Office Action 1: Non-Final Rejection (2024-01-18)
Rejection types: 101 - Patent-Eligible Subject Matter, 102 - Lack of Novelty, 103 - Obviousness, 112 - Written Description/Enablement

CLAIMS:
CLAIMS

What is claimed is:

1\. An analyte monitoring system, comprising:
a sensor control device including an analyte sensor at least a portion of which is inserted into a user's body, first processing circuitry, and a first non-transitory memory, wherein the sensor control device is configured to collect sensor data indicative of an analyte level from the analyte sensor; and
a reader device comprising second processing circuitry and a second non-transitory memory, wherein at least one of the first or the second non-transitory memory includes instructions that, when executed, cause at least one of the first or the second processing circuitry to:
calculate a rate of change metric based on the collected sensor data;
calculate a sum of analyte levels metric based on the collected sensor data;
compare the rate of change metric with a first threshold and compare the sum of analyte levels metric with a second threshold; and
in response to a determination that the rate of change metric exceeds the first threshold and the sum of analyte levels metric exceeds the second threshold, generate an indication of a sensor fault.

2\. The analyte monitoring system of claim 1, wherein the rate of change metric is a function of a plurality of rate of change values.

3\. The analyte monitoring system of claim 2, wherein the rate of change metric is a central tendency of the plurality of rate of change values.

4\. The analyte monitoring system of claim 1, wherein the rate of change metric is based on the collected sensor data within a predetermined time window.

5\. The analyte monitoring system of claim 4, wherein the rate of change metric is based on two consecutive analyte data points of the collected sensor data within the predetermined time window.

6\. The analyte monitoring system of claim 4, wherein the rate of change metric is based on two non-consecutive analyte data points of the collected sensor data within the predetermined time window.

7\. The analyte monitoring system of claim 1, wherein the instructions to compare the rate of change metric to the first threshold comprises instructions to compare an absolute value of the rate of change metric to the first threshold.

8\. The analyte monitoring system of claim 4, wherein the sum of analyte levels metric comprises a sum of analyte level measurements within a second predetermined time window.

9\. The analyte monitoring system of claim 1, wherein the instructions, when executed, further cause the at least one of the first or the second processing circuitry to output a notification to a display of the reader device.

10\. The analyte monitoring system of claim 9, wherein the notification comprises a visual or auditory indication that a sensor fault was detected.

11\. The analyte monitoring system of claim 1, wherein the instructions, when executed, further cause the at least one of the first or the second processing circuitry to disable the analyte sensor.

12\. The analyte monitoring system of claim 1, wherein the rate of change metric is calculated at a first time and the sum of analyte levels metric is calculated at a second time, and
wherein the determination that the rate of change metric exceeds the first threshold and the sum of analyte levels metric exceeds the second threshold includes confirming that a difference between the first time and the second time is less than a predetermined time differential threshold.

13\. A computer-implemented method for detecting a sensor fault, the method comprising:
collecting, by a sensor control device comprising an analyte sensor at least a portion of which is inserted into a user's body, sensor data indicative of an analyte level;
calculating a rate of change metric based on the collected sensor data;
calculating a sum of analyte levels metric based on the collected sensor data;
comparing the rate of change metric with a first threshold and comparing the sum of analyte levels metric with a second threshold; and
in response to determining that the rate of change metric exceeds the first threshold and the sum of analyte levels metric exceeds the second threshold, generating an indication of a sensor fault.

14\. The method of claim 13, wherein the rate of change metric is based on the collected sensor data within a predetermined time window.

15\. The method of claim 14, wherein the sum of analyte levels metric comprises a sum of analyte level measurements within a second predetermined time window.

16\. The method of claim 13, wherein the rate of change metric is a central tendency of a plurality of rate of change values.

17\. The method of claim 13, wherein the rate of change metric is calculated at a first time and the sum of analyte levels metric is calculated at a second time, and wherein determining that the rate of change metric exceeds the first threshold and the sum of analyte levels metric exceeds the second threshold includes confirming that a difference between the first time and the second time is less than a predetermined time differential threshold.

18\. The method of claim 13, further comprising transmitting, by a wireless communication circuitry of the sensor control device, the collected sensor data to a reader device.

19\. The method of claim 18, further comprising outputting a notification to a display of the reader device.

20\. The method of claim 13, further comprising disabling the analyte sensor. - 22 - - 23 - - 24 - - 25 -

