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 .
Election/Restrictions
Applicant’s election without traverse of claims 1-17 in the reply filed on May 04, 2026 is acknowledged.
Status of Claims
This office action for the 18/451530 application is in response to the communications filed May 04, 2025.
Claims 1-20 were initially submitted August 17, 2023.
Claims 1-20 were subject to restriction requirement March 05, 2026.
Claims 1-17 were elected without traverse May 04, 2026.
Claims 18-20 were withdrawn from consideration May 04, 2026,
Claims 1-17 are currently pending and considered below.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Desborough et al. (US 2017/0232195; herein referred to as Desborough).
As per claim 1,
Desborough discloses an insulin delivery device for delivering insulin to a user, comprising: a non-transitory computer-readable storage medium storing computer programming instructions; and a processor for executing the computer programming instructions:
(Paragraphs [0024], [0025] and [0063] of Desborough. The teaching describes that the plurality of glucose sensor data points may be obtained from one of a continuous glucose monitor (CGM) or a blood glucose monitor (BGM). The present disclosure may include a system that includes a glucose a configured to generate a plurality of glucose sensor data points, and a control device. The control device may additionally be configured to generate a signal to deliver a second dose of insulin using the insulin pump for a fourth time interval after the end of the second time interval, the fourth time interval being shorter than the third time interval. The system may also include an insulin pump configured to deliver insulin based on the signal of the control device. In one or more embodiments, the present disclosure may include a non-transitory computer readable medium containing instructions that, when executed by a processor, are configured to perform operations.)
Desborough further discloses determine an insulin dose to be delivered to the user with the insulin delivery device that has a lowest glucose cost, the glucose cost being determined based on effective glucose level values for the user and the effective glucose level values being determined by applying a logarithmic transform to predicted glucose level values for a time horizon:
(Paragraph [0096] of Desborough. The teaching describes methods and systems provided herein can evaluate each basal insulin delivery profile or rate to select the profile or rate that minimizes a variation from the one or more blood glucose targets using any appropriate method. In some cases, methods and systems provided herein can use a cost function to evaluate differences between the predicted blood glucose values for each basal insulin delivery profile or rate and blood glucose targets, potentially specified for a diurnal time segment. Methods and systems provided herein can then select a basal profile or rate that produces the lowest cost function value. Methods and systems provided herein can use any suitable cost function. In some cases, cost functions can sum the absolute value of the difference between each predicted blood glucose value and each blood glucose target. In some cases, cost functions used in methods and systems provided herein can use square of the difference. In some cases, cost functions used in methods and systems provided herein can assign a higher cost to blood glucose values below the blood glucose target in order reduce the risk of a hypoglycemic event. In some cases, the cost function can include a summation of the absolute values of a plurality of predicted deviations, squared deviations, log squared deviations, or a combination thereof. In some cases, a cost function can include variables unrelated to the predicted blood glucose values. For example, a cost function can include a penalty for profiles that do not deliver 100% of the BBR, thus adding a slight preference to use 100% of BBR. In some cases, methods and systems provided herein can include a cost function that provides a slight preference to keep the existing basal modification for every other interval (e.g., a second 15 minute segment), which could reduce the variability in basal insulin delivery rates in typical situations, but allow for more critical adjustments.)
(Paragraphs [0128]-[0132] of Desborough. The teaching describes estimate a first future blood glucose a model as depicted in FIG. 3. In some cases, blood glucose can be approximated using two determinist Integrating first order plus dead time (FOPDT) models for the effect of carbohydrates and insulin, combined with an autoregressive (AR2) disturbance model. From the equation above, the third element may represent the effect on blood glucose due to disturbances (e.g., the AR2 disturbance model) and may be based on the a log-transformed AR2 model)
Desborough further discloses cause the insulin dose to be delivered by the insulin delivery device to the user:
(Paragraph [0203] of Desborough. The teaching describes that insulin can be delivered based on the modified target blood glucose level. For example, a control device can determine insulin delivery profiles or rates based the target blood glucose level(s) using any suitable method, including the methods described above. In some cases, the delivery of insulin can be based off of one or more insulin delivery profiles that can be generated, and selecting one of the profiles that most closely approximates a target blood glucose level. In these and other cases, the actions of the delivery profiles can be a ratio of the modified BBR. For example, the delivery actions can include one of delivering 0×, 1×, or 2× the modified BBR.)
