DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
2. Claims 1-20 are currently pending and under exam herein.
Claim 5 is objected to.
Claims 1-20 are rejected.
Priority
3. The claimed benefit of US provisional patent application no 63/265,324 filed on 13 December 2021 is acknowledged. In this action, all claims are examined as though they had an effective filing date of 13 December 2021. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
4. The information disclosure statements (IDSs) submitted on 03 April 2023 and 21 December 2023 is acknowledged and are being considered by the examiner.
Drawings
5. The drawing submitted on 13 December 2022 are accepted by the examiner.
Claim Objections
6. Claim 5 is objected to because of the following informalities:
Claim 5 recites: “a first waited end-of-life risk factor value”, which should read “a first weighted end-of-life risk factor value.
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.
7. Claim 17 is 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.
Claim 17 recites “determining that the downward drift in sensor sensitivity is increasing if the second slope is greater than the first slope”. Claim 17 depends on claim 16, which states that each slope is between a “local maxima” value and a “local minima value”. Claim 17 is indefinite because the comparison direction in claim 16 is unclear, so it is unclear if the slopes are negative or positive. For the purposes of examination, and with broadest reasonable interpretation, it will be considered that the slopes can be either positive or negative (i.e. the differences in rise of the slopes can either be calculated as a (maxima minus minima) or (minima minus maxima)). Therefore, ‘determining that the downward drift is increasing’ will be considered to occur when the second slope is either greater or less than the first slope.
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.
8. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 recites: evaluating a plurality of risk factor metrics associated with end-of-life symptoms of analyte sensors, at least a portion of the plurality of risk factor metrics being based on the sensor data, and the plurality of risk factor metrics comprising a downward drift in sensor sensitivity over time, an amount of non-symmetrical, non- stationary noise, and a duration of noise
Claim 1 recites: determining the end-of-life status of the analyte sensor based at least in part on the evaluating
Claim 2 recites: the method of claim 1, further comprising translating a first risk factor metric of the plurality of risk factor metrics to a first end-of-life risk factor value, the first end-of-life risk factor value indicating a likelihood that the first risk factor metric indicates that the continuous analyte sensor is in an end-of-life state
Claim 3 recites: the method of claim 2, further comprising translating a second risk factor metric of the plurality of risk factor metrics to a second end-of-life risk factor value, the translating of the first risk factor metric being performed with a first translation function, and the translating of the second risk factor metric being performed with a second translation function different than the first translation function
Claim 4 recites: the method of claim 3, wherein at least one of the first translation function or the second translation function is a logistic regression function
Claim 5 recites: the method of claim 3, further comprising: assigning a first weight to the first end-of-life risk factor value
Claim 5 recites: to determine a first waited end-of-life risk factor value
Claim 5 recites: assigning a second weight to a second end-of-life risk factor value
Claim 5 recites: to determine a second waited end-of-life risk factor value
Claim 5 recites: determining a weighted average using the first waited end-of-life risk factor value and the second waited end-of-life risk factor value
Claim 6 recites: the method of claim 5, wherein the determining further comprises: determining that the weighted average meets a threshold
Claim 6 recites: based on the determining that the weighted average meets the threshold, determining that the continuous analyte sensor is in an end-of-life state
Claim 8 recites: the method of claim 1, wherein the plurality of risk factor metrics further comprises a rate of change of the downward drift in sensor sensitivity
Claim 9 recites: the method of claim 8, further comprising translating the plurality of risk factor metrics to an end-of-life likelihood using a common translation function
Claim 10 recites: the method of claim 9, wherein the translation function is a logistic regression function
Claim 11 recites: the method of claim 1, further comprising determining the downward drift in sensor sensitivity over time at least in part by examining a ratio of a shorter-term sensitivity to a longer term sensitivity
Claim 12 recites: the method of claim 1, further comprising determining the downward drift in sensor sensitivity over time at least in part by: down sampling the sensor data to generate a downed sample signal
Claim 12 recites: periodically determining a slope between selected data points in the down sampled signal to obtain a series of calculated slopes
Claim 12 recites: periodically comparing a currently calculated slope to a previously calculated slope
Claim 12 recites: determining that sensor sensitivity is decreasing if a ratio of the currently calculated slope to the previously calculated slope decreases over time
Claim 13 recites: the method of claim 1, further comprising determining a downward drift in sensor sensitivity over time at least in part by: filtering the sensor data with a slow low pass filter having a lower cutoff frequency than a fast low pass filter to obtain slow filtered sensor data
Claim 13 recites: filtering the sensor data with the fast low pass filter to obtain fast filtered sensor data
Claim 13 recites: examining a ratio of a difference between the fast filtered sensor data and the slow filtered sensor data to the slow filtered sensor data, wherein a downward drift in sensor sensitivity over time is more likely as the ratio becomes more negative
Claim 14 recites: the method of claim 1, further comprising applying a classifier model to generate an end-of-life likelihood for the continuous analyte sensor based at least in part on the plurality of risk factor metrics, the end-of-life status of the analyte sensor being based at least in part on the end-of-life likelihood
Claim 15 recites: the method of claim 1, further comprising applying a plurality of sequential processing layers to the sensor data, the applying of a first processing layer of the plurality of sequential processing layers comprising applying a first filter to the sensor data to generate a first filter output and down sampling the first filter output to generate a first layer output
Claim 15 recites: the applying of a second processing layer of the plurality of sequential processing layers comprising applying a second filter to the first layer output to generate a second filter output
Claim 15 recites: down sampling the second filter output to generate a second layer output, at least one of the plurality of risk factor metrics being based at least in part on the second layer output
Claim 16 recites: the method of claim 1, further comprising determining the downward drift in sensor sensitivity over time at least in part by examining a rate of change in sensor sensitivity
Claim 16 recites: the examining of the rate of change in sensor sensitivity comprises: filtering the sensor data with a low pass filter to obtain filtered sensor data
Claim 16 recites: for a first moving window, determining a first slope between a local maxima value of the filtered sensor data in the first moving window and a local minima value of the filtered sensor data in the first moving window
Claim 16 recites: for a second moving window after the first moving window, determining a second slope between a local maxima value of the filtered sensor data in the second moving window and a local minima value of the filtered sensor data in the second moving window
Claim 16 recites: determining a rate of change in sensor sensitivity based on the first slope and the second slope.