OFFICE ACTION:
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 .
This action is in response to the application filed on 04/07/2021. Claims 1-20 are pending and examined below.
Claim Objections
Claims 1, 10, and 13 objected to because of the following informalities:
Claim 1, line 6: “a” should be inserted before “second processing circuitry”
Claim 1 line 9: “the” should be inserted before “at least one”
Claim 10, line 2: “a sensor fault” should be “the sensor fault”
Claim 13, lines 10-11: “a sensor fault” should be “the sensor fault”
Appropriate correction is required.
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 8 and 15 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.
For claim 8, line 2, it is unclear if “analyte level measurements” is the same as, related to, or different from the collected data of claim 1.
For claim 15, lines 1-2, it is unclear if “analyte level measurements” is the same as, related to, or different from the collected data of claim 13.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Section 33(a) of the America Invents Act reads as follows:
Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism.
Claims 1-12 are rejected under 35 U.S.C. 101 and section 33(a) of the America Invents Act as being directed to or encompassing a human organism.  See also Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (indicating that human organisms are excluded from the scope of patentable subject matter under 35 U.S.C. 101).
Claim 1, lines 2-3 recite “an analyte sensor at least a portion of which is inserted into a user’s body” which improperly claims a human as part of the claimed system.
Claims 2-12 are rejected by virtue of their dependence from claim 1.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA  35 U.S.C. 102 and 103 (or as subject to pre-AIA  35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA  to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 8-10, 13-16, and 18-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20160022221 A1 (Hereinafter Ou).
Regarding claim 1, Ou teaches an analyte monitoring system, comprising: a sensor control device including an analyte sensor at least a portion of which is inserted into a user's body ([0017], “the present disclosure is operable to identify sensor faults from data from an in vivo analyte sensor.”), first processing circuitry ([0021], “The computer system may include one or more processors for executing instructions or programs”), and a first non-transitory memory ([0021], “The computer system may include memory and persistent storage devices to store and manipulate the instructions and sensor data received from the analyte monitoring system.”), wherein the sensor control device is configured to collect sensor data indicative of an analyte level from the analyte sensor ([0017], “The present disclosure provides systems, methods, and apparatus to identify sensor fault modes using sensor data from an analyte monitoring system”); and a reader device ([0021], “may be embodied as a handheld or portable receiver unit within the analyte monitoring system.) comprising second processing circuitry comprising second processing circuitry ([0021], “The computer system may include one or more processors for executing instructions or programs”) and a second non-transitory memory ([0021], “The computer system may include memory and persistent storage devices to store and manipulate the instructions and sensor data received from the analyte monitoring system.”), wherein at least one of the first or the second non-transitory memory includes instructions that, when executed, cause at least one of the first or the second processing circuitry to: calculate a rate of change metric based on the collected sensor data ([0019], “When sensor data obtained over a window of time is first obtained, a baseline pair can be calculated. a baseline analyte variability value is computed from this dataset.”); calculate a sum of analyte levels metric based on the collected sensor data ([0019], “When sensor data obtained over a window of time is first obtained, a baseline pair can be calculated. A baseline median or average analyte value and a baseline analyte variability value are computed from this dataset.”; [0028], “Based on this first dataset, a baseline median glucose value is computed. Alternatively, a mean, a mode, or any other representative “static” value of the first dataset may be used”); compare the rate of change metric with a first threshold ([0019], “The component of the vector is compared to a predetermined threshold value”) and compare the sum of analyte levels metric with a second threshold ([0032], “The system may then compare the sensor output to reference glucose measurement(s) relative to a second threshold”); and in response to a determination that the rate of change metric exceeds the first threshold and the sum of analyte levels metric exceeds the second threshold, generate an indication of a sensor fault ([0031], “If the component is larger than the threshold value, the system alerts the user that a fault has occurred or that the end of the sensor's life has been detected”).
Regarding claim 2, Ou teaches wherein the rate of change metric is a function of the median and 10th percentile values which are plurality of rate of change values ([0034], “For example, glucose variability may be computed by taking the difference between the median value and the 10th percentile value. Other methods and percentiles can be used”; the median value and the 10th percentile value are a plurality of rate of change values).
Regarding claim 3, Ou teaches wherein the rate of change metric is a central tendency of the plurality of rate of change values. Ou uses the median value making the rate of change metric a central tendency ([0034], “For example, glucose variability may be computed by taking the difference between the median value and the 10th percentile value. Other methods and percentiles can be used”; the use of the median value makes the metric a central tendency).
Regarding claim 4, Ou teaches wherein the rate of change metric is based on the collected sensor data within a predetermined time window ([0019], “When sensor data obtained over a window of time is first obtained, a baseline pair can be calculated”).
Regarding claim 5, Ou teaches wherein the rate of change metric is based on two consecutive analyte data points of the collected sensor data within the predetermined time window ([0019], “When sensor data obtained over a window of time is first obtained, a baseline pair can be calculated”).
