DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions. The rejections from the Office Action of 9/11/2025 are hereby withdrawn. New grounds for rejection are presented below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
The elements of Claims 18 and 20 has/have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a generic placeholder “means for” coupled with corresponding functional language without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claim(s) 18 and 20 has/have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: The structure described in Paragraph [0193].
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5-11, 13-15, 18, and 20 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Bohm et al. (US 20120265037 A1)[hereinafter “Bohm”], Liang et al. (US 20120108935 A1)[hereinafter “Liang”], and Troughton et al. (US 20110120206 A1)[hereinafter “Troughton”].
Regarding Claims 1 and 18, Bohm discloses a method for improving glucose sensing using a glucose sensor [Abstract – “Systems and methods for processing sensor data and self-calibration are provided. In some embodiments, systems and methods are provided which are capable of calibrating a continuous analyte sensor based on an initial sensitivity, and then continuously performing self-calibration without using, or with reduced use of, reference measurements.”See Fig. 7A and Paragraphs [0304]-[0305].], the method comprising:
receiving a priori calibration distribution information obtained prior to a sensor session [See Figs. 7A/7B and Paragraph [0304] – “Distribution curve 720 and confidence level 730 (e.g., 25%, 33%, 50%, 75%, 95%, or 99% confidence level) are associated with a lack of initial knowledge about certain parameters that affect sensor sensitivity or provide additional information about sensor sensitivity. For example, distribution curve 720 can be associated with factory information.”See Paragraphs [0300]-[0302].];
receiving glucose sensor data from the glucose sensor during the sensor session, the glucose sensor data comprising at least one sensor data point [Paragraph [0305] – “In turn the estimated sensitivity value may be used to calibrate the sensor, which allows for processing of sensor data to generate a glucose concentration value that is displayed to the user.”];
forming a posteriori calibration distribution information by at least shifting, tightening, or loosening at least some of the a priori calibration distribution information [Paragraph [0304] – “Bayesian networks use causal knowledge and model probabilistic dependence and independence relationships between different events. FIG. 7A depicts distribution curves of sensor sensitivity corresponding to the Bayesian learning process, in accordance with one embodiment. … As information regarding a certain parameter is acquired, the distribution curve 720' becomes steeper and the confidence interval 730' becomes narrower, as certainty of sensor sensitivity profile 710 is increased. Examples of information that may be used to change the distribution curves can include a reference analyte value, a cal-check of the sensor at the factory, patient history information, and any other information described elsewhere herein that can affect sensor sensitivity or provide information about sensor sensitivity.”See Paragraphs [0300]-[0302].] using at least a weighted variable [See Paragraphs [0280]-[0282]], wherein the shifting, tightening, or loosening at least some of the a priori calibration distribution information is based on one or more real-time inputs, the one or more real-time inputs comprising: a first value based on the received glucose sensor data and a second value based on the received glucose sensor data, the second value indicating a glucose rate-of-change [Paragraph [0292] – “Accordingly, to compensate for potential effects resulting from these conditions, in certain embodiments, the continuous analyte sensors are configured to request and accept one or more reference measurements (e.g., from a finger stick glucose measurement or from a calibration solution) at the start of the sensor session. For example, the request for one or more reference measurements can be made at about 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 3 hours, etc., after activation of the sensor.”The multiple measurements being taken at different times thus being “indicative” of rate-of-change.], and the adjusting comprising:;
converting, in real-time during the sensor session, the at least one sensor data point to calibrated sensor data based at least on the a posteriori calibration distribution information; and displaying/transmitting an indication of the calibrated sensor data for the glucose sensor to a receiver or smart phone [Paragraph [0305] – “In turn the estimated sensitivity value may be used to calibrate the sensor, which allows for processing of sensor data to generate a glucose concentration value that is displayed to the user.”See Paragraph [0429].].