Claim 17 recites: the method of claim 16, wherein determining the rate of change in sensor sensitivity comprises determining that the downward drift in sensor sensitivity is increasing if the second slope is greater than the first slope
Claim 18 recites: the method of claim 1, further comprising: determining a noise measure describing the sensor data
Claim 18 recites: determining, based at least in part on the end-of-life status of the analyte sensor and the noise measure that the sensor data will not generate a reliable output
Claim 19 recites: evaluating a second plurality of risk factor metrics at least a portion of the second plurality of risk factor metrics being based at least in part on the second sensor data
Claim 19 recites: determining a second end-of-life status of the analyte sensor based at least in part on the evaluating
Claim 19 recites: determining a second noise measure describing the second sensor data
Claim 19 recites: determining, based at least in part on the second end-of-life status of the analyte sensor and the second noise measure that the second sensor data will generate a reliable output
Claim 20 recites: evaluating a plurality of risk factor metrics associated with end-of-life symptoms of analyte sensors, at least a portion of the plurality of risk factor metrics being based on the sensor data, and the plurality of risk factor metrics comprising a downward drift in sensor sensitivity over time, an amount of non-symmetrical, non- stationary noise, and a duration of noise
Claim 20 recites: determining an end-of-life status of the analyte sensor based at least in part on the evaluating
The limitations regarding ‘translating a first risk factor metric to a value’(optionally using a logistic regression function), ‘translating a second risk factor metric to a value’ (optionally using a logistic regression function), ‘determine a risk factor value‘, determining a weighted average’, ‘translating the plurality of risk factor metrics to a likelihood’, ‘determining a slope’, ‘applying a classifier model to generate a likelihood’, ‘determining a first slope’, ‘determining a second slope’, ‘determining a rate of change’, ‘determining that the downward drift in sensor sensitivity is increasing’, are verbal equivalents that describe a mathematical calculation that is performed as the limitation and are so simple that they could be performed (in the human mind) with pen and paper. Therefore, these limitations fall under the "Mathematical concepts" and "Mental processes" groupings of abstract ideas.
Mental processes
The remaining limitations for ‘evaluating a plurality of risk factor metrics’, ‘determining the end-of-life status’, ‘assigning a first weight’, ‘assigning a second weight’, ‘determining that the weighted average meets a threshold’, ‘determining that the continuous analyte sensor is in an end-of-life state’, ‘examining a ratio’, ‘down sampling the sensor data’, ‘comparing a currently calculated slope to a previously calculated slope’, ‘determining that sensor sensitivity is decreasing’, ‘filtering the sensor data’, ‘filtering the sensor data’, ‘examining a ratio’, ‘applying a first filter’, ‘applying a second filter’, ‘down sampling the second filter output’, ‘examining a rate of change in sensor sensitivity’, ‘filtering the sensor data with a low pass filter’, ‘determining a noise measure’, ‘determining that the sensor data will not generate a reliable output’, ‘evaluating a second plurality of risk factor metrics’, ‘determining a second end-of-life status’, ‘determining a second noise measure’, ‘determining that the second sensor data will generate a reliable output’, ‘evaluating a plurality of risk factor metrics’, ‘determining an end-of-life status’ are generically recited data analysis steps that can be practically performed in the human mind because the human mind is capable of identifying relevant information, comparing values, and determining information from other values.
While claim 20 recites performing some aspects of the analysis with a ‘processor circuit’, there are no additional limitations that indicate that this processor requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the "Mental processes" grouping of abstract ideas. As such, claims 1-20 recite an abstract idea (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or insignificant extra-solution activity. Specifically, the claims recite the following additional elements:
Claim 1 recites: receiving sensor data from an analyte sensor
Claim 1 recites: providing an output related to the end-of-life status of the analyte sensor
Claim 7 recites: the method of claim 6, further comprising initiating termination of sensor use
Claim 18 recites: responsive to determining that the sensor data will not generate a reliable output, suspending display of at least one value based on the sensor data
Claim 19 recites: the method of claim 18, further comprising: after suspending display of at least one value based on the sensor data, receiving second sensor data from the analyte sensor
Claim 19 recites: responsive to determining that the second sensor data will generate a reliable output, displaying at least one value based on the second sensor data
Claim 20 recites: a continuous analyte sensor system comprising a processor circuit programmed to perform operations
Claim 20 recites: receiving sensor data from an analyte sensor
Claim 20 recites: providing an output related to the end-of-life status of the analyte sensor
The limitations for ‘receiving sensor data’ and ‘receiving second sensor data’ merely serve to gather data that is used an input for the judicial exception. Therefore, these limitations are mere data gathering activities. As set forth in MPEP 2106.05(g), mere data gathering activity has been identified by the courts as insignificant extra-solution activity that does not provide a practical application.
Similarly, the limitations for ‘displaying at least one value’ and ‘providing an output’ equate to outputting data that does not meaningfully limit the abstract idea. As set forth in MPEP 2106.05(g), necessary data outputting has been identified in the courts as insignificant extra-solution activity that does not provide a practical application.
There are no limitations that indicate that the ‘processor circuit’ requires anything other than a generic computing system. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The limitation for ‘initiating termination of sensor use’ (claim 7) is post-analysis activity that is stated so broadly that it encompasses simply sending a recommendation to the user (as indicated in the specification para. 0331) rather than affecting the senor electronics or operation. In addition, the termination is a routine operation that is not claimed to be linked as a response to the end-of-life state status. Therefore, the limitation does not integrate the judicial exception into a practical application. If the ‘initiating termination of sensor use’ was clearly claimed as affecting the function of the sensor (i.e. terminating the use of the sensor) in response to the determining that the sensor is in an end-of-life state, this claim could be considered a practical application.
The limitation for ‘suspending display’ when it is determined that the sensor will not generate a reliable output and ‘displaying a value’ when it is determined that the sensor will generate a reliable output (claim 18 and dependent claim 19) are post-analysis actions that fail to integrate the abstract idea into a practical application because failure determination simply affects the display and does not actively change the operation of the monitoring device (for example by terminating the sensor use).
The above recited additional elements do not provide a practical application of the
recited judicial exception. As such, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing environment or well-understood, and conventional activity.
As discussed above, there are no additional limitations to indicate that the claimed hardware computer ‘processor circuit’ requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
Limitations that merely add an insignificant extra-solution activity, do not amount to an inventive concept, particularly when the activities are well-understood and conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). Storing data and retrieving information in memory are well-understood, routine, conventional computer functions as recognized by Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
The limitation to ‘initiate termination of a sensor’ is recited functionally with no technical details, thus it is a routine control action. Additionally, ‘initiating termination of a sensor’ and ‘providing an output’ from a sensor was well understood, routine and conventional at the time of the effective filing date of the invention as evidenced by Hermayer et al. (Curr Diab. Rep. 2015, Vol. 15, p. 1-10). Hermayer et al. discloses that for glucose monitoring systems, normally, two-level (low and high) controls are run once every 24 h when a meter is in use for patient testing at a frequency required by the manufacturers and/or regulatory agencies. Meters usually lock out from patient testing if quality control (QC) fails or is not performed (p. 9, col. 1, para. 3). Hermayer et al. further disclose that glucose meter manufacturers offer barcoded vials and test strips that produce an error message on the glucometer if expired strips are being used (p. 6, col. 1, para. 3).