As per claim 2,
Desborough discloses the limitations of claim 1.
Desborough further discloses wherein the predicted glucose level values are predicted from previous glucose level values of the user for earlier times and/or from predicted glucose level values for the user for earlier times when previous glucose level values are not yet available:
(Paragraphs [0181]-[0185] of Desborough. The teaching describes that a prediction can be made of future blood glucose levels for each of the delivery profiles. For example, the pump assembly 15 and/or the mobile computing device 60 of FIG. 1 can generate a prediction of future blood glucose levels at various points in time if a particular profile is followed. Such prediction may be based on the effect of glucose, insulin, carbohydrates, and/or other disturbances projected for the blood glucose levels at the various points in time. Such user-specific dosage parameters may include, but are not limited to, one or more of the following: total daily basal dosage limits (e.g., in a maximum number of units/day), various other periodic basal dosage limits (e.g., maximum basal dosage/hour, maximum basal dosage/six hour period), insulin sensitivity (e.g., in units of mg/dL/insulin unit), carbohydrate ratio (e.g., in units of g/insulin unit), insulin onset time (e.g., in units of minutes and/or seconds), insulin on board duration (e.g., in units of minutes and/or seconds), and basal rate profile (e.g., an average basal rate or one or more segments of a basal rate profile expressed in units of insulin unit/hour). Also, the control circuitry 240 can cause the memory device 242 to store (and can cause reusable pump controller 200 to periodically communicate out to the mobile computing device 60) any of the following parameters derived from the historical pump usage information: dosage logs, average total daily dose, average total basal dose per day, average total bolus dose per day, a ratio of correction bolus amount per day to food bolus amount per day, amount of correction boluses per day, a ratio of a correction bolus amount per day to the average total daily dose, a ratio of the average total basal dose to the average total bolus dose, average maximum bolus per day, and a frequency of cannula and tube primes per day. To the extent these aforementioned dosage parameters or historical parameters are not stored in the memory device 242, the control circuitry 240 can be configured to calculate any of these aforementioned dosage parameters or historical parameters from other data stored in the memory device 242 or otherwise input via communication with the mobile computing device 60.)
As per claim 3,
The insulin delivery device of claim 2.
Desborough further discloses wherein the non-transitory computer-readable storage medium stores a glucose level history of the user and wherein the glucose level values of the user for times before an earliest time in the time horizon are part of the glucose level history:
(Paragraphs [0181]-[0185] of Desborough. The teaching describes that a prediction can be made of future blood glucose levels for each of the delivery profiles. For example, the pump assembly 15 and/or the mobile computing device 60 of FIG. 1 can generate a prediction of future blood glucose levels at various points in time if a particular profile is followed. Such prediction may be based on the effect of glucose, insulin, carbohydrates, and/or other disturbances projected for the blood glucose levels at the various points in time. Such user-specific dosage parameters may include, but are not limited to, one or more of the following: total daily basal dosage limits (e.g., in a maximum number of units/day), various other periodic basal dosage limits (e.g., maximum basal dosage/hour, maximum basal dosage/six hour period), insulin sensitivity (e.g., in units of mg/dL/insulin unit), carbohydrate ratio (e.g., in units of g/insulin unit), insulin onset time (e.g., in units of minutes and/or seconds), insulin on board duration (e.g., in units of minutes and/or seconds), and basal rate profile (e.g., an average basal rate or one or more segments of a basal rate profile expressed in units of insulin unit/hour). Also, the control circuitry 240 can cause the memory device 242 to store (and can cause reusable pump controller 200 to periodically communicate out to the mobile computing device 60) any of the following parameters derived from the historical pump usage information: dosage logs, average total daily dose, average total basal dose per day, average total bolus dose per day, a ratio of correction bolus amount per day to food bolus amount per day, amount of correction boluses per day, a ratio of a correction bolus amount per day to the average total daily dose, a ratio of the average total basal dose to the average total bolus dose, average maximum bolus per day, and a frequency of cannula and tube primes per day. To the extent these aforementioned dosage parameters or historical parameters are not stored in the memory device 242, the control circuitry 240 can be configured to calculate any of these aforementioned dosage parameters or historical parameters from other data stored in the memory device 242 or otherwise input via communication with the mobile computing device 60.)
