Office Action 2: Non-Final Rejection (2024-08-05)
Rejection types: 103 - Obviousness

CLAIMS:
AMENDMENTS TO THE CLAIMS

This listing of claims replaces all previous versions of the claims in the application.

1\. (Currently Amended) 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, 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 a 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 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;
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;
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, that the sum of analyte levels metric exceeds the second threshold, and that a difference between the first time and the second time is less than a predetermined time threshold,generate an indication of a sensor fault.
2\. (Previously Presented) 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\. (Previously Presented) 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\. (Previously Presented) 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\. (Previously Presented) 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\. (Previously Presented) 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\. (Previously Presented) 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\. (Previously Presented) 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\. (Previously Presented) 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\. (Currently Amended) The analyte monitoring system of claim 9, wherein the notification comprises a visual or auditory indication that \[\[a\]\] the sensor fault was detected.

11\. (Previously Presented) 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\. (Canceled)

13\. (Currently Amended) 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, wherein the rate of change metric is calculated at 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;
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, that the sum of analyte levels metric exceeds the second threshold, and that a difference between the first time and the second time is less than a predetermined time threshold, generating an indication of a the sensor fault.

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

15\. (Previously Presented) 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\. (Previously Presented) The method of claim 13, wherein the rate of change metric is a central tendency of a plurality of rate of change values.

17\. (Canceled)

18\. (Previously Presented) 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\. (Previously Presented) The method of claim 18, further comprising outputting a notification to a display of the reader device.

20\. (Previously Presented) The method of claim 13, further comprising disabling the analyte sensor.