The limitation of ‘suspending the display’ of the sensor data was well understood, routine and conventional at the time of the effective filing date of the invention as evidenced by Koschinsky et al. (Diabetes Metab. Res. Rev. 2021, Vol. 17, p. 113-123). Koschinsky et al. discloses that the first generation of the glucose sensor marketed by MiniMed did not display the current glucose concentration measured, but stored the data for up to 3 days (‘Professional System’) so that the physician could read out the retrospectively calibrated data and discuss them with the patients (p. 118, col. 1, para. 2).
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-20 are not patent eligible.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
9. Claims 1-3, 8, 11 and 16-20 are rejected under 35 U.S.C. 102 (a)(1) and 35 U.S.C. (a)(2) as being anticipated by Vanslyke et al. (US20150351670A1). The italicized text corresponds to the instant claim limitations.
With respect to claim 20, Vanslyke et al. teaches an electronic devise for monitoring data associated with a physiological condition, including a continuous analyte sensor, and a processor module configured to perform a method of discriminating faults responsively (para. 0002, 0022; A continuous analyte sensor system comprising a processor circuit programmed to perform the method).
Regarding claims 1 and 20, Vanslyke et al. discloses a method for discriminating a fault type in a continuous in vivo analyte monitoring system, including receiving a signal from an analyte monitor (para. 0011; receiving sensor data from an analyte sensor).
Regarding claims 1 and 20, Vanslyke et al. discloses that a plurality of risk factors may be evaluated that are indicative of sensor end of life (EOL), for example using risk factor instruction(s), algorithm(s) and/or function(s) (para. 0290; evaluating a plurality of risk factor metrics associated with end-of-life symptoms of analyte sensors).
With respect to claims 1 and 20, Vanslyke et al. discloses that suitable risk factors may include sensor sensitivity or whether there has been a decrease in signal sensitivity (para. 0292; at least a portion of the plurality of risk factor metrics being based on the sensor data).
Regarding claims 1 and 20, Vanslyke et al. discloses that another risk factor that may be useful in the determination of EOL is sensor sensitivity or whether there has been a decrease in signal sensitivity (e.g., change in amplitude and/or variability of the sensitivity of the sensor compared to one or more predetermined criteria), including magnitude and history. Vanslyke et al. further discloses that towards the EOL, the sensitivity of the sensor to changes in glucose may decrease and that this reduction may be recognized as a drop in sensitivity that occurs monotonically over several hours. Vanslyke et al. further discloses that sensor sensitivity may be computed by comparing sensor data (e.g., calibrated sensor data) with reference blood glucose (BG). For example, calibration algorithms adjust the glucose estimates based on the systematic bias between sensor and a reference BG. EOL algorithms may use this bias, called error at calibration or downward drift, to quantify or qualify EOL symptoms. Vanslyke et al. further discloses the error at calibration may be normalized to account for irregular calibration times and smoothed to give more weight to recent data (e.g., moving average or exponential smoothing). This weighting to recent data and moving average indicate the drift in sensitivity is measured over time (para. 0296; 0304; the plurality of risk factor metrics comprising a downward drift in sensor sensitivity over time).
Regarding claims 1 and 20, Vanslyke et al. discloses that end-of-life (EOL) risk factors include noise analysis including measuring factors like skewness, spikiness and rotations, and that the processor module may be configured to evaluate the noise (e.g., amplitude, duration and/or pattern) to determine if there is a predetermined noise pattern indicative of EOL. The measurement of skewness equates to measurement of non-symmetrical noise and the measurement of ‘duration’ and ‘spike activity rate’ equates to capturing measurement of non-stationary noise. Vanslyke et al. further discloses that a risk factor for EOL could be increased spike activity, which can be detected using various methods of spike detection (e.g. by computing the mean rate of negative change) and that an EOL noise pattern may include a series of specific negative spikes in a short time frame (i.e. skewed or non-symmetrical noise). Vanslyke et al. further discloses that the term ‘noise’ includes transient signal artifacts (i.e. non-stationary). Fig. 30 shows examples of non-symmetrical and non-stationary noise at end of life (para. 0105; 0292; 0305, 0308; Fig. 30; the plurality of risk factor metrics comprising an amount of non-symmetrical, non- stationary noise).
With respect to claims 1 and 20, Vanslyke et al. discloses that In some embodiments, the duration of the noise may be indicative of EOL and that the EOL algorithm may penalize the longer duration noise more. Vanslyke et al. further discloses that at each sample time, total duration of noise up to the point is used to calculate the EOL risk factor value at that point (para. 0306; the plurality of risk factor metrics comprising a duration of noise).
Regarding claims 1 and 20, Vanslyke et al. discloses that the processor module is configured to run probability functions to determine a probability of EOL and/or a likelihood of recovery for one or more of the plurality of EOL risk factors. Vanslyke et al. further discloses that the system may be configured to determine an EOL status by using a likelihood or probability analysis to determine an EOL status of the sensor. Vanslyke et al. further discloses that the outputs of the risk factors become inputs into an EOL determination process (e.g. the outputs of the risk factors may be mapped to EOL risk factor values, probability or likelihood scores, and then the EOL risk factor values become inputs into the EOL determination function. Vanslyke et al. further disclose that linear discriminant analysis (LDA) may be used as the EOL determination function, by taking the input variables and providing an output decision. (para. 0323, 0327; determining the end-of-life status of the analyte sensor based at least in part on the evaluating; and providing an output related to the end-of-life status of the analyte sensor).
Regarding claim 2, Vanslyke et al. discloses an example of translating a first risk factor metric into a first end-of life-risk factor value in 3 steps: 1) . loss of sensor sensitivity may be computed by calculating a short-term average of the sensor output and normalizing it by the expected longer term average sensor sensitivity; 2) If the ratio of short term to long term sensitivity is smaller than 70%, there may be a risk of sensor losing sensitivity; and 3) Loss of sensitivity may be translated into an EOL risk factor value, for example a value of about 1 until the ratio is about 70%, reducing to 0.5 at 50% and <0.1 at 25%. Vanslyke et al. further discloses that EOL risk factor values can indicate likelihood of recovery or probability of sensor failure in the future and that the outputs of the risk factors become inputs into an EOL determination process. For example, the outputs of the risk factors may be mapped to EOL risk factor values, for example values from 0 to 1, probability or likelihood scores, actual values (outputs from the risk factor evaluation(s)), and/or the like (para. 0302, 0295, 0322-0323, 0327; the method of claim 1, further comprising translating a first risk factor metric of the plurality of risk factor metrics to a first end-of-life risk factor value, the first end-of-life risk factor value indicating a likelihood that the first risk factor metric indicates that the continuous analyte sensor is in an end-of-life state).