Although Bohm discloses the second value indicating a glucose rate-of-change, Bohm fails to disclose the second value comprising a glucose rate-of-change. However, Liang discloses that glucose rate-of-change can be indicative of a change in sensor sensitivity and the determination of that effect on sensor sensitivity [Paragraph [0042] – “In certain embodiments that are described herein, a closed loop system may assess a reliability of at least one sensor signal with respect to its ability to accurately reflect a blood glucose level of a patient. Among other things, a sensor may lose sensitivity to the presence of blood glucose over time as a sensor is worn on a patient. In one particular embodiment, a change in a sensor's sensitivity to blood-glucose concentration may be detected based, at least in part, at least in part, on an estimate of a dispersion of a rate change in blood glucose sensor measurements over a time interval. If such a sensitivity is determined to decrease significantly, the sensor may be repaired or replaced.”See steps 1202, 1204, and 1206 of Fig. 11 and corresponding text, particularly the following portion of Paragraph [0087] – “Here, in a particular implementation, and as explained below, such a sensitivity metric may be computed based, at least in part, on a measured or estimated dispersion of a rate of change of sensor signal measurements with respect to time (e.g., a first derivative of ISIG dIsig/dt being just one non-limiting example of a rate of change in sensor signal measurements) over a time period.”]. It would have been obvious to adjust the a priori calibration distribution information based on a change in glucoses rate-of-change because Liang teaches that glucose rate-of-change is indicative of changes in sensor sensitivity.
Bohm and Liang would fail to disclose determining a rate of change in sensitivity drift rate over time; and shifting, tightening, or loosening at least some of the a priori calibration distribution information using at least the determining rate of change in sensitivity drift rate over time as the weighted variable.
However, Troughton discloses analyzing whether or not sensor drift rate remain constant and, in the event that the drift rate is not constant (i.e., experiences either a rate or “rate of rate” of change), performing re-calibration [See Fig. 3 and Paragraphs [0048]-[0050]. Particularly Paragraph [0050] – “This update process may be repeated as long as the drift parameters, e.g. the drift rate, remain constant, or at least well-defined enough. Whether or not the drift parameters remain well-defined during a predefined time frame is typically determined either prior to integrating the sensor in a patient monitoring system or from observations of the sensor response during use.”].
It would have been obvious to perform the adjusting of the a priori calibration distribution information when there is an observed change in sensitivity drift rate over time because Troughton teaches that such a condition indicates recalibration is needed. It would have been obvious to subject rate of change in sensitivity drift rate over time to a weighting process (i.e., making it a “weighted variable”)[See Paragraphs [0280]-[0282] of Bohm] because doing so would have been useful in characterizing that parameter for use in the adjustment of the a priori calibration distribution information.
Regarding Claim 18, Bohm discloses the recited system [Paragraph [0109] – “In an embodiment of the thirteenth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processor of the sensor system, cause the sensor system to implement the method of the twelfth aspect or any embodiment thereof.”].
Regarding Claim 2, Bohm discloses that the a priori calibration distribution information comprises information from previous calibrations of a particular sensor session and/or information obtained prior to sensor insertion [Paragraph [0304] – “For example, distribution curve 720 can be associated with factory information.”].
Regarding Claim 3, Bohm discloses that the a priori calibration distribution information comprises probability distributions for sensitivity (m), sensitivity-related information, baseline (b), or baseline-related information [Paragraph [0304] – “Distribution curve 720 and confidence level 730 (e.g., 25%, 33%, 50%, 75%, 95%, or 99% confidence level) are associated with a lack of initial knowledge about certain parameters that affect sensor sensitivity or provide additional information about sensor sensitivity.”].
Regarding Claim 5, Bohm discloses that the one or more real-time inputs comprise data received or determined since a previous calibration process [Paragraph [0304] – “As information regarding still another parameter is acquired, the distribution curve 720'' becomes even steeper and the confidence interval 730'' becomes even narrower, as certainty of sensor sensitivity profile 710 is further increased.”].
Regarding Claim 6, Bohm discloses that the one or more real-time inputs comprises at least one of: internally-derived real-time data, externally-derived real-time data, and combinations of internally- and externally-derived real-time data [Paragraph [0292] – “Accordingly, to compensate for potential effects resulting from these conditions, in certain embodiments, the continuous analyte sensors are configured to request and accept one or more reference measurements (e.g., from a finger stick glucose measurement or from a calibration solution) at the start of the sensor session. For example, the request for one or more reference measurements can be made at about 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 3 hours, etc., after activation of the sensor.”Paragraph [0304] – “Bayesian networks use causal knowledge and model probabilistic dependence and independence relationships between different events. FIG. 7A depicts distribution curves of sensor sensitivity corresponding to the Bayesian learning process, in accordance with one embodiment. … As information regarding a certain parameter is acquired, the distribution curve 720' becomes steeper and the confidence interval 730' becomes narrower, as certainty of sensor sensitivity profile 710 is increased. Examples of information that may be used to change the distribution curves can include a reference analyte value, a cal-check of the sensor at the factory, patient history information, and any other information described elsewhere herein that can affect sensor sensitivity or provide information about sensor sensitivity.”].