Regarding claim 6, Ou teaches wherein the rate of change metric is based on two non-consecutive analyte data points of the collected sensor data within the predetermined time window ([0019], “A baseline median or average analyte value and a baseline analyte variability value are computed from this dataset.”); [0028], “For example, glucose variability may be computed by taking the difference between the median value and the 10th percentile value. Other methods and percentiles, such as standard deviation, inter quartile range, or other second moment calculation, can be used”; The difference between the median value and the 10th percentile value are two non-consecutive data points.).
Regarding claim 8, Ou teaches wherein the sum of analyte levels metric comprises a sum of analyte level measurements within a second predetermined time window ([0019], “The evaluation data point is determined by computing an evaluation median or average analyte value and an evaluation analyte variability value from the second dataset”; the second dataset would be taken during a second time window).
Regarding claim 9, Ou teaches wherein the instructions, when executed, further cause the at least one of the first or the second processing circuitry to output a notification to a display of the reader device ([0039], “displaying, on a system display, an indication that a sensor fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value”).
Regarding claim 10, Ou teaches wherein the notification comprises a visual or auditory indication that a sensor fault was detected ([0039], “displaying, on a system display, an indication that a sensor fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value”).
Regarding claim 13, Ou teaches a computer-implemented method for detecting a sensor fault, ([0005], “Some embodiments of the present disclosure include computer-implemented methods of identifying sensor fault modes using analyte monitoring system sensor data”) the method comprising: collecting, by a sensor control device comprising an analyte sensor at least a portion of which is inserted into a user's body sensor data indicative of an analyte level ([0017], “the present disclosure is operable to identify sensor faults from data from an in vivo analyte sensor.”); calculating a rate of change metric based on the collected sensor data ([0019], “When sensor data obtained over a window of time is first obtained, a baseline pair can be calculated. a baseline analyte variability value is computed from this dataset.”); calculating a sum of analyte levels metric based on the collected sensor data ([0019], “When sensor data obtained over a window of time is first obtained, a baseline pair can be calculated. A baseline median or average analyte value and a baseline analyte variability value are computed from this dataset.”; [0028], “Based on this first dataset, a baseline median glucose value is computed. Alternatively, a mean, a mode, or any other representative “static” value of the first dataset may be used”); comparing the rate of change metric with a first threshold ([0019], “The component of the vector is compared to a predetermined threshold value”) and comparing the sum of analyte levels metric with a second threshold ([0032], “The system may then compare the sensor output to reference glucose measurement(s) relative to a second threshold”); and in response to determining that the rate of change metric exceeds the first threshold and the sum of analyte levels metric exceeds the second threshold, generating an indication of a sensor fault ([0031], “If the component is larger than the threshold value, the system alerts the user that a fault has occurred or that the end of the sensor's life has been detected”).
Regarding claim 14, Ou teaches wherein the rate of change metric is based on the collected sensor data within a predetermined time window ([0019], “When sensor data obtained over a window of time is first obtained, a baseline pair can be calculated”).
Regarding claim 15, Ou teaches wherein the sum of analyte levels metric comprises a sum of analyte level measurements within a second predetermined time window ([0019], “The evaluation data point is determined by computing an evaluation median or average analyte value and an evaluation analyte variability value from the second dataset”).
Regarding claim 16, Ou teaches wherein the rate of change metric is a central tendency of a plurality of rate of change values ([0034], “For example, glucose variability may be computed by taking the difference between the median value and the 10th percentile value. Other methods and percentiles can be used”).
Regarding claim 18, Ou teaches transmitting, by a wireless communication circuitry of the sensor control device, the collected sensor data to a reader device ([0021], “Embodiments of the present disclosure may include a programmed computer system…the computer system may also include communications facilities (e.g., wireless and/or wired) to enable transfer of the sensor data from the analyte monitoring system to the computer… the computer system may be embodied as a handheld or portable receiver unit within the analyte monitoring system”; the computer system is reader device).
Regarding claim 19, Ou teaches outputting a notification to a display of the reader device ([0021], “The computer system may include a display”; [0039], “displaying, on a system display, an indication that a sensor fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value”).
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.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ou in view of WO 02058537 A2 (Hereinafter Heller).
Regarding claim 7, Ou teaches the analyte monitoring system of claim 1 as taught above as well as wherein the instructions to compare the rate of change metric to the first threshold ([0019], “The component of the vector is compared to a predetermined threshold value”).
Ou does not explicitly teach an absolute value of the rate of change metric.
Heller teaches an absolute value of the rate of change metric.
Heller is directed towards analyte monitoring device and methods. It would have been obvious before the effective filling date of the invention to incorporate the measurement circuit of Heller into the teachings of Ou. Doing so would convert raw signals into the absolute value of the signal. This allows the decision on the magnitude of the change to be irrespective of directionality. In addition, the incorporation of Heller would not change the respective functions of Ou to detect sensor fault or Heller to monitor analyte levels.