As per claim 4,
Desborough discloses the limitations of claim 1.
Desborough further discloses further comprising an insulin reservoir for holding insulin for delivering the insulin to the user:
(Paragraph [0105] of Desborough. The teaching describes pump assembly 15, as shown, can include reusable pump controller 200 and a disposable pump 100, which can contain a reservoir for retaining insulin. A drive system for pushing insulin out of the reservoir can be included in either the disposable pump 100 or the reusable pump controller 200 in a controller housing 210. Reusable pump controller 200 can include a wireless communication device 247, which can be adapted to communicate with a wireless communication device 54 of continuous glucose monitor 50 and other diabetes devices in the system, such as those discussed below. In some cases, pump assembly 15 can be sized to fit within a palm of a hand 5. Pump assembly 15 can include an infusion set 146. Infusion set 146 can include a flexible tube 147 that extends from the disposable pump 100 to a subcutaneous cannula 149 that may be retained by a skin adhesive patch (not shown) that secures the subcutaneous cannula 149 to the infusion site. The skin adhesive patch can retain the cannula 149 in fluid communication with the tissue or vasculature of the PWD so that the medicine dispensed through tube 147 passes through the cannula 149 and into the PWD's body. The cap device 130 can provide fluid communication between an output end of an insulin cartridge (not shown) and tube 147 of infusion set 146. Although pump assembly 15 is depicted as a two-part insulin pump, one piece insulin pumps are also contemplated. Additionally, insulin pump assemblies used in methods and systems provided herein can alternatively be a patch pump.)
As per claim 5,
Desborough discloses the limitations of claim 4.
Desborough further discloses wherein the computer programming instructions, when executed by the processor, cause the processor to initiate delivery of the insulin dose from the reservoir to the user:
(Paragraph [0105] of Desborough. The teaching describes pump assembly 15, as shown, can include reusable pump controller 200 and a disposable pump 100, which can contain a reservoir for retaining insulin. A drive system for pushing insulin out of the reservoir can be included in either the disposable pump 100 or the reusable pump controller 200 in a controller housing 210. Reusable pump controller 200 can include a wireless communication device 247, which can be adapted to communicate with a wireless communication device 54 of continuous glucose monitor 50 and other diabetes devices in the system, such as those discussed below. In some cases, pump assembly 15 can be sized to fit within a palm of a hand 5. Pump assembly 15 can include an infusion set 146. Infusion set 146 can include a flexible tube 147 that extends from the disposable pump 100 to a subcutaneous cannula 149 that may be retained by a skin adhesive patch (not shown) that secures the subcutaneous cannula 149 to the infusion site. The skin adhesive patch can retain the cannula 149 in fluid communication with the tissue or vasculature of the PWD so that the medicine dispensed through tube 147 passes through the cannula 149 and into the PWD's body. The cap device 130 can provide fluid communication between an output end of an insulin cartridge (not shown) and tube 147 of infusion set 146. Although pump assembly 15 is depicted as a two-part insulin pump, one piece insulin pumps are also contemplated. Additionally, insulin pump assemblies used in methods and systems provided herein can alternatively be a patch pump.)
As per claim 6,
Desborough discloses the limitations of claim 1.
