DETAILED ACTION
This action is filed in response to the application filed on 10/18/2023.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
Acknowledgement is made of Applicant’s Information Disclosure Statements (IDS) form PTO-1149 filed on 10/18/2023, 5/16/2024, and 8/14/2024, and 4/08/2026. These IDS have been considered.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6 and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Stram (WO2022044018 A2) in view of Fang (CN113340969 A).
Regarding Claim 1, Stram teaches a method for factory calibration of a sensor of a continuous glucose monitoring (CGM) system (e.g. see [0004] “In some embodiments, a CGM device sensor calibration method is provided and comprises confirming sensor-by-sensor parameters to produce calibration data, where such confirmation is via lot measurements of a sampling of select number of produced sensors”) comprising:
receiving, by a processor, enzyme membrane data (e.g. see [0045] “Counter electrode side (e.g., bottom side), can include Ag/AgCl layer 31, and biocompatible layer 50, and, working electrode side can include modified SPC (stone plastic composite) layer 41 (e.g., modified SPC layer), enzyme layer 42, glucose limiting layer 43, and biocompatible layer 50”) including an enzyme membrane thickness after each dip of a working wire in a first dip solution according to first parameters for forming an enzyme membrane on the working wire (e.g. see [0045] “Following the deposition of layers, a height (i.e., thickness) of the layers can be captured, and the sensor-by-sensor data can be stored (per sensor identifier). The layers’ height (thickness) measurement can be done with optical profder, spectral reflectance monitor, and/or the like,” and [0004] “such confirmation is via lot measurements of a sampling of select number of produced sensors (e.g., between 1 and 100), and the parameters are selected from the group consisting of: a surface area of the working electrode, a thickness of one or more specific layers, the thickness of all layers”);
receiving, by the processor, glucose limiting membrane data (e.g. see [0045] “Counter electrode side (e.g., bottom side), can include Ag/AgCl layer 31, and biocompatible layer 50, and, working electrode side can include modified SPC (stone plastic composite) layer 41 (e.g., modified SPC layer), enzyme layer 42, glucose limiting layer 43, and biocompatible layer 50”) including a glucose limiting membrane thickness after each dip of the working wire in a second dip solution according to second parameters for forming a glucose limiting membrane on the working wire (e.g. see [0045] “Following the deposition of layers, a height (i.e., thickness) of the layers can be captured, and the sensor-by-sensor data can be stored (per sensor identifier). The layers’ height (thickness) measurement can be done with optical profder, spectral reflectance monitor, and/or the like,” and [0004] “such confirmation is via lot measurements of a sampling of select number of produced sensors (e.g., between 1 and 100), and the parameters are selected from the group consisting of: a surface area of the working electrode, a thickness of one or more specific layers, the thickness of all layers”);
determining, by the processor, a working wire diameter after the working wire is formed, wherein the working wire diameter includes the enzyme membrane thickness and the glucose limiting membrane thickness(e.g. see [0004] “such confirmation is via lot measurements of a sampling of select number of produced sensors (e.g., between 1 and 100), and the parameters are selected from the group consisting of: a surface area of the working electrode, a thickness of one or more specific layers, the thickness of all layers (i.e. a diameter of the working wire”);
and wherein the formed working wire includes an interference membrane (e.g. see [0045] “the layers shown in Figure 12 are just one example of the composition of layers used for continuous glucose sensing. Other examples may include, for example, any combination of layers including enzymes, mediators, cross linkers, adhesives, and any polymer that can be used for controlling diffusion of glucose and oxygen (as well as controlling the diffusion of various interfering compounds, including, for example, acetaminophen, ascorbic acid, etc.)(i.e. an interference membrane)”), the enzyme membrane and the glucose limiting membrane (e.g. see [0031] “The working electrode, in some embodiments, can include at least one enzyme layer 42, and in some embodiments, also includes a glucose limiting layer 43”).
Stram does not explicitly disclose generating automatically, by the processor in communication with the CGM system, a correlation between the first parameters and the second parameters to at least one of i) a factory sensitivity, and ii) a drift profile of the sensor, wherein the drift profile predicts a sensitivity of the sensor over time; and associating, by the processor, the at least one of the factory sensitivity or the drift profile with the sensor, wherein the sensor outputs a glucose reading during in-vivo use based on the at least one of the factory sensitivity or the drift profile.