Claims 11, 12, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ou in view of US 20160296148 A1 (Hereinafter Hayter).
Regarding claim 11, Ou teaches the analyte monitoring system of claim 1 as taught above.
Ou does not explicitly teach wherein the instructions, when executed, further cause the at least one of the first or the second processing circuitry to disable the analyte sensor ([0046], “displaying the indication that the sensor fault has been detected further includes displaying, on the system display, an indication to remove the glucose sensor”).
Hayter teaches wherein the instructions, when executed, further cause the at least one of the first or the second processing circuitry to disable the analyte sensor ([0144], “for example, when there are more than a predetermined number of data points which deviate from a predefined level, the receiver/monitor unit 104/106, for example, may be configured to disable or disallow the display or output of the glucose trend information”).
Hayter is directed to data processing and verifying the stability of analyte sensors. It would have been obvious to one of ordinary skill in the art prior to the effective filling date to disable the analyte sensor, as suggested by of Hayter, into the teachings of Ou. Doing so would allow the system to stop the collection of data when sensor failure occurs. This would ensure all the obtained data is usable. The incorporation of Hayter into Ou would not change their respective functions.
Regarding claim 12, Ou teaches wherein the rate of change metric is calculated at a first time and the sum of analyte levels metric is calculated at a second time, ([0034], “a baseline data point is stored for later use in, for example, a memory (708). The system then waits for a new dataset of glucose values (710).”; [0035], “the datasets should not overlap in time”) and the determination that the rate of change metric exceeds the first threshold and the sum of analyte levels metric exceeds the second threshold ([0032], “One example is to examine the difference between the latest sensor output value to the latest reference glucose measurement, and flag for a fault if the difference exceeds, for example, 15 mg/dL”).
Ou does not explicitly teach wherein the determination that the rate of change metric exceeds the first threshold and the sum of analyte levels metric exceeds the second threshold includes confirming that a difference between the first time and the second time is less than a predetermined time differential threshold.
Hayter teaches wherein the determination that the rate of change metric exceeds the first threshold and the sum of analyte levels metric exceeds the second threshold includes confirming that a difference between the first time and the second time is less than a predetermined time differential threshold ([0076], “the detection of the signal level from the sensor 101 that falls below the predetermined threshold level for the predefined period of time.”)
Hayter is directed to data processing and verifying the stability of analyte sensors. It would have been obvious to one of ordinary skill in the art prior to the effective filling date to incorporate the teachings of Hayter to detect signal levels during a predetermined period of time into the teachings of Ou. Confirming the time differential helps ensure the metrics are exceeding the thresholds due to sensor fault and as biological metrics can fluctuate throughout the course of the day. This addition would not change the respective functions of Ou or Hayter.
Regarding claim 17, Ou teaches wherein the rate of change metric is calculated at a first time and the sum of analyte levels metric is calculated at a second time ([0032], “One example is to examine the difference between the latest sensor output value to the latest reference glucose measurement, and flag for a fault if the difference exceeds, for example, 15 mg/dL”).
Ou does not explicitly teach wherein determining that the rate of change metric exceeds the first threshold and the sum of analyte levels metric exceeds the second threshold includes confirming that a difference between the first time and the second time is less than a predetermined time differential threshold.
Hayter teaches wherein determining that the rate of change metric exceeds the first threshold and the sum of analyte levels metric exceeds the second threshold includes confirming that a difference between the first time and the second time is less than a predetermined time differential threshold ([0076], “the detection of the signal level from the sensor 101 that falls below the predetermined threshold level for the predefined period of time.”)
Hayter is directed to data processing and verifying the stability of analyte sensors. It would have been obvious to one of ordinary skill in the art prior to the effective filling date to incorporate the teachings of Hayter to detect signal levels during a predetermined period of time into the teachings of Ou. Confirming the time differential helps ensure the metrics are exceeding the thresholds due to sensor fault and as biological metrics can fluctuate throughout the course of the day. This addition would not change the respective functions of Ou or Hayter.
Regarding claim 20, Ou teaches the method of claim 13 as taught above.
Ou does not explicitly teach disabling the analyte sensor ([0046], “displaying the indication that the sensor fault has been detected further includes displaying, on the system display, an indication to remove the glucose sensor”)
Hayter teaches disabling the analyte sensor ([0144], “for example, when there are more than a predetermined number of data points which deviate from a predefined level, the receiver/monitor unit 104/106, for example, may be configured to disable or disallow the display or output of the glucose trend information”).
Hayter is directed to data processing and verifying the stability of analyte sensors. It would have been obvious to one of ordinary skill in the art prior to the effective filling date to incorporate the teachings of Hayter into the teachings of Ou. Doing so would allow the system to stop the collection of data when sensor failure occurs. This would ensure all the obtained data is usable. The incorporation of Hayter into Ou would not change their respective functions.
Conclusion