Desborough further discloses wherein the processor in applying a logarithmic transform to predicted glucose level values for the time horizon applies a logarithmic function to the predicted glucose level values:
(Paragraphs [0128]-[0136] of Desborough. The teaching describes estimate a first future blood glucose a model as depicted in FIG. 3. In some cases, blood glucose can be approximated using two determinist Integrating first order plus dead time (FOPDT) models for the effect of carbohydrates and insulin, combined with an autoregressive (AR2) disturbance model. From the equation above, the third element may represent the effect on blood glucose due to disturbances (e.g., the AR2 disturbance model) and may be based on the a log-transformed AR2 model)
As per claim 7,
Desborough discloses the limitations of claim 6.
Desborough further discloses wherein the applying the logarithmic transform comprises, for each of the predicted glucose level values, entails applying a logarithmic function to a product of a scaling factor and a ratio of the predicted glucose level value to the set point glucose level value to yield a logarithmic value:
(Paragraphs [0128]-[0136] of Desborough. The teaching describes estimate a first future blood glucose a model as depicted in FIG. 3. In some cases, blood glucose can be approximated using two determinist Integrating first order plus dead time (FOPDT) models for the effect of carbohydrates and insulin, combined with an autoregressive (AR2) disturbance model. From the equation above, the third element may represent the effect on blood glucose due to disturbances (e.g., the AR2 disturbance model) and may be based on the a log-transformed AR2 model)
As per claim 8,
Desborough discloses the limitations of claim 7.
Desborough further discloses wherein the applying the applying the logarithmic transform further comprises multiplying the logarithmic value by the set point glucose level value to produce one of the predicted logarithmic glucose level values:
(Paragraphs [0128]-[0136] of Desborough. The teaching describes estimate a first future blood glucose a model as depicted in FIG. 3. In some cases, blood glucose can be approximated using two determinist Integrating first order plus dead time (FOPDT) models for the effect of carbohydrates and insulin, combined with an autoregressive (AR2) disturbance model. From the equation above, the third element may represent the effect on blood glucose due to disturbances (e.g., the AR2 disturbance model) and may be based on the a log-transformed AR2 model)
As per claim 9,
Desborough discloses the limitations of claim 8.
Desborough further discloses wherein the glucose cost is a product of a coefficient and a sum of squared differences between each of the projected glucose level values and the set point glucose level value over the time horizon:
(Paragraphs [0128]-[0136] of Desborough. The teaching describes estimate a first future blood glucose a model as depicted in FIG. 3. In some cases, blood glucose can be approximated using two determinist Integrating first order plus dead time (FOPDT) models for the effect of carbohydrates and insulin, combined with an autoregressive (AR2) disturbance model. From the equation above, the third element may represent the effect on blood glucose due to disturbances (e.g., the AR2 disturbance model) and may be based on the a log-transformed AR2 model)
As per claim 10,
Desborough discloses an insulin delivery system for delivering insulin to a user, comprising: a non-transitory computer-readable storage medium storing computer programming instructions; and a processor for executing the computer programming instructions:
(Paragraphs [0024], [0025] and [0063] of Desborough. The teaching describes that the plurality of glucose sensor data points may be obtained from one of a continuous glucose monitor (CGM) or a blood glucose monitor (BGM). The present disclosure may include a system that includes a glucose a configured to generate a plurality of glucose sensor data points, and a control device. The control device may additionally be configured to generate a signal to deliver a second dose of insulin using the insulin pump for a fourth time interval after the end of the second time interval, the fourth time interval being shorter than the third time interval. The system may also include an insulin pump configured to deliver insulin based on the signal of the control device. In one or more embodiments, the present disclosure may include a non-transitory computer readable medium containing instructions that, when executed by a processor, are configured to perform operations.)