Regarding Claim 7, Bohm discloses that the one or more real-time inputs comprises at least one type of information selected from the group consisting of: stimulus signal output of sensor; sensor data measured by the sensor indicative of a glucose concentration; temperature measurements; sensor data from multielectrode sensors; sensor data generated by redundant sensors; sensor data generated by one or more auxiliary sensors; data representative of a pressure on sensor; data generated by an accelerometer; sensor diagnostic information; impedance; and certainty level [Paragraph [0292] – “Accordingly, to compensate for potential effects resulting from these conditions, in certain embodiments, the continuous analyte sensors are configured to request and accept one or more reference measurements (e.g., from a finger stick glucose measurement or from a calibration solution) at the start of the sensor session. For example, the request for one or more reference measurements can be made at about 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 3 hours, etc., after activation of the sensor.”Paragraph [0304] – “Bayesian networks use causal knowledge and model probabilistic dependence and independence relationships between different events. FIG. 7A depicts distribution curves of sensor sensitivity corresponding to the Bayesian learning process, in accordance with one embodiment. … As information regarding a certain parameter is acquired, the distribution curve 720' becomes steeper and the confidence interval 730' becomes narrower, as certainty of sensor sensitivity profile 710 is increased. Examples of information that may be used to change the distribution curves can include a reference analyte value, a cal-check of the sensor at the factory, patient history information, and any other information described elsewhere herein that can affect sensor sensitivity or provide information about sensor sensitivity.”].
Regarding Claim 8, Bohm discloses that the one or more real-time inputs comprises at least one type of information selected from the group consisting of: glucose concentration information obtained from a reference monitor; information related to meal; insulin dosing time and amounts; insulin estimates; exercise; sleep; illness; stress; hydration; and hormonal conditions [Paragraph [0292] – “Accordingly, to compensate for potential effects resulting from these conditions, in certain embodiments, the continuous analyte sensors are configured to request and accept one or more reference measurements (e.g., from a finger stick glucose measurement or from a calibration solution) at the start of the sensor session. For example, the request for one or more reference measurements can be made at about 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 3 hours, etc., after activation of the sensor.”Paragraph [0304] – “Bayesian networks use causal knowledge and model probabilistic dependence and independence relationships between different events. FIG. 7A depicts distribution curves of sensor sensitivity corresponding to the Bayesian learning process, in accordance with one embodiment. … As information regarding a certain parameter is acquired, the distribution curve 720' becomes steeper and the confidence interval 730' becomes narrower, as certainty of sensor sensitivity profile 710 is increased. Examples of information that may be used to change the distribution curves can include a reference analyte value, a cal-check of the sensor at the factory, patient history information, and any other information described elsewhere herein that can affect sensor sensitivity or provide information about sensor sensitivity.”].
Regarding Claim 9, Bohm discloses that the one or more real-time inputs comprises combinations of internally- and externally-derived real-time data comprising at least one type of information selected from the group consisting of: information gathered from population based data; glucose concentration of the host; error at calibration or error in matched data pair; site of sensor implantation specific relationships; time since sensor manufacture; exposure of sensor, while on shelf, to temperature, humidity, or external factors; a measure of noise in a glucose concentration signal; and a level of certainty [Paragraph [0292] – “Accordingly, to compensate for potential effects resulting from these conditions, in certain embodiments, the continuous analyte sensors are configured to request and accept one or more reference measurements (e.g., from a finger stick glucose measurement or from a calibration solution) at the start of the sensor session. For example, the request for one or more reference measurements can be made at about 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 3 hours, etc., after activation of the sensor.”Paragraph [0304] – “Bayesian networks use causal knowledge and model probabilistic dependence and independence relationships between different events. FIG. 7A depicts distribution curves of sensor sensitivity corresponding to the Bayesian learning process, in accordance with one embodiment. … As information regarding a certain parameter is acquired, the distribution curve 720' becomes steeper and the confidence interval 730' becomes narrower, as certainty of sensor sensitivity profile 710 is increased. Examples of information that may be used to change the distribution curves can include a reference analyte value, a cal-check of the sensor at the factory, patient history information, and any other information described elsewhere herein that can affect sensor sensitivity or provide information about sensor sensitivity.”].