Pertaining to claim 3, Vanslyke et al. discloses an example of translating a second risk factor metric (a series of specific negative spikes in a short timeframe) into a second end-of-life risk-factor value in 4 steps: 1) spikes are detected as point-to-point differences; 2) an algorithm is used that outputs a +1 for an upward spike and a -1 for a downward spike; 3) EOL detection is performed using these algorithms is done by calculating a moving average of spike time series using a negative threshold (e.g. using a value of 2 times for negative versus positive spikes) and 4) determining an EOL risk factor value, which may be 1 for a value of a spike metric <1, 0.5 for a spike metric >2, and <0.1 for spike metric >5 and so on. This translation function is different from the first function described in the claim 2 section (para. 0308-0310; the method of claim 2, further comprising translating a second risk factor metric of the plurality of risk factor metrics to a second end-of-life risk factor value, the translating of the first risk factor metric being performed with a first translation function, and the translating of the second risk factor metric being performed with a second translation function different than the first translation function).
Pertaining to claim 8, Vanslyke et al. discloses that in some embodiments, the sensitivity variable in the EOL function is based on a trend of sensitivity during a particular sensor session (e.g., during the life of the sensor in the host). For example, the determination of whether there has been a decrease in signal sensitivity includes comparing a first measured signal sensitivity at a first time point against a second measured signal sensitivity at a second time point to determine if rate of change in the measured signal sensitivity is within an acceptable range. Vanslyke et al. further discloses that a rate of acceleration (e.g., rate of drop of sensitivity) of greater than 20% over 12 hours may be an indicator of EOL and can be useful as an input in the EOL detection algorithm (para. 0298-0299; the method of claim 1, wherein the plurality of risk factor metrics further comprises a rate of change of the downward drift in sensor sensitivity).
Pertaining to claim 11, Vanslyke et al. teaches that in some embodiments, sensor sensitivity may be computed using an analysis of uncalibrated sensor data; for example, computing a slow-moving average or median of raw count can be used to determine if the average starts showing negative trends, which indicates that the sensor may be losing sensitivity. Vanslyke et al. further disclose that loss of sensitivity may be computed by calculating a short term (e.g. ˜6-8 hours) average (or median) of the sensor output and normalizing it by the expected longer term (48 hours) average sensor sensitivity, and if the ratio of short term to long term sensitivity is smaller than 70%, there may be a risk of sensor losing sensitivity. Vanslyke et al. further discloses that loss of sensitivity may be translated into an EOL risk factor value, for example a value of about 1 until the ratio is about 70%, reducing to 0.5 at 50% and <0.1 at 25% (para. 0302; the method of claim 1, further comprising determining the downward drift in sensor sensitivity over time at least in part by examining a ratio of a shorter-term sensitivity to a longer-term sensitivity).
Pertaining to claim 16, Vanslyke et al. teaches detecting faults related to drifts in sensitivity, wherein a drift can either be of the quantities m or b (where m is slope in the equation y=mx+b and represents the sensitivity of the sensor). Vanslyke et al. further teaches determination of whether there has been a decrease in signal sensitivity by comparing a first measured signal sensitivity at a first time point against a second measured signal sensitivity at a second time point to determine if rate of change in the measured signal sensitivity is within an acceptable range. Vanslyke et al. further teaches that the rate of change of signal sensitivity may be determined based in part on a slow-moving average of raw sensor data (i.e. keeping slower trends or low-frequency components) (para. 0298-0299; 0341; the method of claim 1, further comprising determining the downward drift in sensor sensitivity over time at least in part by examining a rate of change in sensor sensitivity, the examining of the rate of change in sensor sensitivity comprises: filtering the sensor data with a low pass filter to obtain filtered sensor data; for a first moving window, determining a first slope between a local maxima value of the filtered sensor data in the first moving window and a local minima value of the filtered sensor data in the first moving window; for a second moving window after the first moving window, determining a second slope between a local maxima value of the filtered sensor data in the second moving window and a local minima value of the filtered sensor data in the second moving window; and determining a rate of change in sensor sensitivity based on the first slope and the second slope.
Pertaining to claim 17, Vanslyke et al. discloses that in one example, a slow-moving average or median of raw count starts showing negative trends, the sensor may be losing sensitivity. (para. 0302; the method of claim 16, wherein determining the rate of change in sensor sensitivity comprises determining that the downward drift in sensor sensitivity is increasing if the second slope is greater than the first slope.
Regarding claim 18, Vanslyke et al. discloses that the end-of-life (EOL) status is determined using a likelihood or probability analysis using the risk factor values as inputs. Vanslyke et al. further discloses that decision functions may be used to provide a Fused Bayesian likelihood estimate based on multiple risk factors that are measured and fused together. Examples of factors that contribute to the status calculation include: 1) if sensor sensitivity decreased over a fixed amount of time (yes or no) and 2) if sensor has had severe noise (above a predetermined threshold level) for more than 12 hours of the last 24 hours (yes or no). Vanslyke et al. further discloses that these individual likelihood values are multiplied together for a final fused likelihood value the ability of each individual variable to separate EOL from non-EOL. Vanslyke et al. further discloses that EOL determination function may determine the EOL status is a high probability that the senor will not track glucose in the future, and that in some embodiments, the processor module is configured to suspend display of sensor data during verification or determination of a likelihood of recovery, after which the processor module may be configured to either re-allow display of sensor data if it is determined that the sensor has recovered from the EOL symptoms (para. 0327-0330, 0335; the method of claim 1, further comprising: determining a noise measure describing the sensor data; determining, based at least in part on the end-of-life status of the analyte sensor and the noise measure that the sensor data will not generate a reliable output).
Pertaining to claim 18, Vanslyke et al. discloses that if the fault discrimination routine determines that the signal can be corrected, e.g., by a prediction, then the fault may be categorized as a “Category 1 fault” and signal processing appropriate for such may be applied. If not, the routine may determine if the display should be suspended, and if so the fault may be categorized as a category 2 fault, and display suspended (Fig. 19; para. 0343; and responsive to determining that the sensor data will not generate a reliable output, suspending display of at least one value based on the sensor data.