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

APPLICANT REBUTTAL: REMARKS
Reconsideration is respectfully requested. Claims 1-11, 13-16, and 18-20 are pending. Claims 12 and 17 remain canceled without prejudice. No new matter has been introduced. For the reasons set forth below, it is respectfully submitted that the pending claims are allowable.
REJECTIONS UNDER SECTION 103
Claims 1-6 and 8-10 were rejected under 35 U.S.C. § 103 as being allegedly unpatentable over US20160022221 ("Ou") in view of CN108544925 ("Xiong"). See 8/5/24 NFOA at 3-7. Claim 7 was rejected under 35 U.S.C. § 103 as being allegedly unpatentable over Ou in view of Xiong and further in view of W002058537 ("Heller"). See id. at 7. Claims 11, 13-16, and 18-20 were rejected under 35 U.S.C. § 103 as being allegedly unpatentable over Ou in view of Xiong and further in view of U.S. Publ. No. 2016/0296148 ("Hayter"). See id. at 7-11. For the reasons set forth below, it is respectfully submitted that the cited references, either alone or in combination, do not render the pending claims obvious.
A. Xiong is Not Properly Combinable with Ou
First, it is respectfully submitted that the cited references do not render the pending claims obvious because Xiong is not properly combinable with Ou. In particular, Xiong is non-analogous art. It is well-established law that art is non-analogous unless it is: (1) from the same field of endeavor as the claimed invention; or (2) reasonably pertinent to the particular problem faced by the inventor. In re Bigio, 381 F.3d 1320, 1325 (Fed. Cir. 2004); In re Wood and Eversole, 599 F.2d 1032, 1036 (CCPA 1979). Moreover, art is "reasonably pertinent" to a particular problem when it would "logically commend itself' to an inventor's attention in considering the problem at hand. In re Icon Health and Fitness, Inc., 496 F.3d 1374, 1379-80 (Fed. Cir. 2007) (citing In re Clay, 966 F.2d 656, 658-59 (Fed. Cir. 1992)); see also MPEP § 2141.01(a). Conversely, when art is directed to a different purpose than a claimed invention, an inventor would have less motivation or occasion to consider it. See In re Clay, 966 F.2d at 658-59.
Here, Xiong is directed to a battery management system of an electric automobile. Xiong at 2. By contrast, the embodiments of the present application comprise "systems, devices and methods for detecting a sensor fault in an analyte sensor." U.S. Publ. No. 2021/0219879 ("the '879 Publication"), Abstract. As such, it is respectfully submitted that Xiong encompasses subject matter
from a completely different field of endeavor than the embodiments of the present application. With respect to the particular problem being solved, Xiong focuses on utilizing a high real-time vehicle battery management unit and a cloud computing system based on battery history data to address issues with the incomplete functionality of battery management systems in electric vehicles. See Xiong at 2. By contrast, the embodiments of the present application are designed to solve the problem of detecting sensor faults in analyte sensors using one or more calculated metrics, and one or more thresholds based on the calculated metrics. See '879 Publication, Para. [0008]. Thus, Xiong neither addresses the same problem nor serves the same purpose as the analyte monitoring systems or the methods of the current claims. Xiong would not have logically commended itself to Applicants' attention as being analogous.
Accordingly, it is respectfully submitted that Xiong is neither from the same field of endeavor as the claimed subject matter, nor is Xiong's battery management system reasonably pertinent to the problem faced by the embodiments of the instant application. See In re Bigio, 381 F.3d at 1325. As such, it is not properly combinable with Ou, and it is respectfully requested that the Section 103 rejections be withdrawn.
B. The Cited References, Alone or in Combination, Fail to Teach, Describe or Suggest
the Claimed Subject Matter of Independent Claims 1 and 13.
Second, even assuming arguendo that the cited references are properly combinable (which they are not), it is respectfully submitted that they do not teach, describe, or suggest all of the limitations of independent claims 1 and 13.
Independent claims 1 and 13, recite (with emphasis added):
1.    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, 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 a 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 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;
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;
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, that the sum of analyte levels metric exceeds the second threshold, and
that a difference between the first time and the second time is less than a
predetermined time threshold, generate an indication of a sensor fault.
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, wherein the rate of change metric is calculated at 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;
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, that the sum of analyte levels metric exceeds the second threshold, and
that a difference between the first time and the second time is less than a predetermined time threshold, generating an indication of the sensor fault.
It is respectfully submitted that the cited references fail to disclose generating an indication of sensor fault in response to determining "that a difference between the first time and the second time is less than a predetermined time threshold."
The 8/5/24 NFOA acknowledges that "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." 8/5/24 NFOA at 5. The 8/5/24 NFOA relies on Xiong for allegedly disclosing this limitation. Id. Specifically, the 8/5/24 NFOA cites to paragraph 3 of page 6 of Xiong, which states: "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." Id. While Xiong mentions "a time threshold" and "the duration time exceeds the time threshold," Xiong is silent regarding generating a sensor fault in response to determining a difference between the first time and the second time is less than a predetermined time threshold.
The remaining references are cited for other purposes, and do not teach, describe or suggest the claim limitation "in response to a determination . . . that a difference between the first time and the second time is less than a predetermined time threshold, generate an indication of a sensor fault."
Accordingly, the cited references, either alone or in combination, fail to disclose all of the limitations of independent claims 1 and 13, and it is requested that the Section 103 rejection to claims
1 and 13 be withdrawn, as well as to all claims that depend therefrom.
CONCLUSION

NEXT OFFICE ACTION: REASONS FOR ALLOWANCE
The following is an examiner’s statement of reasons for allowance:
The prior art does not teach or suggest “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, and that a difference between the first time and the second time is less than a predetermined time threshold, generate an indication of a sensor fault.”.
Regarding claim 1, the closest prior art, US 20160022221 A1 (Hereinafter Ou) (previously cited), 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 teach or suggest 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.
Claims 2-11 are allowable by virtue of their dependence from claim 1.
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 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.
Claims 14-16 and 18-20 are allowable by virtue of their dependence from claim 13.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”.
Conclusion

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 CN108544925A: Claims:
A battery management system, comprising a vehicle battery management unit and a cloud computing system based on battery history data; The cloud computing system estimates the internal short-circuit resistance of the battery; The vehicle battery management unit includes: The battery model parameter identification module obtains model parameters; the model parameters include ohmic internal resistance, polarization internal resistance and polarization capacitance; The fault type judgment module judges whether it is a suspected battery fault or a suspected sensor fault; Sensor fault detection and online calibration module, which can judge the fault of voltage sensor, current sensor and temperature sensor when the sensor is suspected to be faulty; The model-based short-circuit fault diagnosis module realizes the diagnosis of the internal short-circuit fault of the battery in combination with the internal short-circuit resistance value of the battery.
A battery management system, comprising a vehicle battery management unit and a cloud computing system based on battery history data;
The cloud computing system estimates the internal short-circuit resistance of the battery;
The vehicle battery management unit includes:
The battery model paramete
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