NEXT OFFICE ACTION: 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 .
Applicant' s arguments, filed 04/18/2024, have been fully considered.  The following rejections and/or objections are either reiterated or newly applied.  They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 04/18/2024, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Applicant has cancelled claims 12 and 17 in the response filed 04/18/2024, which is acknowledged.
Claims 1-11, 13-16, and 18-20 are the current claims hereby under examination.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA  35 U.S.C. 102 and 103 (or as subject to pre-AIA  35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA  to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over US 20160022221 A1 (Hereinafter Ou) (previously cited) in view of CN 108544925 A (Hereinafter “Xiong”)(citations from Xiong will be from the computer translation).
Regarding claim 1, Ou teaches an analyte monitoring system, comprising: a sensor control device including an analyte sensor at least a portion of which is configured to be inserted into a user's body ([0017], “the present disclosure is operable to identify sensor faults from data from an in vivo analyte sensor.”), first processing circuitry ([0021], “The computer system may include one or more processors for executing instructions or programs”), and a first non-transitory memory ([0021], “The computer system may include memory and persistent storage devices to store and manipulate the instructions and sensor data received from the analyte monitoring system.”), wherein the sensor control device is configured to collect sensor data indicative of an analyte level from the analyte sensor ([0017], “The present disclosure provides systems, methods, and apparatus to identify sensor fault modes using sensor data from an analyte monitoring system”); and a reader device ([0021], “may be embodied as a handheld or portable receiver unit within the analyte monitoring system.) comprising second processing circuitry comprising second processing circuitry ([0021], “The computer system may include one or more processors for executing instructions or programs”) and a second non-transitory memory ([0021], “The computer system may include memory and persistent storage devices to store and manipulate the instructions and sensor data received from the analyte monitoring system.”), wherein at least one of the first or the second non-transitory memory includes instructions that, when executed, cause the at least one of the first or the second processing circuitry to: calculate a rate of change metric based on the collected sensor data wherein the rate of change metric is calculated at a first time ([0019], “When sensor data obtained over a window of time is first obtained, a baseline pair can be calculated. a baseline analyte variability value is computed from this dataset.”; [0035], “the datasets should not overlap in time”; The first-time window is a first time.); calculate a sum of analyte levels metric based on the collected sensor data, wherein the sum of analyte levels metric is calculated at a second time ([0028], “Based on this first dataset, a baseline median glucose value is computed. Alternatively, a mean, a mode, or any other representative “static” value of the first dataset may be used”; [0035], “Based on this new dataset, an evaluation median glucose value is computed (714).” The second time is the time in which the second dataset is obtained. The sum is then calculated as alternative to the median as discussed in [0028]); compare the rate of change metric with a first threshold ([0019], “The component of the vector is compared to a predetermined threshold value”) and compare the sum of analyte levels metric with a second threshold ([0032], “The system may then compare the sensor output to reference glucose measurement(s) relative to a second threshold”); and in response to a determination that the rate of change metric exceeds the first threshold, that the sum of analyte levels metric exceeds the second threshold, generate an indication of a sensor fault ([0031], “If the component is larger than the threshold value, the system alerts the user that a fault has occurred or that the end of the sensor's life has been detected”).
Ou does not explicitly teach that an indication of a sensor fault is generated when a difference between the first time and the second time is less than a predetermined time threshold.
Xiong teaches determining a possible sensor change or fault when the sensor signal is below/above a threshold after a predetermined time period (page 6 of English translation, paragraph 3, “fault type judging module, a single current sensor value exceeds the current threshold, the monomer battery voltage sensor value exceeds the voltage threshold value and the monomer battery temperature sensor value exceeds a temperature threshold value exceeds a temperature threshold in any one condition is satisfied and the duration time exceeds the time threshold, then the module output is suspected a sensor fault”).
Xiong is directed toward determining sensor fault. It would have been obvious to one of ordinary skill in the art prior to the effective filing date to modify the system of Ou to include determining if the time in which the data was collected exceeds a time threshold and determining sensor fault when the time exceeds the threshold. Doing so will ensure that the data changes are not just a random occurrence and the sensor is consistently failing.
Regarding claim 2, the combination teaches wherein the rate of change metric is a function of the median and 10th percentile values which are plurality of rate of change values (Ou, [0034], “For example, glucose variability may be computed by taking the difference between the median value and the 10th percentile value. Other methods and percentiles can be used”; the median value and the 10th percentile value are a plurality of rate of change values).
Regarding claim 3, the combination teaches wherein the rate of change metric is a central tendency of the plurality of rate of change values. Ou uses the median value making the rate of change metric a central tendency (Ou, [0034], “For example, glucose variability may be computed by taking the difference between the median value and the 10th percentile value. Other methods and percentiles can be used”; the use of the median value makes the metric a central tendency).
Regarding claim 4, the combination teaches wherein the rate of change metric is based on the collected sensor data within a predetermined time window (Ou, [0019], “When sensor data obtained over a window of time is first obtained, a baseline pair can be calculated”).
Regarding claim 5, the combination teaches wherein the rate of change metric is based on two consecutive analyte data points of the collected sensor data within the predetermined time window (Ou, [0019], “When sensor data obtained over a window of time is first obtained, a baseline pair can be calculated”).
Regarding claim 6, the combination teaches wherein the rate of change metric is based on two non-consecutive analyte data points of the collected sensor data within the predetermined time window (Ou, [0019], “A baseline median or average analyte value and a baseline analyte variability value are computed from this dataset.”); [0028], “For example, glucose variability may be computed by taking the difference between the median value and the 10th percentile value. Other methods and percentiles, such as standard deviation, inter quartile range, or other second moment calculation, can be used”; The difference between the median value and the 10th percentile value are two non-consecutive data points.).
Regarding claim 8, the combination teaches wherein the sum of analyte levels metric comprises a sum of analyte level measurements within a second predetermined time window (Ou, [0019], “The evaluation data point is determined by computing an evaluation median or average analyte value and an evaluation analyte variability value from the second dataset”; the second dataset would be taken during a second time window).
Regarding claim 9, the combination teaches wherein the instructions, when executed, further cause the at least one of the first or the second processing circuitry to output a notification to a display of the reader device (Ou, [0039], “displaying, on a system display, an indication that a sensor fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value”).
Regarding claim 10, the combination teaches wherein the notification comprises a visual or auditory indication that a sensor fault was detected (Ou, [0039], “displaying, on a system display, an indication that a sensor fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value”).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ou in view of Xiong as applied to claim 1 above further in view of WO 02058537 A2 (Hereinafter Heller)(previously cited).
Regarding claim 7, the combination teaches the analyte monitoring system of claim 1 as taught above as well as wherein the instructions to compare the rate of change metric to the first threshold (Ou, [0019], “The component of the vector is compared to a predetermined threshold value”).
The combination does not explicitly teach an absolute value of the rate of change metric.
Heller teaches an absolute value of the rate of change metric.