Regarding claim 19, Vanslyke et al. discloses that in some embodiments, the processor module is configured to suspend display of sensor data during verification or determination of a likelihood of recovery, after which the processor module may be configured to either re-allow display of sensor data if it is determined that the sensor has recovered from the EOL symptoms. (para. 0335; the method of claim 18, further comprising: after suspending display of at least one value based on the sensor data, receiving second sensor data from the analyte sensor; evaluating a second plurality of risk factor metrics at least a portion of the second plurality of risk factor metrics being based at least in part on the second sensor data; determining a second end-of-life status of the analyte sensor based at least in part on the evaluating; determining a second noise measure describing the second sensor data; determining, based at least in part on the second end-of-life status of the analyte sensor and the second noise measure that the second sensor data will generate a reliable output; and responsive to determining that the second sensor data will generate a reliable output, displaying at least one value based on the second sensor data.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
10. Claims 12-13 are rejected under 35 U.S.C. 103 as being anticipated by Vanslyke et al. (US20150351670A1). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1-3, 8, 11 and 16-20 are taught by Vanslyke et al. above
Regarding claim 12,Vanslyke et al. teaches detecting faults related to drift involving calculating and comparing slopes. Vanslyke et al. teaches that drift may be in either of the quantities m or b, where y=mx+b, which is a regression equation where the slope M represent sensitivity of the sensor and the intercept b represents a background or offset. Vanslyke et al. further discloses that signal criteria to discriminate such faults may include measuring a potential at a first time and measuring a potential at a second time, at the same electrode, and determining if a drift in m or b has occurred. Vanslyke et al further discloses that the processor is configured to detect a drop in signal sensitivity by detecting a change in slope between two time points and optionally determining a rate of change of signal sensitivity (e.g. 20% per day). Vanslyke et al. further discloses that continuous measurements can be collected periodically or intermittently (para. 0296-0300, 0341, 0440; the method of claim 1, further comprising determining the downward drift in sensor sensitivity over time at least in part by: down sampling the sensor data to generate a downed sample signal; periodically determining a slope between selected data points in the down sampled signal to obtain a series of calculated slopes; periodically comparing a currently calculated slope to a previously calculated slope; and determining that sensor sensitivity is decreasing if a ratio of the currently calculated slope to the previously calculated slope decreases over time.
Pertaining to claim 12, Vanslyke et al. is silent to downsampling before drift detection, but it would be obvious to try because Vanslyke et al. discloses that the sampling rate may be adjusted to a faster or slower rate to accommodate various fault situations (para. 0354) and Vanslyke et al. discloses using different sampling rates (slow or fast) depending on the application and that fast intervals (e.g. 30 second) are good for detecting noise, whereas slow intervals (e.g. 5 minute) are sufficient for detecting glucose concentrations (para. 0206-0207) , therefore, it would be obvious to try down sampling to a slower sampling rate (rather than a faster rate) before detecting downward drift of sensitivity glucose measurement in order to measure low frequency glucose levels and filter out high frequency noise. A person of ordinary skill in the art could have pursued downsampling before sensitivity drift measurement with a reasonable expectation of success because glucose levels are a slow process relative to random noise as taught by Vanslyke et al. Therefore, the invention is prima facia obvious.
Regarding claim 13, Vanslyke et al. teaches that normal glucose signal has very low frequencies (e.g., 0 and 1.8 mHz). Vanslyke et al further teaches that a slow changing long-time scale average signal may be used to normalize the data to enhance the reliability of detection methods, e.g., signal sensitivity or noise pattern. Vanslyke et al. further teaches that loss of sensitivity may be computed by calculating a short term (e.g. ˜6-8 hours) average (or median) of the sensor output and normalizing it by the expected longer term (48 hours) average sensor sensitivity, and if the ratio of short term to long term sensitivity is smaller than 70%, there may be a risk of sensor losing sensitivity (para. 0311-0312, 0302; the method of claim 1, further comprising determining a downward drift in sensor sensitivity over time at least in part by: filtering the sensor data with a slow low pass filter having a lower cutoff frequency than a fast low pass filter to obtain slow filtered sensor data; filtering the sensor data with the fast low pass filter to obtain fast filtered sensor data; and examining a ratio of (a difference between the fast filtered sensor data and the slow filtered sensor data) to the slow filtered sensor data, wherein a downward drift in sensor sensitivity over time is more likely as the ratio becomes more negative.
It would be obvious to one of ordinary skill in the art to use the approach claimed in the instant application equivalently to disclosed approach of Vanslyke et al. to determine decreased sensitivity of a continuous sensor over time because the two approaches effectively measure the same thing. If short term average is ‘x’ and long-term average is ‘y’, Vanslyke et al. teaches loss of sensitivity when x/y < 1, whereas the instant application teaches loss of sensitivity when (x-y)/y is negative, which are effectively the same thing. As an example, if y is 10 and x is 7:
Vanslyke et al. teaches that x/y = 0.7 (i.e. the readout decreased to 70% of the original value)
The instant application teaches that (x-y)/y = -0.3 (the readout decreased by 30%)
Decreasing to 70% of the original value is equivalent to decreasing by 30%. Therefore, the approaches are effectively the same and the invention is prima facia obvious.
11. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Vanslyke et al. (US20150351670A1) as applied to claims 1-3, 8, 11-13 and 16-20 above, in view of Torimoto et al. (Diabetes Technology and Therapeutics, 2018, Vol. 20, p. 603-612) The italicized text corresponds to the instant claim limitations.
The limitations of claims 1-3, 8, 11-13 and 16-20 are taught by Vanslyke et al. above.
With respect to claim 4, Vanslyke et al. is silent to the method of claim 3, wherein at least one of the first translation function or the second translation function is a logistic regression function. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Torimoto et al.
With respect to claim 4, the claim describes univariate logistic regression analysis wherein a binary outcome is predicted based on just one independent variable. Torimoto et al. describes a method of predicting development of hypoglycemia in patients based on analysis of continuous glucose monitoring data. Torimoto et al. further discloses that individual risk factor metrics were first analyzed for predictive value using univariate logistic regression to translate metrics into risk factor values and only those with p values <0.05 were then used for multivariate logistic regression to predict development of hypoglycemia. Metrics analyzed included readouts from continuous glucose monitoring including mean blood glucose (per mg/dL) and standard deviation (per mg/dL) and coefficient of variation (per %) (p. 606, col. 2, para. 2; Table 2; p. 605, col. 1, para. 1; the method of claim 3, wherein at least one of the first translation function or the second translation function is a logistic regression function).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Torimoto et al. taught that their approach to apply univariate logistic regression to individual variables followed by multivariate logistic regression with important variables, identified that both low mean blood glucose and large blood glucose fluctuations together are important features for predicting hypoglycemia in patients (p. 611, col. 1, para. 4). Therefore, one of ordinary skill in the art would have been motivated to utilize the univariate linear regression modeling taught by Torimoto et al. in the fault discrimination method taught by Vanslyke et al., in order to help identify important variables in fault prediction before multivariate modeling. Furthermore, one of ordinary skill in the art would predict that the univariate logistic regression taught by Torimoto et al. could be readily added to the system of Vanslyke et al. with a reasonable expectation of success because they both pertain to univariate and multivariate analysis of metrics from continuous sensors. The invention is therefore prima facie obvious.
12. Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Vanslyke et al. (US20150351670A1), as applied to claims 1-3, 8, 11-13 and 16-20 above, in view of Peyser et al. (US20140005505A1). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1-3, 8, 11-13 and 16-20 are taught by Vanslyke et al. above.