In the same field of endeavor, Fang teaches generating automatically, by the processor in communication with the CGM system, a correlation between the first parameters and the second parameters to at least one of i) a factory sensitivity (e.g. see [pg. 2, 2nd to last paragraph ] “therefore, the present disclosure provides a glucose sensor delivery calibration method, wherein it comprises: obtaining a plurality of glucose sensors having a consistency process parameter; selecting at least one glucose sensor from the plurality of glucose sensors as a sensor sample; analyzing and testing the sensor sample; obtaining the change curve of the current of the sensor sample with the change of glucose concentration and the attenuation curve of the sensitivity,”) , and ii) a drift profile of the sensor, wherein the drift profile predicts a sensitivity of the sensor over time (e.g. see [pg. 3 paragraph 6] “In addition, the present disclosure relates to the factory calibration method, optionally, the attenuation curve reflects the change of the sensitivity of the sensor sample with time. Thus, it can observe the change of the sensitivity through the attenuation curve”); and
associating, by the processor, the at least one of the factory sensitivity or the drift profile with the sensor (e.g. see [pg. 3 paragraph 7] “In addition, the present disclosure relates to the factory calibration method, optionally, obtaining the initial sensitivity and attenuation coefficient of the sensor sample according to the attenuation curve to calculate the compensation amount. Therefore, it is convenient to obtain the compensation amount needed by the glucose sensor”),
wherein the sensor outputs a glucose reading during in-vivo use based on the at least one of the factory sensitivity or the drift profile (e.g. see [pg. 9 2nd to last paragraph- pg. 10 paragraph 2 ] “the sensitivity attenuation curve L can be obtained, compensation model can be based on the difference of the sensitivity of the reduced Δ L (t) to compensate the measured value of the glucose sensor 1. In some examples, the compensation model can be embedded in the form of software to the electronic system 2. Here, glucose sensor 1, electronic system 2 and reading device 3 can form a continuous blood glucose monitor. In the continuous blood glucose monitor, the electronic system 2 is electrically connected with the glucose sensor 1; the electronic system 2 can store the glucose concentration signal obtained by the glucose sensor 1. The concentration signal of the glucose can be transmitted to the reading device 3 in a wireless manner, whereby the concentration signal of the glucose can be obtained. In addition, in some examples, the concentration signal of the glucose can be directly displayed on the display screen 3a of the reading device 3. In this embodiment, the compensation model can be embedded into a plurality of glucose sensor 1 to realize the automatic calibration of a plurality of glucose sensor 1”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the sensitivity and drift profile embodiments of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
Regarding Claim 2, Stram and Fang teach the limitations of Claim 1. Stram does not explicitly disclose wherein the drift profile is divided into time regions and each time region predicts the sensitivity based on a mathematical model.
In the same field of endeavor, Fang teaches wherein the drift profile is divided into time regions and each time region predicts the sensitivity based on a mathematical model (e.g. see [pg. 9, 3rd to last paragraph] “in some examples, the sensitivity of the glucose sensor 1 will decay with time, forming a sensitivity attenuation curve L (see FIG. 7). In this case, it can judge the stability of the glucose sensor 1 according to the use time.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the sensitivity and drift profile embodiments of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
Regarding Claim 3, Stram and Fang teach the limitations of Claim 2. Stram does not explicitly disclose wherein the mathematical model is derived from historical data from enzyme membrane thickness and glucose limiting membrane thickness.
In the same field of endeavor, Fang teaches wherein the mathematical model is derived from historical data (e.g. see [pg. 10 paragraph 4] “in some examples, the compensation model is calculated based on the change curve and the attenuation curve, to compensate the glucose concentration measured by the glucose sensor 1 with time attenuation,”) from enzyme membrane thickness and glucose limiting membrane thickness (e.g. see [pg. 3 paragraph 3] “In addition, the present disclosure relates to the factory calibration method, optionally, the mass of the glucose enzyme layer in the attenuation curve and the sensor sample, the volume of the glucose enzyme layer, the thickness of the glucose enzyme layer (i.e. enzyme membrane thickness), the activity of the glucose enzyme layer, the film thickness of the semi-permeable membrane (i.e. glucose limiting membrane thickness); At least one of the diffusion coefficients of the semi-permeable membrane is associated.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the mathematical model of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
Regarding Claim 4, Stram and Fang teaches the limitations of Claim 1. Stram does not explicitly disclose determining, by the processor, a relationship between the factory sensitivity and a baseline, wherein the baseline is an electrical current generated by the sensor at a blood glucose level of zero; and associating, by the processor, the relationship with the sensor.
In the same field of endeavor, Fang teaches determining, by the processor, a relationship between the factory sensitivity and a baseline, wherein the baseline is an electrical current generated by the sensor at a blood glucose level of zero; and associating, by the processor, the relationship with the sensor (e.g. see [pg. 9 paragraph 3] “FIG. 6 shows a variation curve of the current of the glucose sensor (sensor sample) a, the sensor b, the sensor c and the sensor d varying with the glucose concentration. by FIG. 6 can be obtained, sensor a, sensor b, sensor c and sensor d of the current change curve consistency of glucose concentration (initial value and slope are close). based on the corresponding curve can obtain the batch of glucose sensor 1 of the response current y and the glucose concentration x of the relational expression: y = Ax + B ... (IV).In the formula (IV), A represents the sensitivity of the sensor.”
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the sensor sensitivity embodiments of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
Regarding Claim 5, Stram and Fang teaches the limitations of Claim 4. Stram does not explicitly disclose wherein the relationship is linear. In the same field of endeavor, Fang teaches wherein the relationship is linear (e.g. see Fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the sensor sensitivity embodiments of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
Regarding Claim 6, Stram and Fang teaches the limitations of Claim 1. Stram does not explicitly disclose determining, by the processor, a relationship between historical data of in-vivo sensitivity of a plurality of sensors and the drift profile of the sensor; and associating, by the processor, the relationship with the sensor.