Heller is directed towards analyte monitoring device and methods. It would have been obvious before the effective filling date of the invention to incorporate the measurement circuit of Heller into the system of Ou in view of Xiong. Doing so would convert raw signals into the absolute value of the signal. This allows the decision on the magnitude of the change to be irrespective of directionality. In addition, the incorporation of Heller would not change the respective functions of Ou in view of Xiong to detect sensor fault or Heller to monitor analyte levels.
Claims 11, 13-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20160022221 A1 (Hereinafter Ou) (previously cited) in view of CN 108544925 A (Hereinafter “Xiong”) further in view of US 20160296148 A1 (Hereinafter “Hayter”)(previously cited).
Regarding claim 11, the combination teaches the analyte monitoring system of claim 1 as taught above.
The combination as applied to claim 1 does not explicitly teach but suggests wherein the instructions, when executed, further cause the at least one of the first or the second processing circuitry to disable the analyte sensor (Ou, [0046], “displaying the indication that the sensor fault has been detected further includes displaying, on the system display, an indication to remove the glucose sensor”).
Hayter teaches wherein the instructions, when executed, further cause the at least one of the first or the second processing circuitry to disable the analyte sensor ([0144], “for example, when there are more than a predetermined number of data points which deviate from a predefined level, the receiver/monitor unit 104/106, for example, may be configured to disable or disallow the display or output of the glucose trend information”).
Hayter is directed to data processing and verifying the stability of analyte sensors. It would have been obvious to one of ordinary skill in the art prior to the effective filling date to disable the analyte sensor, as suggested by of Hayter, into the system of the combination. Doing so would allow the system to stop the collection of data when sensor failure occurs. This would ensure all the obtained data is usable. The incorporation of Hayter into Ou would not change their respective functions.
Regarding claim 13, Ou teaches a computer-implemented method for detecting a sensor fault, ([0005], “Some embodiments of the present disclosure include computer-implemented methods of identifying sensor fault modes using analyte monitoring system sensor data”) the method comprising: collecting, by a sensor control device comprising an analyte sensor at least a portion of which is inserted into a user's body sensor data indicative of an analyte level ([0017], “the present disclosure is operable to identify sensor faults from data from an in vivo analyte sensor.”); calculating a rate of change metric based on the collected sensor data wherein the rate of change metric is calculated at a first time ([0019], “When sensor data obtained over a window of time is first obtained, a baseline pair can be calculated. a baseline analyte variability value is computed from this dataset.”; [0035], “the datasets should not overlap in time”; The first-time window is a first time.); calculating a sum of analyte levels metric based on the collected sensor data, wherein the sum of analyte levels metric is calculated at a second time ([0028], “Based on this first dataset, a baseline median glucose value is computed. Alternatively, a mean, a mode, or any other representative “static” value of the first dataset may be used”; [0035], “Based on this new dataset, an evaluation median glucose value is computed (714).” The second time is the time in which the second dataset is obtained. The sum is then calculated as alternative to the median as discussed in [0028]); comparing the rate of change metric with a first threshold ([0019], “The component of the vector is compared to a predetermined threshold value”) and comparing the sum of analyte levels metric with a second threshold ([0032], “The system may then compare the sensor output to reference glucose measurement(s) relative to a second threshold”); and in response to determining that the rate of change metric exceeds the first threshold and that the sum of analyte levels metric exceeds the second threshold, generating an indication of a sensor fault ([0031], “If the component is larger than the threshold value, the system alerts the user that a fault has occurred or that the end of the sensor's life has been detected”).
Ou does not explicitly teach that an indication of a sensor fault is generated when a difference between the first time and the second time is less than a predetermined time threshold.
Hayter teaches determining a possible sensor change or fault when the sensor signal is below/above a threshold after a predetermined time period ([0076], “where the receiver unit 104 receives one or more signals from the transmitter unit 102 associated with the detection of the signal level below the predetermined threshold level for the predefined period of time”).
Hayter is directed to data processing and verifying the stability of analyte sensors. It would have been obvious to one of ordinary skill in the art prior to the effective filling date to incorporate the teachings of Hayter to detect signal levels during a predetermined period of time into the teachings of Ou. Confirming the time differential helps ensure the metrics are exceeding the thresholds due to sensor fault and as biological metrics can fluctuate throughout the course of the day. This addition would not change the respective functions of Ou or Hayter.
Regarding claim 14, the combination teaches wherein the rate of change metric is based on the collected sensor data within a predetermined time window (Ou, [0019], “When sensor data obtained over a window of time is first obtained, a baseline pair can be calculated”).
Regarding claim 15, the combination teaches wherein the sum of analyte levels metric comprises a sum of analyte level measurements within a second predetermined time window (Ou, [0019], “The evaluation data point is determined by computing an evaluation median or average analyte value and an evaluation analyte variability value from the second dataset”).
Regarding claim 16, the combination teaches wherein the rate of change metric is a central tendency of a plurality of rate of change values (Ou, [0034], “For example, glucose variability may be computed by taking the difference between the median value and the 10th percentile value. Other methods and percentiles can be used”).
Regarding claim 18, the combination teaches transmitting, by a wireless communication circuitry of the sensor control device, the collected sensor data to a reader device (Ou, [0021], “Embodiments of the present disclosure may include a programmed computer system…the computer system may also include communications facilities (e.g., wireless and/or wired) to enable transfer of the sensor data from the analyte monitoring system to the computer… the computer system may be embodied as a handheld or portable receiver unit within the analyte monitoring system”; the computer system is reader device).
Regarding claim 19, the combination teaches outputting a notification to a display of the reader device (Ou, [0021], “The computer system may include a display”; [0039], “displaying, on a system display, an indication that a sensor fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value”).
Regarding claim 20, Ou in view of Hayter as applied to claim 13 does not explicitly teach disabling the analyte sensor ([0046], “displaying the indication that the sensor fault has been detected further includes displaying, on the system display, an indication to remove the glucose sensor”)
Hayter teaches disabling the analyte sensor ([0144], “for example, when there are more than a predetermined number of data points which deviate from a predefined level, the receiver/monitor unit 104/106, for example, may be configured to disable or disallow the display or output of the glucose trend information”).
Hayter is directed to data processing and verifying the stability of analyte sensors. It would have been obvious to one of ordinary skill in the art prior to the effective filling date to incorporate the teachings of Hayter into the combination of claim 13. Doing so would allow the system to stop the collection of data when sensor failure occurs. This would ensure all the obtained data is usable. The incorporation of Hayter into Ou would not change their respective functions.
Response to Arguments
Applicant’s arguments, see page 6 of Applicant Remark's, filed 04/18/2024, with respect to the objections of claims 1, 10, and 13 have been fully considered and are persuasive.  The objections of claims 1, 10, and 13 have been withdrawn.
Applicant’s arguments, see page 6 of Applicant Remark's, filed 04/18/2024, with respect to the 112(b) rejections of claims 8 and 15 have been fully considered and are persuasive.  The U.S.C. 112(b) rejections of claims 8 and 15 have been withdrawn.
Applicant’s arguments, see page , filed 04/18/2024, with respect to the 101 rejections of claims 1-12 have been fully considered and are persuasive.  The U.S.C. 101 rejections of claims 1-12 have been withdrawn.
Applicant’s arguments with respect to the 103 rejections claims 1-6, 8-11, 13-16, and 18-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The Applicant's amendment did not necessitate all the new grounds of rejection presented in this Office action.  Accordingly, THIS ACTION IS MADE NON-FINAL