Regarding claim 5, Vanslyke et al. discloses that risk factors may be weighted or otherwise processed using a probability analysis, decision matrix, various subroutines or the like, to determine an actual EOL indicator, a probability (or likelihood) of EOL, a predicted time to EOL, or the like. (para. 0327; the method of claim 3, further comprising: assigning a first weight to the first end-of-life risk factor value to determine a first waited end-of-life risk factor value; assigning a second weight to a second end-of-life risk factor value to determine a second waited end-of-life risk factor value; and determining a weighted average using the first waited end-of-life risk factor value and the second weighted end-of-life risk factor value.
Regarding claim 6, Vanslyke et al. discloses that decision fusion may be used as the function through which the various inputs are processed, wherein decision fusion may provide a Fused Bayesian likelihood estimate based on sensitivity and specificity of individual detector algorithms associated with each input or variable. Vanslyke et al. further discloses that suitable risk factors are measured and fused together to determine whether or not a sensor has reached EOL. A decision can be made for “yes” EOL or “no” EOL based on each individual risk factor. For example, if sensor sensitivity has decreased by more than Δm over some amount of time Δt then “yes” EOL otherwise “no”, or if the sensor has had severe noise (above a predetermined threshold level) for more than 12 hours of the last 24 hours then “yes” EOL, otherwise “no”. Vanslyke et al. further discloses that in some embodiments, the individual likelihood values are multiplied together for a final fused likelihood value that takes into account the ability of each individual variable to separate EOL from non-EOL. Thus, more sensitive and specific tests will be given greater weight in the final decision. Vanslyke et al. further discloses that a threshold may be determined empirically for the final fused likelihood values to achieve the best separation of EOL and non-EOL. (para. 0300, 0328, 0330; the method of claim 5, wherein the determining further comprises: determining that the weighted average meets a threshold; and based on the determining that the weighted average meets the threshold, determining that the continuous analyte sensor is in an end-of-life state.
Pertaining to claim 7, Vanslyke et al. discloses that in detecting end of life (“EOL”) of continuous sensors is the end of the useful life in which a sensor can provide reliable sensor data without providing inaccurate sensor data. Vanslyke et al. further discloses that to prevent use beyond the useful life, some embodiments notify a user to change the sensor after it has been determined that the sensor should no longer be used. (para. 0289; the method of claim 6, further comprising initiating termination of sensor use).
With respect to claim 5, Vanslyke et al. is silent to determining a weighted average using the first waited end-of-life risk factor value and the second weighted end-of-life risk factor value. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Peyser et al.
Regarding claim 5, Peyser et al. discloses a method of detecting sensor failures that involves combining data from various sensor elements, wherein each measurement is weighted and weighted averages or weighted sums are used to estimate analyte concentration values. Peyser et al. further discloses a system with two enzymatic electrodes (that could detect two different enzyme layers) and a processing module to compare signals associated with each electrode and with known patterns in order to detect sensor failure. Peyser et al. further discloses that this is done by assigning each sensor element a weight and then calculating a weighted average in the process of determining sensor failure (para. 0203; determining a weighted average using the first waited end-of-life risk factor value and the second weighted end-of-life risk factor value (claim 5)).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Peyser et al. taught that their approach to assign weights from readouts from multiple elements and combine the data by calculating a weighted average was part of a method to improve reliability of transcutaneous glucose sensors by controlling for non-analyte related signals caused by interfering species or unknown noise-causing events (para. 0004 – 0026). Therefore, one of ordinary skill in the art would have been motivated to utilize the approach to weight variables and then calculate weighted averages in detecting noise as taught by taught by Peyser et al. in the fault discrimination method taught by Vanslyke et al., in order to improve reliability and control for noise in transcutaneous glucose sensors. Furthermore, one of ordinary skill in the art would predict that the weighting and weighted average approach taught by Peyser et al. could be readily added to the system of Vanslyke et al. with a reasonable expectation of success because they both pertain to analysis and control of noise in continuous sensor readouts. The invention is therefore prima facie obvious.
13. Claims 9, 10 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Vanslyke et al. (US20150351670A1) as applied to claims 1-3, 8, 11-13 and 16-20 above, in view of Battifarano et al. (arXiv, 2018, vol 1, p. 1-6). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1-3, 8, 11-13 and 16-20 are taught by Vanslyke et al. above.
Regarding claims 9 and 10, Vanslyke et al. is silent to the method of claim 8, further comprising translating the plurality of risk factor metrics to an end-of-life likelihood using a common translation function (claim 9) and the method of claim 9, wherein the translation function is a logistic regression function (claim 10); and the method of claim 1, further comprising applying a classifier model to generate an end-of-life likelihood for the continuous analyte sensor based at least in part on the plurality of risk factor metrics, the end-of-life status of the analyte sensor being based at least in part on the end-of-life likelihood (claim 14). However, these limitations were known in the art at the time of the effective filing date of the invention as taught by Battifarano et al.
Regarding claims 9 and 10, Battifarano et al. teaches using logistic regression to model the probability of the failure state of a machine 24 hours after the measurements (i.e. failure or no failure) by mapping a linear combination of the features to (0,1) via a non-linear transformation function. Battifarano et al. further discloses that each feature is a vector containing the complete machine state of a single machine at a single point in time and the target variable. Battifarano et al. discloses that the feature vector includes various different data types such as whether or not error type 1 occurred, whether or not error type 2 occurred, whether or not component 1 was replaced, voltage measurements, age of machine in years etc. (p. 3, para.3-4, p. 3, equation 1, Table 2; the method of claim 8, further comprising translating the plurality of risk factor metrics to an end-of-life likelihood using a common translation function (claim 9); the method of claim 9, wherein the translation function is a logistic regression function (claim 10).
With respect to claim 14, Battifaroni et al. teaches that logistic regression is an approach to binary classification that classifies data into identifiable areas where distance from a decision boundary dictates the probability of inclusion within the class. Battifaroni et al. further teaches that logistic regression models the probability of a binary target variable Y given the features X by mapping a linear combination of the features to (0,1) via a non-linear transformation function. Battifaroni et al. further discloses using a logistic regression model based on a variety of metrics from the sensor to predict failure versus non-failure events by training and testing a classifier model (p. 1, para. 2; p. 3, para. 3; p. 4, para. 2, Fig. 1; the method of claim 1, further comprising applying a classifier model to generate an end-of-life likelihood for the continuous analyte sensor based at least in part on the plurality of risk factor metrics, the end-of-life status of the analyte sensor being based at least in part on the end-of-life likelihood.