In the same field of endeavor, Fang teaches determining, by the processor, a relationship between historical data of in-vivo sensitivity of a plurality of sensors and the drift profile of the sensor; and associating, by the processor, the relationship with the sensor (e.g. see [pg. 6, 2nd to last paragraph] “the electronic system 2, and display the glucose concentration value. In addition, because the embodiment of the glucose sensor 1 can realize continuous monitoring, so it can realize long time (e.g., 1 day to 24 days) continuously monitoring human body glucose concentration value,” and [pg. 9, paragraph 8] “in the use process, the initial sensitivity and attenuation coefficient measured by the glucose sensor 1 is stored in the electronic system 2, whereby the initial sensitivity and attenuation coefficient can be used to calculate the compensation amount Δ L (t), so as to obtain the proper compensation model. In the example of FIG. 7, compensation amount is Δ L (t) = L (t) -L0 (t),” and [pg. 9, paragraph 11] “In this case, even after several days, the change of the sensitivity will not be too large, so, even under the condition of not changing the sensitivity coefficient, it can accurately calculate the concentration of glucose,” Examiner notes the initial data is historical data as it can be from as far back as 24 days prior and is used to compare between those readings and the current readings to determine any attenuation i.e. a drift profile of the sensor).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the drift profile embodiments of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
Regarding Claim 14, Stram teaches a method for factory calibration of a sensor of a continuous glucose monitoring (CGM) system (e.g. see [0004] “In some embodiments, a CGM device sensor calibration method is provided and comprises confirming sensor-by-sensor parameters to produce calibration data, where such confirmation is via lot measurements of a sampling of select number of produced sensors”) comprising:
dipping a working wire in a first dip coating solution according to first parameters for forming an enzyme membrane on the working wire (e.g. see [0045] “Following the deposition of layers, a height (i.e., thickness) of the layers can be captured, and the sensor-by-sensor data can be stored (per sensor identifier). The layers’ height (thickness) measurement can be done with optical profder, spectral reflectance monitor, and/or the like,”);
measuring enzyme membrane data, wherein the enzyme membrane data includes an enzyme membrane thickness (e.g. see [0004] “such confirmation is via lot measurements of a sampling of select number of produced sensors (e.g., between 1 and 100), and the parameters are selected from the group consisting of: a surface area of the working electrode, a thickness of one or more specific layers, the thickness of all layers”);
dipping the working wire in a second coating solution according to second parameters to form a glucose limiting membrane on the working wire (e.g. see [0045] “Counter electrode side (e.g., bottom side), can include Ag/AgCl layer 31, and biocompatible layer 50, and, working electrode side can include modified SPC (stone plastic composite) layer 41 (e.g., modified SPC layer), enzyme layer 42, glucose limiting layer 43, and biocompatible layer 50,” and [0045] “Following the deposition of layers, a height (i.e., thickness) of the layers can be captured, and the sensor-by-sensor data can be stored (per sensor identifier). The layers’ height (thickness) measurement can be done with optical profder, spectral reflectance monitor, and/or the like,”);
measuring glucose limiting membrane data, wherein the glucose limiting membrane data includes a glucose limiting membrane thickness (e.g. see [0004] “such confirmation is via lot measurements of a sampling of select number of produced sensors (e.g., between 1 and 100), and the parameters are selected from the group consisting of: a surface area of the working electrode, a thickness of one or more specific layers, the thickness of all layers”);
determining, by the processor, a working wire diameter after the working wire is formed, wherein the working wire diameter includes the enzyme membrane thickness and the glucose limiting membrane thickness(e.g. see [0004] “such confirmation is via lot measurements of a sampling of select number of produced sensors (e.g., between 1 and 100), and the parameters are selected from the group consisting of: a surface area of the working electrode, a thickness of one or more specific layers, the thickness of all layers (i.e. a diameter of the working wire”);
and wherein the formed working wire includes an interference membrane (e.g. see [0045] “the layers shown in Figure 12 are just one example of the composition of layers used for continuous glucose sensing. Other examples may include, for example, any combination of layers including enzymes, mediators, cross linkers, adhesives, and any polymer that can be used for controlling diffusion of glucose and oxygen (as well as controlling the diffusion of various interfering compounds, including, for example, acetaminophen, ascorbic acid, etc.)(i.e. an interference membrane)”), the enzyme membrane and the glucose limiting membrane (e.g. see [0031] “The working electrode, in some embodiments, can include at least one enzyme layer 42, and in some embodiments, also includes a glucose limiting layer 43”).
Stram does not explicitly disclose generating automatically, by the processor in communication with the CGM system, a correlation between the first parameters and the second parameters to at least one of i) a factory sensitivity, and ii) a drift profile of the sensor, wherein the drift profile predicts a sensitivity of the sensor over time; and associating, by the processor, the at least one of the factory sensitivity or the drift profile with the sensor, wherein the sensor outputs a glucose reading during in-vivo use based on the at least one of the factory sensitivity or the drift profile.
In the same field of endeavor, Fang teaches generating automatically, by the processor in communication with the CGM system, a correlation between the first parameters and the second parameters to at least one of i) a factory sensitivity (e.g. see [pg. 2, 2nd to last paragraph ] “therefore, the present disclosure provides a glucose sensor delivery calibration method, wherein it comprises: obtaining a plurality of glucose sensors having a consistency process parameter; selecting at least one glucose sensor from the plurality of glucose sensors as a sensor sample; analyzing and testing the sensor sample; obtaining the change curve of the current of the sensor sample with the change of glucose concentration and the attenuation curve of the sensitivity,”) , and ii) a drift profile of the sensor, wherein the drift profile predicts a sensitivity of the sensor over time (e.g. see [pg. 3 paragraph 6] “In addition, the present disclosure relates to the factory calibration method, optionally, the attenuation curve reflects the change of the sensitivity of the sensor sample with time. Thus, it can observe the change of the sensitivity through the attenuation curve”); and
associating, by the processor, the at least one of the factory sensitivity or the drift profile with the sensor (e.g. see [pg. 3 paragraph 7] “In addition, the present disclosure relates to the factory calibration method, optionally, obtaining the initial sensitivity and attenuation coefficient of the sensor sample according to the attenuation curve to calculate the compensation amount. Therefore, it is convenient to obtain the compensation amount needed by the glucose sensor”),
wherein the sensor outputs a glucose reading during in-vivo use based on the at least one of the factory sensitivity or the drift profile (e.g. see [pg. 9 2nd to last paragraph- pg. 10 paragraph 2 ] “the sensitivity attenuation curve L can be obtained, compensation model can be based on the difference of the sensitivity of the reduced Δ L (t) to compensate the measured value of the glucose sensor 1. In some examples, the compensation model can be embedded in the form of software to the electronic system 2. Here, glucose sensor 1, electronic system 2 and reading device 3 can form a continuous blood glucose monitor. In the continuous blood glucose monitor, the electronic system 2 is electrically connected with the glucose sensor 1; the electronic system 2 can store the glucose concentration signal obtained by the glucose sensor 1. The concentration signal of the glucose can be transmitted to the reading device 3 in a wireless manner, whereby the concentration signal of the glucose can be obtained. In addition, in some examples, the concentration signal of the glucose can be directly displayed on the display screen 3a of the reading device 3. In this embodiment, the compensation model can be embedded into a plurality of glucose sensor 1 to realize the automatic calibration of a plurality of glucose sensor 1”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the sensitivity and drift profile embodiments of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
Regarding Claim 15, Stram and Fang teach the limitations of Claim 14. Stram does not explicitly disclose wherein the drift profile is divided into time regions and each time region predicts the sensitivity based on a mathematical model.