CITED REFERENCE SUMMARIES: Ref US20160022221A1: Claims:
A computer-implemented method, comprising: storing a baseline data point in a memory wherein the baseline data point is representative of a first point on a glucose control grid; storing an evaluation data point in the memory wherein the evaluation data point is representative of a second point on the glucose control grid; computing a magnitude of a vector that would extend between the baseline data point and the evaluation data point if plotted on the glucose control grid; defining a gradient function over an area of the glucose control grid and determining gradient contour lines around the baseline data point and the evaluation data point; computing a component of the magnitude of the vector between the baseline data point and the evaluation data point that is parallel to a contour line of the defined gradient function; comparing the component of the magnitude of the vector to a predefined threshold value; and displaying, on a system display, an indication that a sensor fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value.
storing a baseline data point in a memory wherein the baseline data point is representative of a first point on a glucose control grid;
storing an evaluation data point in the memory wherein the evaluation data point is representative of a second point on the glucose control grid;
computing a magnitude of a vector that would extend between the baseline data point and the evaluation data point if plotted on the glucose control grid;
defining a gradient function over an area of the glucose control grid and determining gradient contour lines around the baseline data point and the evaluation data point;
computing a component of the magnitude of the vector between the baseline data point and the evaluation data point that is parallel to a contour line of the defined gradient function;
comparing the component of the magnitude of the vector to a predefined threshold value; and
displaying, on a system display, an indication that a sensor fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value.
The computer-implemented method of claim 1 , wherein the baseline data point is determined based on using a baseline median glucose value and a baseline glucose variability value as coordinates for the baseline data point, and further wherein the baseline median glucose value and the baseline glucose variability value are computed by a processor based on a first dataset of glucose values received from a first data communication from sensor electronics operatively coupled to a transcutaneously positioned glucose sensor.
wherein the baseline median glucose value and the baseline glucose variability value are computed by a processor based on a first dataset of glucose values received from a first data communication from sensor electronics operatively coupled to a transcutaneously positioned glucose sensor.
The computer-implemented method of claim 2 , wherein the evaluation data point is determined based on using an evaluation median glucose value and an evaluation glucose variability value as coordinates for the evaluation data point, and further wherein the evaluation median glucose value and the evaluation glucose variability value are computed using the processor based on a second dataset of glucose values received from a second data communication from the sensor electronics operatively coupled to the transcutaneously positioned glucose sensor, where the second dataset of glucose values includes glucose values that are not in the first dataset of glucose values.
wherein the evaluation median glucose value and the evaluation glucose variability value are computed using the processor based on a second dataset of glucose values received from a second data communication from the sensor electronics operatively coupled to the transcutaneously positioned glucose sensor, where the second dataset of glucose values includes glucose values that are not in the first dataset of glucose values.
The computer-implemented method of claim 1 , further comprising displaying, on the system display, the indication that the sensor fault has not been detected if the component of the magnitude of the vector is less than the first predefined threshold value.
The computer-implemented method of claim 1 , wherein displaying, on the system display, the indication that the sensor fault has been detected further includes prompting a user to indicate whether to maintain use of a glucose sensor.
The computer-implemented method of claim 5 , wherein displaying, on the system display, the indication that the sensor fault has been detected further includes requesting a reference glucose measurement for validation.
The computer-implemented method of claim 6 , wherein displaying, on the system display, the indication that the sensor fault has been detected further includes comparing sensor output to the reference glucose measurement relative to a second predefined threshold value.
The computer-implemented method of claim 6 , wherein displaying, on the system display, the indication that the sensor fault has been detected further includes displaying, on the system display, an indication to remove the glucose sensor if the component of the magnitude of the vector is greater than a second predefined threshold value.
A system for determining analyte concentration in blood based on analyte concentration measured in interstitial fluid, the system comprising: a processor; and a memory coupled to the processor, the memory storing processor executable instructions to: store a baseline data point in the memory wherein the baseline data point is representative of a first point on an analyte control grid; store an evaluation data point in the memory wherein the evaluation data point is representative of a second point on the analyte control grid; compute a magnitude of a vector that would extend between the baseline data point and the evaluation data point if plotted on the analyte control grid; define a gradient function over an area of the analyte control grid and determine gradient contour lines around the baseline data point and the evaluation data point; compute a component of the magnitude of the vector between the baseline data point and the evaluation data point that is parallel to a contour line of the defined gradient function; compare the component of the magnitude of the vector to a predefined threshold value; and display, on a system display, an indication that a sensor fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value.
a processor; and
a memory coupled to the processor, the memory storing processor executable instructions to: store a baseline data point in the memory wherein the baseline data point is representative of a first point on an analyte control grid; store an evaluation data point in the memory wherein the evaluation data point is representative of a second point on the analyte control grid; compute a magnitude of a vector that would extend between the baseline data point and the evaluation data point if plotted on the analyte control grid; define a gradient function over an area of the analyte control grid and determine gradient contour lines around the baseline data point and the evaluation data point; compute a component of the magnitude of the vector between the baseline data point and the evaluation data point that is parallel to a contour line of the defined gradient function; compare the component of the magnitude of the vector to a predefined threshold value; and display, on a system display, an indication that a sensor fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value.
store a baseline data point in the memory wherein the baseline data point is representative of a first point on an analyte control grid;
store an evaluation data point in the memory wherein the evaluation data point is representative of a second point on the analyte control grid;
compute a magnitude of a vector that would extend between the baseline data point and the evaluation data point if plotted on the analyte control grid;
define a gradient function over an area of the analyte control grid and determine gradient contour lines around the baseline data point and the evaluation data point;
compute a component of the magnitude of the vector between the baseline data point and the evaluation data point that is parallel to a contour line of the defined gradient function;
compare the component of the magnitude of the vector to a predefined threshold value; and
display, on a system display, an indication that a sensor fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value.
The system of claim 9 , wherein the memory is further configured to store processor executable instructions to: receive a first dataset of analyte values from sensor electronics operatively coupled to a transcutaneously positioned analyte sensor; compute a baseline median analyte value based on the first dataset of analyte values; compute a baseline analyte variability value based on the first dataset of analyte values; and determine the baseline data point based on using the baseline median analyte value and the baseline analyte variability value as coordinates for the baseline data point on the analyte control grid.
receive a first dataset of analyte values from sensor electronics operatively coupled to a transcutaneously positioned analyte sensor;
compute a baseline median analyte value based on the first dataset of analyte values;
compute a baseline analyte variability value based on the first dataset of analyte values; and
determine the baseline data point based on using the baseline median analyte value and the baseline analyte variability value as coordinates for the baseline data point on the analyte control grid.