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Battifarano et al. taught that applying logistic regression based on various metrics collected during machine operation had 100% accuracy of predicting machine failures in the test set. Battifarano et al. further discloses that it is important to develop methods to predict faults while machines are in operation because this is the time when consequences of failure are most detrimental (p. 5, para. 1). Therefore, one of ordinary skill in the art would have been motivated to utilize the linear regression modeling taught by Battifarano et al. in the fault discrimination method taught by Vanslyke et al., in order to improve performance of predicting faults in monitors during operation to avoid detrimental outcome. Furthermore, one of ordinary skill in the art would predict that the logistic regression taught by Battifarano et al. could be readily added to the system of Vanslyke et al. with a reasonable expectation of success because they both pertain to detecting faults based on data from various operation metrics collected during operation of a machine. The invention is therefore prima facie obvious.
14. Claim 15 rejected under 35 U.S.C. 103 as being unpatentable over Vanslyke et al. (US20150351670A1) as applied to claims 1-3, 8, 11-13 and 16-20 above, in view of Stojnic et al. (Telsiks 2011, Serbia, Nis, October 5-8, 2011, p.273-276). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1-3, 8, 11-13 and 16-20 are taught by Vanslyke et al. above.
Pertaining to claim 15, Vanslyke et al. is silent to the method of claim 1, further comprising applying a plurality of sequential processing layers to the sensor data, the applying of a first processing layer of the plurality of sequential processing layers comprising applying a first filter to the sensor data to generate a first filter output and down sampling the first filter output to generate a first layer output, and the applying of a second processing layer of the plurality of sequential processing layers comprising applying a second filter to the first layer output to generate a second filter output, and down sampling the second filter output to generate a second layer output, at least one of the plurality of risk factor metrics being based at least in part on the second layer output. However, these limitations were known in the art at the time of the effective filing date of the invention as taught by Stojnic et al.
Regarding claim 15, Stojnic et al. teaches an optimized multistage decimation filter chain as a method of decreasing of an original signal sampling rate to a lower sampling rate by an integer factor by process involving a combination of lowpass filtering and decimation (downsampling). Stojnic et al. further teaches that this can include a two-stage process with decimation (H1) then filter (N1), followed by decimation (H2) then filter (N2) steps (p. 273, col. 1, para. 1; Fig. 1; p. 275, col. 2, para. 2, Table 1; the method of claim 1, further comprising applying a plurality of sequential processing layers to the sensor data, the applying of a first processing layer of the plurality of sequential processing layers comprising applying a first filter to the sensor data to generate a first filter output and down sampling the first filter output to generate a first layer output, and the applying of a second processing layer of the plurality of sequential processing layers comprising applying a second filter to the first layer output to generate a second filter output, and down sampling the second filter output to generate a second layer output, at least one of the plurality of risk factor metrics being based at least in part on the second layer output).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Stojnic et al. teaches that in decreasing a signal sampling rate to a lower sampling rate, it is more efficient if the sample–rate-change operations are done in multiple stages then in one-stage process (p. 273, col. 1, para 1). Stojnic et al. further teaches that the main advantage of the multistage implementation is in the fact that the number of multipliers reduces due to filters of lower order in each stage, and the most of multiplications is performed at lower sampling rate. In this way the overall number of operations per an input sample is significantly lower (p. 274, col. 2, para. 2). Therefore, one of ordinary skill in the art would have been motivated to utilize the multistage decimation filter chain method taught by Stojnic et al. in the fault discrimination method taught by Vanslyke et al., in order to improve processing efficiency by reducing the overall number of operations performed per input sample. Furthermore, one of ordinary skill in the art would predict that the sample rate reduction strategy taught by Stojnic et al. could be readily added to the system of Vanslyke et al. with a reasonable expectation of success because the technique is a routine and conventional signal processing approach independent of the particular sensor modality. Additionally, Vanslyke et al discloses that it is well known in signal processing theory that there are numerous applications where it is advantageous or necessary to change (increase or decrease) the sampling rate (p. 273, col. 1, para. 2). The invention is therefore prima facie obvious.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
15. Claims 1-13 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 and 20 of U.S. Patent No. 11803769B2. Although the claims at issue are not identical, they are not patentably distinct from each other.
Regarding claim 1, claim 1 of both the referenced patent and the instant application have the same four limitations shown below (the identical words are italicized):
Receiving sensor data/signal from an analyte sensor
Evaluating a plurality of risk factors (metrics) associated with end-of-life symptoms of the analyte sensors wherein (at least a portion of) the risk factors (include/comprising) a downward drift in sensor sensitivity over time, an amount of non-symmetrical, nonstationary noise and a duration of noise (note that in both applications, metrics are not limited to these three listed metrics)
Determining an end-of-life status of the analyte sensor based on at least in part on the evaluating: and
Providing an output related to the end-of-life status of the analyte sensor.
Regarding claim 2, claim 2 of both the referenced patent and the instant application depend on claim 1 and both are directed to translating/mapping each risk factor to an end-of-life risk factor metric/value; however, the instant application limits the analyte sensor to a continuous analyte sensor.
Regarding claim 3, claim 3 of both the referenced patent and the instant application depend on claim 2 and both are directed to translating/mapping using a second/different translation function.
Regarding claim 4, claim 4 of both the referenced patent and the instant application depend on claim 3 and further limit wherein at least one of the first translation functions (or the second translation function) is a logistic regression function.
Regarding claim 5, claim 5 of both the referenced patent and the instant application depend on claim 3 and are directed to assigning weights to each of the end-of-life factors and determining a weighted average.
Regarding claim 6, claim 6 of both the referenced patent and the instant application depend on claim 5 and both are directed to determining if the weighted average meets a threshold to determine that the analysis sensor it at the end of life when the threshold is exceeded. However, the instant claim 6 further limits the sensor to a continuous analyte sensor.
Regarding claim 7, claim 7 of both the referenced patent and the instant application depend on claim 6 and both are directed to further comprising termination of sensor use.
Regarding claim 8, claim 8 of both the referenced patent and the instant application depend on claim 1 and are directed to wherein the plurality of risk factors (metrics) further includes a rate of change of the downward drift in sensor sensitivity.
Regarding claim 9, both claim 9 of the instant application and claims 9 and 10 of the reference are dependent on claim 8 and are further directed to mapping/translating the risk factors to end of life factor (likelihood) using a common translation function.
Regarding claim 10, both claims 10 of the instant application and claim 11 of the reference further limit claims 9 (or 9 and 10) to using a logistic regression function.
Regarding claim 11, both claim 11 of the instant application and claim 12 of the reference are directed to: The method of claim 1, further comprising determining a downward drift in sensor sensitivity (over time) (at least in part) by examining a ratio of a shorter-term sensitivity to a longer-term sensitivity.
Regarding claim 12, both claim 12 of the instant application and claim 13 of the reference depend on claim 1 and further comprise determining the downward drift in sensor sensitivity (over time) by: down sampling the sensor signal;
(periodically) calculating/determining at least two slopes between data points in the down sampled sensor signal wherein the at least two slopes comprise a previously calculated slope and a currently calculated slope (obtaining a series of calculated slopes);
calculating a ratio by comparing the currently calculated slope to the previously calculated slope; and
determining when the ratio of the currently calculated slope to the previously calculated slope decreases (over time).