In the same field of endeavor, Fang teaches wherein the drift profile is divided into time regions and each time region predicts the sensitivity based on a mathematical model (e.g. see [pg. 9, 3rd to last paragraph] “in some examples, the sensitivity of the glucose sensor 1 will decay with time, forming a sensitivity attenuation curve L (see FIG. 7). In this case, it can judge the stability of the glucose sensor 1 according to the use time.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the sensitivity and drift profile embodiments of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
Regarding Claim 16, Stram and Fang teach the limitations of Claim 15. Stram does not explicitly disclose wherein the mathematical model is derived from historical data from enzyme membrane thickness and glucose limiting membrane thickness.
In the same field of endeavor, Fang teaches wherein the mathematical model is derived from historical data (e.g. see [pg. 10 paragraph 4] “in some examples, the compensation model is calculated based on the change curve and the attenuation curve, to compensate the glucose concentration measured by the glucose sensor 1 with time attenuation,”) from enzyme membrane thickness and glucose limiting membrane thickness (e.g. see [pg. 3 paragraph 3] “In addition, the present disclosure relates to the factory calibration method, optionally, the mass of the glucose enzyme layer in the attenuation curve and the sensor sample, the volume of the glucose enzyme layer, the thickness of the glucose enzyme layer (i.e. enzyme membrane thickness), the activity of the glucose enzyme layer, the film thickness of the semi-permeable membrane (i.e. glucose limiting membrane thickness); At least one of the diffusion coefficients of the semi-permeable membrane is associated.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the mathematical model of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
Regarding Claim 17, Stram and Fang teaches the limitations of Claim 14. Stram does not explicitly disclose determining, by the processor, a relationship between the factory sensitivity and a baseline, wherein the baseline is an electrical current generated by the sensor at a blood glucose level of zero; and associating, by the processor, the relationship with the sensor.
In the same field of endeavor, Fang teaches determining, by the processor, a relationship between the factory sensitivity and a baseline, wherein the baseline is an electrical current generated by the sensor at a blood glucose level of zero; and associating, by the processor, the relationship with the sensor (e.g. see [pg. 9 paragraph 3] “FIG. 6 shows a variation curve of the current of the glucose sensor (sensor sample) a, the sensor b, the sensor c and the sensor d varying with the glucose concentration. by FIG. 6 can be obtained, sensor a, sensor b, sensor c and sensor d of the current change curve consistency of glucose concentration (initial value and slope are close). based on the corresponding curve can obtain the batch of glucose sensor 1 of the response current y and the glucose concentration x of the relational expression: y = Ax + B ... (IV).In the formula (IV), A represents the sensitivity of the sensor.”
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the sensor sensitivity embodiments of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
Regarding Claim 18, Stram and Fang teaches the limitations of Claim 14. Stram does not explicitly disclose determining, by the processor, a relationship between historical data of in-vivo sensitivity of a plurality of sensors and the drift profile of the sensor; and associating, by the processor, the relationship with the sensor.
In the same field of endeavor, Fang teaches determining, by the processor, a relationship between historical data of in-vivo sensitivity of a plurality of sensors and the drift profile of the sensor; and associating, by the processor, the relationship with the sensor (e.g. see [pg. 6, 2nd to last paragraph] “the electronic system 2, and display the glucose concentration value. In addition, because the embodiment of the glucose sensor 1 can realize continuous monitoring, so it can realize long time (e.g., 1 day to 24 days) continuously monitoring human body glucose concentration value,” and [pg. 9, paragraph 8] “in the use process, the initial sensitivity and attenuation coefficient measured by the glucose sensor 1 is stored in the electronic system 2, whereby the initial sensitivity and attenuation coefficient can be used to calculate the compensation amount Δ L (t), so as to obtain the proper compensation model. In the example of FIG. 7, compensation amount is Δ L (t) = L (t) -L0 (t),” and [pg. 9, paragraph 11] “In this case, even after several days, the change of the sensitivity will not be too large, so, even under the condition of not changing the sensitivity coefficient, it can accurately calculate the concentration of glucose,” Examiner notes the initial data is historical data as it can be from as far back as 24 days prior and is used to compare between those readings and the current readings to determine any attenuation i.e. a drift profile of the sensor).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the drift profile embodiments of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Stram (WO2022044018 A2) in view of Fang (CN113340969 A), and in further view of Wang (US 20180199873 A1).