Regarding Claim 10, Bohm discloses determining a level of certainty associated with calibration information and/or calibrated sensor data [Paragraph [0304] – “Distribution curve 720 and confidence level 730 (e.g., 25%, 33%, 50%, 75%, 95%, or 99% confidence level) are associated with a lack of initial knowledge about certain parameters that affect sensor sensitivity or provide additional information about sensor sensitivity. For example, distribution curve 720 can be associated with factory information.”].
Regarding Claim 11, Bohm discloses that forming a posteriori calibration distribution information further comprises: a creation of a new range or distribution information based on the one or more real-time inputs [Paragraph [0304] – “As information regarding a certain parameter is acquired, the distribution curve 720' becomes steeper and the confidence interval 730' becomes narrower, as certainty of sensor sensitivity profile 710 is increased.”].
Regarding Claim 13, Bohm discloses that the calibration distribution information is selected from the group consisting of: sensitivity; change in sensitivity; rate of change of sensitivity; baseline; change in baseline, rate of change of baseline, baseline profile associated with the sensor; sensitivity profile associated with the sensor; linearity; response time; relationships between properties of the sensor; relationships between particular stimulus signal output; and patient specific relationships between sensor and sensitivity, baseline, drift, impedance, impedance/temperature relationship, site of sensor implantation [Paragraph [0304] – “As information regarding a certain parameter is acquired, the distribution curve 720' becomes steeper and the confidence interval 730' becomes narrower, as certainty of sensor sensitivity profile 710 is increased.”].
Regarding Claim 14, Bohm discloses comprising providing output of calibrated sensor data [Paragraph [0305] – “In turn the estimated sensitivity value may be used to calibrate the sensor, which allows for processing of sensor data to generate a glucose concentration value that is displayed to the user.”].
Regarding Claim 15, Bohm discloses that the method is implemented using a processor and a memory coupled to the processor [Paragraph [0109] – “In an embodiment of the thirteenth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processor of the sensor system, cause the sensor system to implement the method of the twelfth aspect or any embodiment thereof.”].
Regarding Claim 20, Bohm discloses means for forming the a posteriori calibration distribution information by creating a new range or distribution information based on the one or more real-time inputs [Paragraph [0304] – “As information regarding a certain parameter is acquired, the distribution curve 720' becomes steeper and the confidence interval 730' becomes narrower, as certainty of sensor sensitivity profile 710 is increased.”].
Claims 16, 17, 21, and 22 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Bohm et al. (US 20120265037 A1)[hereinafter “Bohm”], Liang et al. (US 20120108935 A1)[hereinafter “Liang”], Troughton et al. (US 20110120206 A1)[hereinafter “Troughton”], and Shen et al., Online Dropout Detection In Subcutaneously Implanted Continuous Glucose Monitoring, AACC, 2010 [hereinafter “Shen”].
Regarding Claim 16, Bohm discloses the recited system [Paragraph [0109] – “In an embodiment of the thirteenth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processor of the sensor system, cause the sensor system to implement the method of the twelfth aspect or any embodiment thereof.”]. The corresponding method steps are rejected in light of Bohm, Liang, and Troughton per the explanation given with regards to Claims 1 and 18, above.
Bohm, Liang, and Troughton fail to disclose that the rate of change of sensitivity drift over time is determined as a second derivative of sensitivity with respect to time.
However, Shen discloses a method for eliminating data dropout periods from CGM sensor readings in order to prevent CGM miscalibration [Page 4373, second column – “In many experiments and clinical tests, spurious dropouts are observed in the measurements of the subcutaneously implanted continuous glucose sensor (Fig. 1). Dropouts do not reflect the true blood glucose level and could result in significant problems in the real time glucose calculation such as calibration. The data values during a dropout are usually lower than the normal glucose variations. If a data point from the dropout is accidentally picked for calibration, the calibrated glucose level will be larger than the true BG level. The deeper the dropout, the larger the error will be resulted. Therefore, dropouts should be detected and avoided before using measurement data for glucose calculations.”]. Shen estimates the dropout periods (which correlate to anomalous sensitivity drift periods) by taking the first and second derivatives of glucose level data [Page 4374, second column, Step 3 – “First derivative G’(t) and second derivative G”(t) are updated”Page 4375, second column – “For better detection accuracy, the left and right boundaries of the dropout are refined using the first and second derivatives of the glucose signal, with the assumption that the accurate boundaries L and R lie in places around the preliminary ones but where the first derivative decreases the fastest, i.e. the second derivative reaches its local minimum.”]. It would have been obvious to take the second derivative of blood glucose sensitivity data and to take that into account during the calibration process because doing so would have helped ensure that the calibration process is accurate through the use of appropriate calibration data.