Desborough further discloses choose a selected one of candidate insulin dosages that has a best cost value when a cost function is applied to the candidate insulin dosages for delivery by the insulin delivery device to the user; wherein the cost function includes an insulin cost component and a glucose cost component, wherein the glucose cost component is based on cumulative differences between predicted effective glucose level values and set point glucose level values over a time horizon;:
(Paragraph [0096] of Desborough. The teaching describes methods and systems provided herein can evaluate each basal insulin delivery profile or rate to select the profile or rate that minimizes a variation from the one or more blood glucose targets using any appropriate method. In some cases, methods and systems provided herein can use a cost function to evaluate differences between the predicted blood glucose values for each basal insulin delivery profile or rate and blood glucose targets, potentially specified for a diurnal time segment. Methods and systems provided herein can then select a basal profile or rate that produces the lowest cost function value. Methods and systems provided herein can use any suitable cost function. In some cases, cost functions can sum the absolute value of the difference between each predicted blood glucose value and each blood glucose target. In some cases, cost functions used in methods and systems provided herein can use square of the difference. In some cases, cost functions used in methods and systems provided herein can assign a higher cost to blood glucose values below the blood glucose target in order reduce the risk of a hypoglycemic event. In some cases, the cost function can include a summation of the absolute values of a plurality of predicted deviations, squared deviations, log squared deviations, or a combination thereof. In some cases, a cost function can include variables unrelated to the predicted blood glucose values. For example, a cost function can include a penalty for profiles that do not deliver 100% of the BBR, thus adding a slight preference to use 100% of BBR. In some cases, methods and systems provided herein can include a cost function that provides a slight preference to keep the existing basal modification for every other interval (e.g., a second 15 minute segment), which could reduce the variability in basal insulin delivery rates in typical situations, but allow for more critical adjustments.)
Desborough further discloses wherein the predicted effective glucose level values are determined by applying a logarithmic function to predicted glucose level values of the user over the time horizon:
(Paragraphs [0128]-[0132] of Desborough. The teaching describes estimate a first future blood glucose a model as depicted in FIG. 3. In some cases, blood glucose can be approximated using two determinist Integrating first order plus dead time (FOPDT) models for the effect of carbohydrates and insulin, combined with an autoregressive (AR2) disturbance model. From the equation above, the third element may represent the effect on blood glucose due to disturbances (e.g., the AR2 disturbance model) and may be based on the a log-transformed AR2 model)
Desborough further discloses cause delivery of the selected one of the candidate insulin dosages to the user:
(Paragraph [0203] of Desborough. The teaching describes that insulin can be delivered based on the modified target blood glucose level. For example, a control device can determine insulin delivery profiles or rates based the target blood glucose level(s) using any suitable method, including the methods described above. In some cases, the delivery of insulin can be based off of one or more insulin delivery profiles that can be generated, and selecting one of the profiles that most closely approximates a target blood glucose level. In these and other cases, the actions of the delivery profiles can be a ratio of the modified BBR. For example, the delivery actions can include one of delivering 0×, 1×, or 2× the modified BBR.)
As per claim 11,
Desborough discloses the limitations of claim 10.
Desborough further discloses wherein at least one of the predicted glucose level values is determined by the processor from glucose level values of the user for points in time before the time horizon:
(Paragraphs [0181]-[0185] of Desborough. The teaching describes that a prediction can be made of future blood glucose levels for each of the delivery profiles. For example, the pump assembly 15 and/or the mobile computing device 60 of FIG. 1 can generate a prediction of future blood glucose levels at various points in time if a particular profile is followed. Such prediction may be based on the effect of glucose, insulin, carbohydrates, and/or other disturbances projected for the blood glucose levels at the various points in time. Such user-specific dosage parameters may include, but are not limited to, one or more of the following: total daily basal dosage limits (e.g., in a maximum number of units/day), various other periodic basal dosage limits (e.g., maximum basal dosage/hour, maximum basal dosage/six hour period), insulin sensitivity (e.g., in units of mg/dL/insulin unit), carbohydrate ratio (e.g., in units of g/insulin unit), insulin onset time (e.g., in units of minutes and/or seconds), insulin on board duration (e.g., in units of minutes and/or seconds), and basal rate profile (e.g., an average basal rate or one or more segments of a basal rate profile expressed in units of insulin unit/hour). Also, the control circuitry 240 can cause the memory device 242 to store (and can cause reusable pump controller 200 to periodically communicate out to the mobile computing device 60) any of the following parameters derived from the historical pump usage information: dosage logs, average total daily dose, average total basal dose per day, average total bolus dose per day, a ratio of correction bolus amount per day to food bolus amount per day, amount of correction boluses per day, a ratio of a correction bolus amount per day to the average total daily dose, a ratio of the average total basal dose to the average total bolus dose, average maximum bolus per day, and a frequency of cannula and tube primes per day. To the extent these aforementioned dosage parameters or historical parameters are not stored in the memory device 242, the control circuitry 240 can be configured to calculate any of these aforementioned dosage parameters or historical parameters from other data stored in the memory device 242 or otherwise input via communication with the mobile computing device 60.)