Regarding Claim 7, Stram and Fang teaches the limitations of Claim 6. Stram does not explicitly disclose wherein the relationship is linear. In the same field of endeavor Fang teaches wherein the relationship is linear (e.g. see [Fig. 6]) Also In the same field of endeavor Wang also teaches wherein the relationship is linear ( e.g. see [0242] “the sensing mechanism generally depends on phenomena that are linear with glucose concentration, for example: (1) diffusion of glucose through a membrane system (for example, biointerface membrane and membrane system) situated between implantation site and/or the electrode surface, (2) an enzymatic reaction within the membrane system, and (3) diffusion of the H.sub.2O.sub.2 to the sensor. Because of this linearity, calibration of the sensor can be understood by solving an equation: y=mx+b in which y represents the sensor signal (e.g., counts), x represents the estimated glucose concentration,” and [0318] “FIGS. 46 and 47 respectively show linear-scale and log-scale graphs 4600 and 4700 of fatigue characteristics for various exemplary flexible analyte sensors”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the sensor calibration of Stram with the linear relationships of Fang and Wang for the purpose of determining glucose levels with the advantage of additional data.
Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Stram (WO2022044018 A2) in view of Fang (CN113340969 A), and in further view of Boock (US 8583204 B2) and Bhavaraju (EP2941191 B1).
Regarding Claim 8, Stram and Fang teach the limitations of Claim 1. Stram does not explicitly disclose determining, by the processor, a baseline of the sensor based on the enzyme membrane thickness and the glucose limiting membrane thickness, wherein the baseline is an electrical current generated by the sensor at a blood glucose level of zero;
determining, by the processor, a correlation between the baseline and the sensitivity of the sensor, the correlation being a log-log relationship; and
using, by the processor, the correlation to determine a background current of the sensor, wherein the background current is a current that is not glucose related.
In the same field of endeavor, Boock teaches disclose determining, by the processor, a baseline of the sensor based on the enzyme membrane thickness and the glucose limiting membrane thickness, wherein the baseline is an electrical current generated by the sensor at a blood glucose level of zero (e.g. see [Col 13 lines 11-20] “Accordingly, the working electrodes of a dual-electrode system may have varying diffusion, membrane thickness, and diffusion characteristics. As a result, the above-described difference signal (i.e., a glucose-only signal, generated from subtracting the baseline signal from the first signal) may not be completely accurate. To mitigate this, it is contemplated that in some dual-electrode systems, both working electrodes may be fabricated with one or more membranes that each includes a bioprotective layer, which is described in more detail elsewhere herein,” and [Col 28 lines 10-14] “In some embodiments, sensor accuracy may be improved by using a membrane with a bioprotective layer that unexpectedly and substantially reduces the baseline signal, thereby providing not only better overall accuracy, but also better accuracy at the hypoglycemic range,” and [Col 29 lines 15-16] “In certain embodiments, the thickness of the bioprotective domain may be from about 0.1, 0.5, 1, 2, 4, 6, 8 microns”);
determining, by the processor, a correlation between the baseline and the sensitivity of the sensor (e.g. see [Fig. 10A and 10B] and [Col 27 lines 53-61] “A comparison of FIGS. 10A with 10B (both of which are not necessarily drawn to scale) further illustrates this phenomenon. FIG. 10A displays the conversion function of a sensor with a high background signal, while FIG. 10B displays the conversion function of a sensor similar to the sensor associated with FIG. 10A, but with a low background signal. As illustrated, the sensitivities (i.e., the slopes of the conversion function as measured in units of mg/dLpA) of the two sensors are the same,” and [Col 28 lines 1-4] “As can be realized from comparing FIG. 10A with FIG. 10B, the difference in the glucose-signal-to-baseline-signal ratios, between the two sensors, is particularly pronounced in the hypoglycemic range, and less so in the euglycemic range, and even less so in the hyperglycemic range”)
using, by the processor, the correlation to determine a background current of the sensor, wherein the background current is a current that is not glucose related (e.g. see [Col 17 lines 2-14] “For example, in an enzymatic electrochemical analyte sensor, preferably after sensor break-in is complete, the total signal can be divided into an `analyte component,` which is representative of analyte (e.g., glucose) concentration, and a `noise component,` which is caused by non-analyte-related species that have a redox potential that substantially overlaps with the redox potential of the analyte (or measured species, e.g., H.sub.2O.sub.2) at an applied voltage. The noise component can be further divided into its component parts, e.g., constant and non-constant noise. It is not unusual for a sensor to experience a certain level of noise. In general, `constant noise` (also referred to as constant background or baseline”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the sensor calibration method with the thickness, baseline, and background currents of Boock for the purpose of calibration of glucose sensors with the advantage of ensuring the accuracy of the measured signals.
Stram as modified by Fang and Boock does not explicitly disclose the correlation being a log-log relationship. In the same field of endeavor Bhavaraju teaches wherein the correlation being a log-log relationship (e.g. see [0027] “In certain embodiments, self-calibration of an analyte sensor system can be performed by determining sensor sensitivity based on a sensitivity profile (and a measured or estimated baseline),”and [0030] “With the sensors tested in this study, the change in sensor sensitivity (expressed as a percentage of a substantially steady state sensitivity), over a time defined by a sensor session, resembled a logarithmic growth curve”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the sensitivity and baseline embodiments of Stram as modified by Fang and Boock with the log relationship of Bhavaraju for the purpose of calibrating sensors with the advantage of additional data for the purpose of ensuring the calibration determination is correct.