Regarding claim 13, both claim 13 of the instant application and claim 14 of the reference application are directed to: The method of claim 1, further comprising determining the downward drift in sensor sensitivity (over time at least in part) by:
filtering the sensor signal/data with a slow low pass filter having a lower cutoff frequency than a fast low pass filter to obtain slow filtered sensor data;
filtering the sensor signal/data with the fast low pass filter to obtain fast filtered sensor data;
examining a ratio of a difference between the fast filtered sensor data and the slow filtered sensor data to the slow filtered sensor data and determining when the ratio decreases, wherein a downward drift in sensor sensitivity over time is more likely as the ratio becomes more negative.
Regarding claim 20, both claim 20 of the instant application and claim 19 of the reference application recite the limitations of claim 1, but are directed to a system comprised of a ‘processor circuit’ in the instant application and ‘sensor electronics’ in the reference application.
The claims of the instant application are patentably indistinct from those of the reference application. The difference between the claims is that the instant invention is directed to a ‘continuous analyte sensor’ (as indicated in the preamble of claims 1 and 20 and claims 2 and 6 and 12-13), whereas the reference is directed to an ‘analyte sensor’. The reference application does not exclude applying the method to a ‘continuous analyte sensor’, thus the method is inclusive of ‘continuous analyte sensors’. Supporting this assertion, the disclosure of the reference patent directs the invention to a ‘continuous analyte sensor’ (for example, see col. 1, lines 21-24 and col. 2 lines 18-30). In addition, although the instant application directs the method to a ‘continuous analyte sensor’, they could also be applied to an ‘analyte sensor’. Therefore, the method applied to a continuous analyte sensor is an obvious variation of the method applied to an analyte sensor.
16. Claim 8 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2, 7 and 20 of U.S. Patent No. 12354030B2. Although the claims at issue are not identical, they are not patentably distinct from each other.
Regarding claim 8, claim 8 of the instant application is dependent on claim 1 and claims 2 and 7 of the reference patent are dependent on claims 1 and claims 1 and 6, respectively. Claim 8 of the instant application and claims 2 and 7 of the reference patent are directed to:
Receiving sensor data/signal from an analyte sensor (claim 1 of both instant application and patent)
Evaluating the amount of non-symmetrical, non-stationary noise (claim 1 of instant application and claim 2 of reference patent)
Evaluating a duration of noise (claim 1 of instant application and claim 7 of reference application)
Evaluating downward drift in sensitivity over time (claim 1 of instant application and claim 7 of reference patent)
Evaluating rate of change of downward drift of sensitivity (claim 8 of instant application and claim 7 of reference patent)
Evaluating risk factor metrics based on evaluating (claim 1 of instant application and claim 1 of reference application)
Determine end of life status based on the evaluating (claim 1 of instant application
Providing an output of end-of-life status (claim 1 of instant application and claim 1 of the reference patent).
The claims of the instant application are patentably indistinct from those of the reference application. The difference between the claims is that the instant invention is directed to a ‘continuous analyte sensor’, whereas the reference is directed to an ‘analyte sensor’. The reference application does not exclude applying the method to a ‘continuous analyte sensor’, thus the method is inclusive of ‘continuous analyte sensors’. Supporting this assertion, the disclosure of the reference patent directs the invention to a ‘continuous analyte sensor’ (for example, see col. 1, lines 25-28 and col. 2 lines 21-30). In addition, although the instant application directs the method to a ‘continuous analyte sensor’, they could also be applied to an ‘analyte sensor’. Additionally, the instant application claim 8 is different from the reference patent claims 2 and 7 in that the reference application claims requires using the amount of noise in the determining of the end-of-life status, whereas the instant application calculates the amount of noise but does not require its use in the determining. In addition, the reference patent requires comparing the risk factor metrics to a threshold, however there is no requirement that this threshold is used in the providing the output related to the end of life status of the sensor (or any other step of the analysis).
Therefore, the method applied to a continuous analyte sensor is an obvious variation of the method applied to an analyte sensor.
With respect to claim 20, claim 20 of the instant application is an independent system claim, and claim 20 of the reference patent is a system claim that is dependent on claim 19. Claim 20 of the instant application and the reference patent both claim:
A system comprising sensor electronics (or a processor circuit) to perform the operations of the method below (claim 20 of the instant application and claim 19 of the reference patent)
Receiving sensor data/signal from an analyte sensor (claim 20 of the instant application and claim 19 of the reference patent)
Evaluating non-symmetrical, non-stationary noise (claim 20 of instant application and claim 19 of reference patent)
Evaluating a duration of noise (claim 20 of instant application and claim 20 of reference application)
Evaluating downward drift in sensitivity over time (claim 20 of instant application and claim 20 of reference patent)
Evaluating risk factor metrics based on evaluating (claim 20 of instant application and claim 19 of reference application)
Determine end of life status based on the evaluating (claim 1 of instant application
Providing an output of end-of-life status (claim 20 of instant application and claim 19 of the reference patent).
The difference between the claims is:
The instant invention is directed to a ‘continuous analyte sensor’, whereas the reference is directed to an ‘analyte sensor’. The reference application does not exclude applying the method to a ‘continuous analyte sensor’, thus the method is inclusive of ‘continuous analyte sensors’. Supporting this assertion, the disclosure of the reference patent directs the invention to a ‘continuous analyte sensor’ (for example, see col. 1, lines 25-28 and col. 2 lines 21-30). In addition, although the instant application directs the method to a ‘continuous analyte sensor’, they could also be applied to an ‘analyte sensor’.
The reference patent includes evaluation of the rate of change of downward drift of sensitivity (recited in claim 20), but this is not recited in the claims of the instant application. However, the inclusion of this limitation in determining the risk factor is optional in the reference application, so the disclosure of the reference includes the system of the instant application.
The instant application claims determining an amount of noise, whereas the reference application claims only evaluating noise, which could include determining the amount of noise.
The claims of the instant application are patentably indistinct from those of the reference application because the broader claims of the reference patent are inclusive of those of the narrower claims of the instant application. Therefore, the method applied to a continuous analyte sensor is an obvious variation of the method applied to an analyte sensor.
References not used: Wu et al. (US12573486B2) teaches stacking or stacked generalization involving training a learning algorithm to combine the predictions of several other learning algorithms in two phases: 1) multiple base classifiers are used to predict the class and then a new learner is used to combine their predictions with the aim of reducing the generalization error. A logistic regression model is used as the combiner.
Conclusion
17. No claims are allowed.
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/J.J.S./ Examiner, Art Unit 1685
/OLIVIA M. WISE/ Supervisory Patent Examiner, Art Unit 1685
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