As per claim 12,
Desborough discloses the limitations of claim 10.
Desborough further discloses further comprising an insulin reservoir for holding insulin:
(Paragraph [0105] of Desborough. The teaching describes pump assembly 15, as shown, can include reusable pump controller 200 and a disposable pump 100, which can contain a reservoir for retaining insulin. A drive system for pushing insulin out of the reservoir can be included in either the disposable pump 100 or the reusable pump controller 200 in a controller housing 210. Reusable pump controller 200 can include a wireless communication device 247, which can be adapted to communicate with a wireless communication device 54 of continuous glucose monitor 50 and other diabetes devices in the system, such as those discussed below. In some cases, pump assembly 15 can be sized to fit within a palm of a hand 5. Pump assembly 15 can include an infusion set 146. Infusion set 146 can include a flexible tube 147 that extends from the disposable pump 100 to a subcutaneous cannula 149 that may be retained by a skin adhesive patch (not shown) that secures the subcutaneous cannula 149 to the infusion site. The skin adhesive patch can retain the cannula 149 in fluid communication with the tissue or vasculature of the PWD so that the medicine dispensed through tube 147 passes through the cannula 149 and into the PWD's body. The cap device 130 can provide fluid communication between an output end of an insulin cartridge (not shown) and tube 147 of infusion set 146. Although pump assembly 15 is depicted as a two-part insulin pump, one piece insulin pumps are also contemplated. Additionally, insulin pump assemblies used in methods and systems provided herein can alternatively be a patch pump.)
As per claim 13,
Desborough discloses the limitations of claim 12.
Desborough further discloses wherein the computer programming instructions, when executed by the processor, cause the processor to initiate delivery of the selected one of the candidate insulin dosages from the reservoir to the user:
(Paragraph [0105] of Desborough. The teaching describes pump assembly 15, as shown, can include reusable pump controller 200 and a disposable pump 100, which can contain a reservoir for retaining insulin. A drive system for pushing insulin out of the reservoir can be included in either the disposable pump 100 or the reusable pump controller 200 in a controller housing 210. Reusable pump controller 200 can include a wireless communication device 247, which can be adapted to communicate with a wireless communication device 54 of continuous glucose monitor 50 and other diabetes devices in the system, such as those discussed below. In some cases, pump assembly 15 can be sized to fit within a palm of a hand 5. Pump assembly 15 can include an infusion set 146. Infusion set 146 can include a flexible tube 147 that extends from the disposable pump 100 to a subcutaneous cannula 149 that may be retained by a skin adhesive patch (not shown) that secures the subcutaneous cannula 149 to the infusion site. The skin adhesive patch can retain the cannula 149 in fluid communication with the tissue or vasculature of the PWD so that the medicine dispensed through tube 147 passes through the cannula 149 and into the PWD's body. The cap device 130 can provide fluid communication between an output end of an insulin cartridge (not shown) and tube 147 of infusion set 146. Although pump assembly 15 is depicted as a two-part insulin pump, one piece insulin pumps are also contemplated. Additionally, insulin pump assemblies used in methods and systems provided herein can alternatively be a patch pump.)
As per claim 14,
Desborough discloses the limitations of claim 13.