Regarding Claim 17, Bohm discloses that the sensor electronics comprise a processor module, the processor module comprising instructions stored in computer memory, wherein the instructions, when executed by the processor module, cause the sensor electronics to perform the forming [Paragraph [0109] – “In an embodiment of the thirteenth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processor of the sensor system, cause the sensor system to implement the method of the twelfth aspect or any embodiment thereof.”].
Regarding Claim 21, Bohm fails to disclose that the change in sensitivity drift rate over time is a second derivative of sensitivity with respect to time. However, Shen discloses a method for eliminating data dropout periods from CGM sensor readings in order to prevent CGM miscalibration [Page 4373, second column – “In many experiments and clinical tests, spurious dropouts are observed in the measurements of the subcutaneously implanted continuous glucose sensor (Fig. 1). Dropouts do not reflect the true blood glucose level and could result in significant problems in the real time glucose calculation such as calibration. The data values during a dropout are usually lower than the normal glucose variations. If a data point from the dropout is accidentally picked for calibration, the calibrated glucose level will be larger than the true BG level. The deeper the dropout, the larger the error will be resulted. Therefore, dropouts should be detected and avoided before using measurement data for glucose calculations.”]. Shen estimates the dropout periods (which correlate to anomalous sensitivity drift periods) by taking the first and second derivatives of glucose level data [Page 4374, second column, Step 3 – “First derivative G’(t) and second derivative G”(t) are updated”Page 4375, second column – “For better detection accuracy, the left and right boundaries of the dropout are refined using the first and second derivatives of the glucose signal, with the assumption that the accurate boundaries L and R lie in places around the preliminary ones but where the first derivative decreases the fastest, i.e. the second derivative reaches its local minimum.”]. It would have been obvious to take the second derivative of blood glucose sensitivity data and to take that into account during the calibration process because doing so would have helped ensure that the calibration process is accurate through the use of appropriate calibration data.
Regarding Claim 22, Shen (as combined with Bohm) would disclose that the change in sensitivity drift rate over time would indicate a change in probability that the a priori calibration distribution information is accurate [Page 4373, second column – “In many experiments and clinical tests, spurious dropouts are observed in the measurements of the subcutaneously implanted continuous glucose sensor (Fig. 1). Dropouts do not reflect the true blood glucose level and could result in significant problems in the real time glucose calculation such as calibration. The data values during a dropout are usually lower than the normal glucose variations. If a data point from the dropout is accidentally picked for calibration, the calibrated glucose level will be larger than the true BG level. The deeper the dropout, the larger the error will be resulted. Therefore, dropouts should be detected and avoided before using measurement data for glucose calculations.”].
Response to Arguments
Applicant argues:
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Examiner’s Response:
The Examiner agrees that determination of “a rate of change in drift rate” is not disclosed by Troughton.
However, Troughton discloses analyzing whether or not sensor drift rate remain constant and, in the event that the drift rate is not constant (i.e., experiences either a rate or “rate of rate” of change), performing re-calibration [See Fig. 3 and Paragraphs [0048]-[0050]. Particularly Paragraph [0050] – “This update process may be repeated as long as the drift parameters, e.g. the drift rate, remain constant, or at least well-defined enough. Whether or not the drift parameters remain well-defined during a predefined time frame is typically determined either prior to integrating the sensor in a patient monitoring system or from observations of the sensor response during use.”].
It would have been obvious to perform the adjusting of the a priori calibration distribution information when there is an observed change in sensitivity drift rate over time because Troughton teaches that such a condition indicates recalibration is needed.
The Examiner separately notes that Shen discloses taking the first and second derivatives of glucose level data [Page 4374, second column, Step 3 – “First derivative G’(t) and second derivative G”(t) are updated”].
New grounds for rejection are presented above with regards to the amended claimed subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Jose Bernardo, Bayesian Statistics, Encyclopedia of Life Support Systems, 2003 – See 3.1 Learning Process at Pages 10-14 in which a priori distribution information is transformed to a posteriori distribution information by way of a Bayesian approach to refine statistical analysis based on newly acquired sample data.
Dempler, SECOND DERIVATIVE OF FLUX DIGITAL CONTROL OF A SPACE REACTOR FROM SOURCE LEVEL TO CRITICAL, IEEE, 1965
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Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ROBERT QUIGLEY whose telephone number is (313)446-4879. The examiner can normally be reached 9AM-5PM EST.
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857