Regarding Claim 19, Stram and Fang teach the limitations of Claim 14. Stram does not explicitly disclose determining, by the processor, a baseline of the sensor based on the enzyme membrane thickness and the glucose limiting membrane thickness, wherein the baseline is an electrical current generated by the sensor at a blood glucose level of zero;
determining, by the processor, a correlation between the baseline and the sensitivity of the sensor, the correlation being a log-log relationship; and
using, by the processor, the correlation to determine a background current of the sensor, wherein the background current is a current that is not glucose related.
In the same field of endeavor, Boock teaches disclose determining, by the processor, a baseline of the sensor based on the enzyme membrane thickness and the glucose limiting membrane thickness, wherein the baseline is an electrical current generated by the sensor at a blood glucose level of zero (e.g. see [Col 13 lines 11-20] “Accordingly, the working electrodes of a dual-electrode system may have varying diffusion, membrane thickness, and diffusion characteristics. As a result, the above-described difference signal (i.e., a glucose-only signal, generated from subtracting the baseline signal from the first signal) may not be completely accurate. To mitigate this, it is contemplated that in some dual-electrode systems, both working electrodes may be fabricated with one or more membranes that each includes a bioprotective layer, which is described in more detail elsewhere herein,” and [Col 28 lines 10-14] “In some embodiments, sensor accuracy may be improved by using a membrane with a bioprotective layer that unexpectedly and substantially reduces the baseline signal, thereby providing not only better overall accuracy, but also better accuracy at the hypoglycemic range,” and [Col 29 lines 15-16] “In certain embodiments, the thickness of the bioprotective domain may be from about 0.1, 0.5, 1, 2, 4, 6, 8 microns”);
determining, by the processor, a correlation between the baseline and the sensitivity of the sensor (e.g. see [Fig. 10A and 10B] and [Col 27 lines 53-61] “A comparison of FIGS. 10A with 10B (both of which are not necessarily drawn to scale) further illustrates this phenomenon. FIG. 10A displays the conversion function of a sensor with a high background signal, while FIG. 10B displays the conversion function of a sensor similar to the sensor associated with FIG. 10A, but with a low background signal. As illustrated, the sensitivities (i.e., the slopes of the conversion function as measured in units of mg/dLpA) of the two sensors are the same,” and [Col 28 lines 1-4] “As can be realized from comparing FIG. 10A with FIG. 10B, the difference in the glucose-signal-to-baseline-signal ratios, between the two sensors, is particularly pronounced in the hypoglycemic range, and less so in the euglycemic range, and even less so in the hyperglycemic range”)
using, by the processor, the correlation to determine a background current of the sensor, wherein the background current is a current that is not glucose related (e.g. see [Col 17 lines 2-14] “For example, in an enzymatic electrochemical analyte sensor, preferably after sensor break-in is complete, the total signal can be divided into an `analyte component,` which is representative of analyte (e.g., glucose) concentration, and a `noise component,` which is caused by non-analyte-related species that have a redox potential that substantially overlaps with the redox potential of the analyte (or measured species, e.g., H.sub.2O.sub.2) at an applied voltage. The noise component can be further divided into its component parts, e.g., constant and non-constant noise. It is not unusual for a sensor to experience a certain level of noise. In general, `constant noise` (also referred to as constant background or baseline”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the sensor calibration method with the thickness, baseline, and background currents of Boock for the purpose of calibration of glucose sensors with the advantage of ensuring the accuracy of the measured signals.
Stram as modified by Fang and Boock does not explicitly disclose the correlation being a log-log relationship. In the same field of endeavor Bhavaraju teaches wherein the correlation being a log-log relationship (e.g. see [0027] “In certain embodiments, self-calibration of an analyte sensor system can be performed by determining sensor sensitivity based on a sensitivity profile (and a measured or estimated baseline),”and [0030] “With the sensors tested in this study, the change in sensor sensitivity (expressed as a percentage of a substantially steady state sensitivity), over a time defined by a sensor session, resembled a logarithmic growth curve”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the sensitivity and baseline embodiments of Stram as modified by Fang and Boock with the log relationship of Bhavaraju for the purpose of calibrating sensors with the advantage of additional data for the purpose of ensuring the calibration determination is correct.
Claims 9-12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Stram (WO2022044018 A2) in view of Fang (CN113340969 A), and in further view of Wang (WO 2017117468 A1); hereinafter “Wang 2017”).
Regarding Claim 9, Stram and Fang teaches the limitations of Claim 1. Stram does not explicitly disclose receiving, by the processor from a transmitter in the CGM system, a confirmation that the sensor of the CGM system is in use by a patient;
identifying, by the processor, the drift profile of the sensor; and
communicating, by the processor to a microprocessor of the CG M system, a new sensitivity for the sensor based on the drift profile of the sensor, wherein the sensor outputs a glucose reading based on drift profile.
In the same field of endeavor, Fang teaches identifying, by the processor, the drift profile of the sensor (e.g. see [pg. 9 last 2 paragraphs] “by the sensitivity attenuation curve L can be obtained, compensation model can be based on the difference of the sensitivity of the reduced Δ L (t) to compensate the measured value of the glucose sensor 1. In some examples, the compensation model can be embedded in the form of software to the electronic system 2”); and communicating, by the processor to a microprocessor of the CGM system, a new sensitivity for the sensor based on the drift profile of the sensor (e.g. see [pg. 9 paragraph 5] “In the formula (IV), A represents the sensitivity of the sensor,” and [pg. 9 paragraph 7] “In some examples, the value of A is based on the sensitivity attenuation curve of the sensor sample 10 to change. Therefore, it can realize the automatic calibration function of the glucose sensor 1.”), wherein the sensor outputs a glucose reading based on drift profile (e.g. see [pg. 10 paragraph 5] “In some examples, in the automatic calibration, the compensation amount can be calculated by the compensation model to calibrate the glucose concentration. Therefore, it can improve the reliability of the measured glucose concentration”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the sensitivity and drift profile embodiments of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
While Fang teaches transmitting data via Bluetooth (e.g. see [pg. 6; second to last paragraph]) Stram as modified by Fang does not explicitly disclose receiving, by the processor from a transmitter in the CGM system, a confirmation that the sensor of the CGM system is in use by a patient.