The system of claim 10 , wherein the memory is further configured to store processor executable instructions to: receive a second dataset of analyte values from the sensor electronics where the second dataset of analyte values includes analyte values that are not in the first dataset of analyte values; compute an evaluation median analyte value based on the second dataset of analyte values; compute an evaluation analyte variability value based on the second dataset of analyte values; and determine the evaluation data point based on using the evaluation median analyte value and the evaluation analyte variability value as coordinates for the evaluation data point on the analyte control grid.
receive a second dataset of analyte values from the sensor electronics where the second dataset of analyte values includes analyte values that are not in the first dataset of analyte values;
compute an evaluation median analyte value based on the second dataset of analyte values;
compute an evaluation analyte variability value based on the second dataset of analyte values; and
determine the evaluation data point based on using the evaluation median analyte value and the evaluation analyte variability value as coordinates for the evaluation data point on the analyte control grid.
The system of claim 9 , wherein the memory is further configured to store processor executable instructions to display, on the system display, the indication that the sensor fault has not been detected if the component of the magnitude of the vector is less than the first predefined threshold value.
The system of claim 9 , wherein the memory is further configured to store processor executable instructions to display, on the system display, the indication that the sensor fault has been detected further includes an instruction to prompt a user to indicate whether to maintain use of a sensor.
The system of claim 13 , wherein the instruction to display, on the system display, the indication that the sensor fault has been detected further includes an instruction to request a reference glucose measurement for validation.
The system of claim 14 , wherein the instruction to display, on the system display, the indication that the sensor fault has been detected further includes an instruction to compare sensor output to the reference glucose measurement relative to a second predefined threshold value.
The system of claim 15 , wherein the instruction to display, on the system display, the indication that the sensor fault has been detected further includes an instruction to display, on the system display, an indication to remove the sensor if the component of the magnitude of the vector is greater than the second predefined threshold value.
A computer-implemented method, comprising: storing an evaluation data point representative of a first point on a glucose control grid; storing a baseline data point representative of a second point on the glucose control grid; computing a magnitude of a vector that extends between the baseline data point and the evaluation data point; defining a gradient function over an area of the glucose control grid including determining at least one gradient contour line around the baseline data point and the evaluation data point; computing a component of the magnitude of the vector between the baseline data point and the evaluation data point that is parallel to a contour line of the defined gradient function; comparing the component of the magnitude of the vector to a predefined threshold value; and displaying, on a system display, an indication that an early signal attenuation (ESA) fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value.
storing an evaluation data point representative of a first point on a glucose control grid;
storing a baseline data point representative of a second point on the glucose control grid;
computing a magnitude of a vector that extends between the baseline data point and the evaluation data point;
defining a gradient function over an area of the glucose control grid including determining at least one gradient contour line around the baseline data point and the evaluation data point;
displaying, on a system display, an indication that an early signal attenuation (ESA) fault has been detected if the component of the magnitude of the vector is greater than a first predefined threshold value.
The computer-implemented method of claim 17 , further comprising: receiving during an early wear period, a first dataset of glucose values from sensor electronics operatively coupled to a transcutaneously positioned glucose sensor; computing an evaluation median glucose value based on the first dataset of glucose values; computing an evaluation glucose variability value based on the first dataset of glucose values; and determining the evaluation data point based on using the evaluation median glucose value and the evaluation glucose variability value as coordinates for the evaluation data point on the glucose control grid.
receiving during an early wear period, a first dataset of glucose values from sensor electronics operatively coupled to a transcutaneously positioned glucose sensor;
computing an evaluation median glucose value based on the first dataset of glucose values;
computing an evaluation glucose variability value based on the first dataset of glucose values; and
determining the evaluation data point based on using the evaluation median glucose value and the evaluation glucose variability value as coordinates for the evaluation data point on the glucose control grid.
The computer-implemented method of claim 18 , further comprising: receiving after the early wear period a second dataset of glucose values from the sensor electronics where the second dataset of glucose values does not include glucose values that are also in the first dataset of glucose values; computing a baseline median glucose value based on the second dataset of glucose values; computing a baseline glucose variability value based on the second dataset of glucose values; and determining the baseline data point based on using the baseline median glucose value and the baseline glucose variability value as coordinates for the baseline data point on the glucose control grid.
receiving after the early wear period a second dataset of glucose values from the sensor electronics where the second dataset of glucose values does not include glucose values that are also in the first dataset of glucose values;
computing a baseline median glucose value based on the second dataset of glucose values;
computing a baseline glucose variability value based on the second dataset of glucose values; and
determining the baseline data point based on using the baseline median glucose value and the baseline glucose variability value as coordinates for the baseline data point on the glucose control grid.
The computer-implemented method of claim 17 , further comprising displaying, on the system display, the indication that the early signal attenuation (ESA) fault has not been detected if the component of the magnitude of the vector is less than the first predefined threshold value.
The computer-implemented method of claim 17 , wherein displaying, on the system display, the indication that the early signal attenuation (ESA) fault has been detected further includes prompting a user whether to maintain use of a glucose sensor.
The computer-implemented method of claim 21 , wherein displaying, on the system display, the indication that the early signal attenuation (ESA) fault has been detected further includes requesting a reference glucose measurement for validation.
The computer-implemented method of claim 22 , wherein displaying, on the system display, the indication that the early signal attenuation (ESA) fault has been detected further includes comparing sensor output to the reference glucose measurement relative to a second predefined threshold value.
The computer-implemented method of claim 23 , wherein displaying, on the system display, the indication that the early signal attenuation (ESA) fault has been detected further includes displaying, on the system display, an indication that a first dataset of glucose values are invalid.
The computer-implemented method of claim 17 , further comprising storing the evaluation data point as the baseline data point if no fault is detected for the evaluation data point.
The computer-implemented method of claim 25 , further comprising comparing the evaluation data point against multiple baseline data points.
Ref US20160296148A1: Claims:
A method, comprising: prior to calibrating an in vivo analyte sensor, retrieving one or more parameters to calibrate the analyte sensor data; analyzing the retrieved one or more parameters to determine whether the analyte sensor data calibration will fail; and generating a notification based on the analysis of the retrieved one or more parameters.
prior to calibrating an in vivo analyte sensor, retrieving one or more parameters to calibrate the analyte sensor data;
analyzing the retrieved one or more parameters to determine whether the analyte sensor data calibration will fail; and
generating a notification based on the analysis of the retrieved one or more parameters.
The method of claim 1 including determining a calibration time period based on a predetermined calibration schedule.
The method of claim 2 wherein the predetermined calibration schedule includes a plurality of calibration time periods after the positioning of the analyte sensor in fluid contact with an analyte.
The method of claim 3 wherein the plurality of calibration time periods includes two or more of 10 minutes, 30 minutes, 45 minutes, one hour, two hours, five hours, 10 hours, 12 hours, 24 hours, 48 hours or 72 hours measured from the positioning of the analyte se
[Truncated to input limit]