primarily measures glucose tolerance, which reflects both IR (Insulin Resistance) and beta-cell function; IR can be calculated from OGTT using validated formulae). Whaley-Connell et. al.’2017 teaches that insulin responsiveness to glucose is indicative of different states of kidney disease (Page 44 Paragraph 3 - Indeed, when compared to early stages (e.g., CKD stage 1 and 2), insulin sensitivity measures were more reduced in patients with CKD stage 3). 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 as evidenced by Robinson et. al.’533 to include glucose profiles/patterns obtained by glucose tolerance tests that are reflective of insulin resistance as seen in Liao et. al.’2012 in order to understand a user’s stage/level of kidney dysfunction as seen in Whaley-Connell et. al.’2017. Whaley-Connell et. al.’2017 teaches observing kidney dysfunction based on insulin sensitivity and therefore could also use insulin resistance as evidenced by Otvos et. al.'267 (Paragraph [0006] - measures insulin sensitivity (S.sub.i), the inverse of insulin resistance).
Regarding Claim 5, Simpson et. al.'990 in view of Schleicher et. al.'625 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, and further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267 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).
Regarding Claim 6, Simpson et. al.'990 in view of Schleicher et. al.'625 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, and further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267 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 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, and further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267 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 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, and further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267 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 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, and further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267 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 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, and further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267 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 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, and further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267 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 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, and further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267 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 Publication 20160328990 – previously cited), in view of Schleicher et. al.'625 (U.S. Patent 11197625 – previously cited) as evidenced by Robinson et. al.’533 (U.S. Patent Publication 20170079533), further in view of Liao et. al.'2012 (Insulin Resistance in Patients with Chronic Kidney Disease), further in view of Whaley-Connell et. al.’2017 (Insulin Resistance in Kidney Disease: Is There a Distinct Role Separate from That of Diabetes or Obesity) as evidenced by Otvos et. al.'267 (U.S. Patent Publication 20150127267), 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 Publication 20180263539 – previously cited).
Regarding Claim 2, Simpson et. al.'990 in view of Schleicher et. al.'625 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, and further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267 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 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, and further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267 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 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267, 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 Publication 20160328990 – previously cited), in view of Schleicher et. al.'625 (U.S. Patent 11197625 – previously cited) as evidenced by Robinson et. al.’533 (U.S. Patent Publication 20170079533), further in view of Liao et. al.'2012 (Insulin Resistance in Patients with Chronic Kidney Disease), further in view of Whaley-Connell et. al.’2017 (Insulin Resistance in Kidney Disease: Is There a Distinct Role Separate from That of Diabetes or Obesity) as evidenced by Otvos et. al.'267 (U.S. Patent Publication 20150127267), as applied to Claim 9 above, and further in view of Kiani et. al.'729 (U.S. Patent Publication 20210236729 – previously cited).
Regarding Claim 11, Simpson et. al.'990 in view of Schleicher et. al.'625 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, and further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267 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 in view of Schleicher et. al.'625 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, and further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267 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 as evidenced by Robinson et. al.’533, further in view of Liao et. al.’2012, further in view of Whaley-Connell et. al.’2017 as evidenced by Otvos et. al.'267, and further in view of Kiani et. al.'729 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 06 May 2026 have been fully considered and they are not entirely persuasive.
Examiner has addressed a current claim objection regarding Claim 1 in Paragraph 3 above.
Examiner has addressed current 112a and 112b rejections under 35 U.S.C. 112 in Paragraphs 4-5 above.
Regarding the applicant’s arguments regarding the limitations “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” as well as “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” recited in Claim 1, the examiner has found these arguments to be not entirely persuasive. The examiner has cited additional paragraphs from the prior arts of record Simpson et. al.’990 as well as Schleicher et. al.’625 that more clearly read on these limitations when in combination with newly cited prior art. These limitations are addressed in Paragraph 6 above.
Regarding the applicant’s arguments regarding the limitation “determine a level of kidney dysfunction based on the glucose pattern by identifying a glucose profile for the patient corresponding to the level of kidney dysfunction” recited in Claim 1, the examiner has found these arguments to be not entirely persuasive. The examiner has noted that this limitation meets the requirements for a rejection under 35 U.S.C. 112(a) as addressed in Paragraph 4 above as well as a rejection under 35 U.S.C. 112(b) as addressed in Paragraph 5 above. Additionally, it should be noted that the examiner has also provided prior art that they deem relevant pertaining to the interpretation of this limitation as best as possible. This limitation has also been addressed in Paragraph 6 above.
Claims 1-2, 5-6, and 8-15 are rejected under 35 U.S.C. 103 as necessitated by amendments, as discussed in Paragraphs 6-8 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Melker et. al.’175 (U.S. Patent Publication 20100216175) as evidenced by Ling et. al.'2022 (Use of Continuous Glucose Monitoring in the Assessment and Management of Patients with Diabetes and Chronic Kidney Disease) teaches comparing glucose patterns as a way of monitoring disease states and conditions related to renal failure and correlation between HbA1c and mean sensor glucose values tend to fall in CKD stage G4-5 respectively. Goodyear et. al.’872 (U.S. Patent Publication 20160000872) teaches using a glucose tolerance test to assist a subject in maintaining glucose homeostasis (Paragraph [0014] - “Glucose tolerance” is the ability of a subject to regulate blood glucose levels to normal, accepted ranges, i.e., to maintain glucose homeostasis or control blood glucose levels. A glucose tolerance test is a test in which glucose is given to a subject and the rate of clearance from the blood is measured).
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/SARAH ANN WESTFALL/Examiner, Art Unit 3791
/AURELIE H TU/Primary Examiner, Art Unit 3791
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