In the same field of endeavor, Wang 2017 teaches receiving, by the processor from a transmitter in the CGM system (e.g. see [0081] “An output module, which may be integral with and/or operatively connected with the processor, includes programming for generating output based on the sensor data received from the electronics unit (and any processing that incurred in the processor)” and [0074] “The system includes a transcutaneous analyte sensor 200 and an electronics unit (referred to interchangeably as "sensor electronics" or "transmitter") 500 for wirelessly transmitting analyte information to a receiver”), a confirmation that the sensor of the CGM system is in use by a patient (e.g. see [0074] “During use, a sensing portion of the sensor 200 is under the host's skin and a contact portion of the sensor 200 is operatively connected (e.g., electrically connected) to the electronics unit 500. The electronics unit 500 is engaged with a housing which is attached to an adhesive patch fastened to the skin of the host”);
identifying, by the processor, the drift profile of the sensor (e.g. see [0127] “Drift can be driven by a change in permeability of the sensor membrane system, which can be particularly evident in embodiments which use a polyurethane diffusion resistance domain,” and [0130] “in some embodiments, the wetting property of the membrane (and by extension the extent of sensor drift exhibited by the sensor) can be adjusted and/or controlled by creating covalent cross-links between surface-active group-containing polymers, functional-group containing polymers”); and
communicating, by the processor to a microprocessor of the CGM system (e.g. see [0121] “in some embodiments, the processor module includes a microprocessor, however a computer system other than a microprocessor can be used to process data as described herein”).
It would have been obvious to one of ordinary skill in the art to combine the calibration method of Stram as modified by Fang with the transmitting embodiments of Wang 2017 for the purpose of evaluating the accuracy of glucose sensors with the advantage of a streamlined and efficient communication of data.
Regarding claim 10, Stram and Fang teach the limitations of Claim 1. Stram as modified by Fang does not explicitly disclose wherein the interference membrane is formed by an electropolymerization process.
In the same field of endeavor, Wang 2017 teaches wherein the interference membrane is formed by an electropolymerization process (e.g. see [0244] and [0246]).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the interference layer of Stram with the interference formation method of Wang 2017 for the purpose of creating an interference layer with the advantage of substantially reducing the introduction of interferents into the reactive surface.
Regarding Claim 11, Stram and Fang teach the limitations of Claim 1. Stram further discloses measuring interference membrane data including an interference membrane thickness after forming the interference membrane on the working wire (e.g. see [0045] “Following the deposition of layers, a height (i.e., thickness) of the layers can be captured, and the sensor-by-sensor data can be stored (per sensor identifier). The layers’ height (thickness) measurement can be done with optical profder, spectral reflectance monitor, and/or the like”).
Regarding Claim 12, Stram and Fang teach the limitations of Claim 1. Stram does not explicitly disclose wherein the first parameters and the second parameters include at least one of a dip solution viscosity, dip solution temperature, immersion speed, dwell time, withdrawal speed, and airflow.
In the same field of endeavor, Wang 2017 teaches wherein the first parameters and the second parameters include at least one of a dip solution viscosity, dip solution temperature, immersion speed, dwell time, withdrawal speed, and airflow (e.g. see [0090] “Depending on the final thickness of the enzyme layer and solution viscosity (as related to the percent of polymer solid), the coating can be applied in a single step or multiple repeated steps of the chosen process such as dipping to build the desired thickness”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the working wire of Stram with the parameters of Wang 2017 for the purpose of calibrating glucose sensors with the advantage of additional data ensure accuracy.
Regarding Claim 20, Stram and Fang teaches the limitations of Claim 14. Stram does not explicitly disclose receiving, by the processor from a transmitter in the CGM system, a confirmation that the sensor of the CGM system is in use by a patient;
identifying, by the processor, the drift profile of the sensor; and
communicating, by the processor to a microprocessor of the CG M system, a new sensitivity for the sensor based on the drift profile of the sensor, wherein the sensor outputs a glucose reading based on drift profile.
In the same field of endeavor, Fang teaches identifying, by the processor, the drift profile of the sensor (e.g. see [pg. 9 last 2 paragraphs] “by the sensitivity attenuation curve L can be obtained, compensation model can be based on the difference of the sensitivity of the reduced Δ L (t) to compensate the measured value of the glucose sensor 1. In some examples, the compensation model can be embedded in the form of software to the electronic system 2”); and communicating, by the processor to a microprocessor of the CGM system, a new sensitivity for the sensor based on the drift profile of the sensor (e.g. see [pg. 9 paragraph 5] “In the formula (IV), A represents the sensitivity of the sensor,” and [pg. 9 paragraph 7] “In some examples, the value of A is based on the sensitivity attenuation curve of the sensor sample 10 to change. Therefore, it can realize the automatic calibration function of the glucose sensor 1.”), wherein the sensor outputs a glucose reading based on drift profile (e.g. see [pg. 10 paragraph 5] “In some examples, in the automatic calibration, the compensation amount can be calculated by the compensation model to calibrate the glucose concentration. Therefore, it can improve the reliability of the measured glucose concentration”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calibration method of Stram with the sensitivity and drift profile embodiments of Fang for the purpose of calibrating a glucose monitoring system with the advantage of additional data to ensure the accuracy of glucose readings.