Desborough further discloses wherein the predicted glucose level values are determined by the processor from glucose level values before the time horizon and/or predicted glucose level values:
(Paragraphs [0181]-[0185] of Desborough. The teaching describes that a prediction can be made of future blood glucose levels for each of the delivery profiles. For example, the pump assembly 15 and/or the mobile computing device 60 of FIG. 1 can generate a prediction of future blood glucose levels at various points in time if a particular profile is followed. Such prediction may be based on the effect of glucose, insulin, carbohydrates, and/or other disturbances projected for the blood glucose levels at the various points in time. Such user-specific dosage parameters may include, but are not limited to, one or more of the following: total daily basal dosage limits (e.g., in a maximum number of units/day), various other periodic basal dosage limits (e.g., maximum basal dosage/hour, maximum basal dosage/six hour period), insulin sensitivity (e.g., in units of mg/dL/insulin unit), carbohydrate ratio (e.g., in units of g/insulin unit), insulin onset time (e.g., in units of minutes and/or seconds), insulin on board duration (e.g., in units of minutes and/or seconds), and basal rate profile (e.g., an average basal rate or one or more segments of a basal rate profile expressed in units of insulin unit/hour). Also, the control circuitry 240 can cause the memory device 242 to store (and can cause reusable pump controller 200 to periodically communicate out to the mobile computing device 60) any of the following parameters derived from the historical pump usage information: dosage logs, average total daily dose, average total basal dose per day, average total bolus dose per day, a ratio of correction bolus amount per day to food bolus amount per day, amount of correction boluses per day, a ratio of a correction bolus amount per day to the average total daily dose, a ratio of the average total basal dose to the average total bolus dose, average maximum bolus per day, and a frequency of cannula and tube primes per day. To the extent these aforementioned dosage parameters or historical parameters are not stored in the memory device 242, the control circuitry 240 can be configured to calculate any of these aforementioned dosage parameters or historical parameters from other data stored in the memory device 242 or otherwise input via communication with the mobile computing device 60.)
As per claim 15,
Desborough discloses the limitations of claim 14.
Desborough further discloses wherein each of the predicted effective glucose level values is determined by applying a logarithmic function to a product of a scaling factor and a ratio of a corresponding predicted glucose level value to one of the set point glucose level values to yield a logarithmic value:
(Paragraphs [0128]-[0136] of Desborough. The teaching describes estimate a first future blood glucose a model as depicted in FIG. 3. In some cases, blood glucose can be approximated using two determinist Integrating first order plus dead time (FOPDT) models for the effect of carbohydrates and insulin, combined with an autoregressive (AR2) disturbance model. From the equation above, the third element may represent the effect on blood glucose due to disturbances (e.g., the AR2 disturbance model) and may be based on the a log-transformed AR2 model)
As per claim 16,
Desborough discloses the limitations of claim 15.
Desborough further discloses wherein the computer programming instructions, when executed by the processor, further cause the processor to multiply the logarithmic value by the set point glucose level value to produce one of the predicted logarithmic projected glucose level values:
(Paragraphs [0128]-[0136] of Desborough. The teaching describes estimate a first future blood glucose a model as depicted in FIG. 3. In some cases, blood glucose can be approximated using two determinist Integrating first order plus dead time (FOPDT) models for the effect of carbohydrates and insulin, combined with an autoregressive (AR2) disturbance model. From the equation above, the third element may represent the effect on blood glucose due to disturbances (e.g., the AR2 disturbance model) and may be based on the a log-transformed AR2 model)
As per claim 17,
Desborough discloses the limitations of claim 16.
Desborough further discloses wherein the glucose cost value is a product of a coefficient and a sum of squared differences between each of the projected glucose level values and the set point glucose level values over the time horizon:
(Paragraphs [0128]-[0136] of Desborough. The teaching describes estimate a first future blood glucose a model as depicted in FIG. 3. In some cases, blood glucose can be approximated using two determinist Integrating first order plus dead time (FOPDT) models for the effect of carbohydrates and insulin, combined with an autoregressive (AR2) disturbance model. From the equation above, the third element may represent the effect on blood glucose due to disturbances (e.g., the AR2 disturbance model) and may be based on the a log-transformed AR2 model)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER H. CHOI can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681
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