While Fang teaches transmitting data via Bluetooth (e.g. see [pg. 6; second to last paragraph]) Stram as modified by Fang does not explicitly disclose receiving, by the processor from a transmitter in the CGM system, a confirmation that the sensor of the CGM system is in use by a patient.
In the same field of endeavor, Wang 2017 teaches receiving, by the processor from a transmitter in the CGM system (e.g. see [0081] “An output module, which may be integral with and/or operatively connected with the processor, includes programming for generating output based on the sensor data received from the electronics unit (and any processing that incurred in the processor)” and [0074] “The system includes a transcutaneous analyte sensor 200 and an electronics unit (referred to interchangeably as "sensor electronics" or "transmitter") 500 for wirelessly transmitting analyte information to a receiver”), a confirmation that the sensor of the CGM system is in use by a patient (e.g. see [0074] “During use, a sensing portion of the sensor 200 is under the host's skin and a contact portion of the sensor 200 is operatively connected (e.g., electrically connected) to the electronics unit 500. The electronics unit 500 is engaged with a housing which is attached to an adhesive patch fastened to the skin of the host”);
identifying, by the processor, the drift profile of the sensor (e.g. see [0127] “Drift can be driven by a change in permeability of the sensor membrane system, which can be particularly evident in embodiments which use a polyurethane diffusion resistance domain,” and [0130] “in some embodiments, the wetting property of the membrane (and by extension the extent of sensor drift exhibited by the sensor) can be adjusted and/or controlled by creating covalent cross-links between surface-active group-containing polymers, functional-group containing polymers”); and
communicating, by the processor to a microprocessor of the CGM system (e.g. see [0121] “in some embodiments, the processor module includes a microprocessor, however a computer system other than a microprocessor can be used to process data as described herein”).
It would have been obvious to one of ordinary skill in the art to combine the calibration method of Stram as modified by Fang with the transmitting embodiments of Wang 2017 for the purpose of evaluating the accuracy of glucose sensors with the advantage of a streamlined and efficient communication of data.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Stram (WO2022044018 A2) in view of Fang (CN113340969 A), and in further view of Scott (WO2019035073 A2) and Wang (US20180199873).
Regarding Claim 13, Stram and Fang teach the limitations of Claim 1. Stram does not explicitly disclose wherein each dip of the working wire comprises: dipping the working wire into a first dip solution according to the first parameters for the forming of the enzyme membrane or dipping the working wire into a second dip solution according to the second parameters for the forming of the glucose limiting membrane; measuring, as an in-line process, a plurality of diameters along a length of the working wire using an automated measurement system; determining, by the processor in communication with the automated measurement system, a thickness difference, the thickness difference being a difference between a thickness setpoint and an aggregate criteria for the plurality of diameters; and calculating, by the processor, adjusted parameters for the dipping process based on the thickness difference.
In the same field of endeavor, Fang teaches wherein each dip of the working wire comprises: dipping the working wire into a first dip solution according to the first parameters for the forming of the enzyme membrane or dipping the working wire into a second dip solution according to the second parameters for the forming of the glucose limiting membrane (e.g. see [pg. 5 paragraph 5] “In some examples, it further comprises through spin coating, dipping and pulling, at least one process of drop coating and spraying process to realize the consistency process parameter. Therefore, it can improve the consistency of glucose sensor 1. In some examples, for the glucose enzyme layer 112, semipermeable membrane 113 and so on, can be controlled by the spin coating speed and quantity and so on,”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the working wire of Stram with the dipping process of Fang for the purpose of obtaining glucose measurements with the advantage of ensuring uniform formation of working wire layers.
Stram as modified by Fang does not explicitly disclose measuring, as an in-line process, a plurality of diameters along a length of the working wire using an automated measurement system; determining, by the processor in communication with the automated measurement system, a thickness difference, the thickness difference being a difference between a thickness setpoint and an aggregate criteria for the plurality of diameters; and calculating, by the processor, adjusted parameters for the dipping process based on the thickness difference.
In the same field of endeavor, Scott teaches measuring, as an in-line process, a plurality of diameters along a length of the working wire using an automated measurement system (e.g. see [0351] “In this example, the total lateral thickness of the sensor is measured at multiple locations at and near the sensing region”); determining, by the processor in communication with the automated measurement system, a thickness difference, the thickness difference being a difference between a thickness setpoint and an aggregate criteria for the plurality of diameters (e.g. see [0351] “A representative value for the sensor thickness beneath the membrane (e.g., a nominal substrate thickness) is then subtracted from the average value to provide the average membrane thickness for each particular sensor, which was then used as the representation of the thickness of the membrane”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the working wire of Stram with the thickness difference of Scott for the purpose of obtaining glucose measurements with the advantage of ensuring uniform formation of working wire layers.
Stram as modified by Fang and Scott does not explicitly disclose calculating, by the processor, adjusted parameters for the dipping process based on the thickness difference. In the same field of endeavor, Wang teaches calculating, by the processor, adjusted parameters for the dipping process based on the thickness difference (e.g. see [0221] “In some embodiments, the dip process can be repeated at least one time and up to 10 times or more. In other embodiments, only one dip is performed. The number of repeated dip processes used depends upon the cellulosic derivative(s) used, their concentration, conditions during deposition (e.g., dipping) and the desired thickness (e.g., sufficient thickness to provide functional blocking of certain interferents), and the like”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the working wire of Stram with the dipping process of Wang for the purpose of obtaining glucose measurements with the advantage of ensuring uniform formation of working wire layers.
Conclusion
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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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