DETAILED ACTION
Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Final Office Action is in Reply to the arguments/amendment (hereinafter “Response”) dated 06/10/2026. Claim(s) 1-20 are presently pending. Claim(s) 1, 2, and 16 is/are amended.
Response to Amendment
The rejection of claim(s) 2 under 35 U.S.C. 112(b) is/are withdrawn in light of the submitted amendment to the claims.
Response to Arguments
Regarding the rejection of claim(s) 1-11 and 14-20 under 35 U.S.C. 102(a)(1) as being anticipated by Cinar (U.S. Pat. Pub. No. 2011/0106011 A1), and of claims 12-13 under 35 U.S.C. 103 as being unpatentable over Cinar in view of Reifman (U.S. Pat. Pub. No. 2011/0160555 A1), the applicant(s) argues that Cinar does not teach the limitation “determine values of weights for the past glucose levels of the user based on a glucose history of the user”, as required by the amended claim 1. Specifically, applicant(s) asserts that the forgetting factor λ may not be considered as a “weight for a past glucose level”, thereby apparently alleging that the Office considers the forgetting factor λ as mapping to the claimed weights for past glucose levels.
The Office respectfully considers this argument not persuasive. The cited limitation was not present in the original claim 1 (original claim 1 having no mention at all of weighted values), but is present in original claim 8. In regards to the limitation found in original claim 8, the Office action of 03/10/2026 makes no mention of the forgetting factor λ, much less assigns this parameter as mapping to the claimed weights for the past glucose levels of the user.” Instead, the Office action explained that a recursive least squares method is used to determine weights/estimated model parameters that are applied to past glucose level data in Cinar (see Non-Final Rejection, para. 14). Here, as a part of this recursive least squares method, the forgetting factor λ is used as a meta-parameter for influencing the nature of the weights/estimated model parameters (the degree to which they favor newer data over older data) – i.e. a piece contributing to the determining of the weights/estimated model parameters, not the weights themselves. Applicant(s) assertion, therefore, that the forgetting factor λ may not be considered as a “weight for a past glucose level” is valid, but not relevant to the Office’s position as presented in the prior Office action of 03/10/2026. Nevertheless, in order to avoid any unclarity in the Office’s position, the Office has reconfigured the prior rejection of claims 1-11 and 14-19 under 35 U.S.C. 102(a)(1) as being anticipated by Cinar (see below revised rejections).
The applicant also argues that, in regards to claim 7, Cinar does not disclose that “the glucose prediction model ignores how much insulin has been delivered to the user.” Specifically, applicant alleges that the glucose prediction model as it is configured for use within the closed-loop glucose control system of Cinar (such that it may be used in determining a basal insulin delivery dosage as claimed) requires the input of previous insulin flow rates.
The Office respectfully considers this argument persuasive. Therefore, the rejection of claim 7 under 35 U.S.C. 102 as being anticipated by Cinar has been withdrawn. However, upon further consideration, and in response to applicant(s) incorporation of newly claimed subject matter into claim 1, a new ground(s) of rejection is made regarding amended claims 1-7 and 16-20 under 35 USC 103 as being unpatentable over Palerm (U.S. Pat. Pub. No. 2015/0165119 A1) in view of Cinar.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 18 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “more recent” in Claim 18 is a relative term which renders the claim indefinite. The term “more recent” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Because the term “more” is comparative, but no standard for comparison has been provided within the claim or specification, it is thus unclear how recently a “more recent” past glucose level must be in order to meet the limitation. Appropriate clarification or correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 8-11, and 14-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cinar (U.S. Pat. Pub. No. 2011/0106011 A1).
Regarding claim 1, Cinar discloses an insulin delivery device (device/automatically controlled insulin pump 20 – see Fig. 1, [0002], and [0028-0030]), comprising: a reservoir for storing insulin ([0038], ln 2-4); a non-transitory storage medium (recordable medium, which records past glucose levels of the user) for storing computer programming instructions (including a recursive multivariate dynamic model for predicting future glucose level using data measured by a glucose/CGM sensor) and past glucose levels (recorded glucose measurements from the CGM sensor) of a user of the insulin delivery device ([0035-0036] and [0041]); a processor for executing the computer programming instructions (Fig. 1, [0035-0041], wherein controller 40 includes a processor in cooperation with the recordable medium, the processor implementing the algorithms of a prediction module 42, wherein the recursive prediction model is stored, an update module 44, and a control module 46 of the controller 40) to cause the processor to: determine values of weights for the past glucose levels of the user (see [0043-0047] and [0055-0056], wherein a VARMA/VARMAX based multivariate model is used for glucose prediction, wherein for a sample time k and for all data points in the window of estimation i=1 to i=nA, the estimated model parameter a11,i,k of matrix Ai,k is a weight applied to a past glucose level y1,k-I, these weighted past glucose levels y1,k-I being summed to produce an estimated/predicted glucose value y1,k for sample time k – see equation (1) and (2)); apply the determined weights to the past glucose levels to produce weighted past glucose levels (see [0043-0047] and [0055-0056] and above comments); customize a glucose prediction model of the user for predicting future glucose levels (ẏ1,k+j|k;θ_k within the vector ẏk+j|k;θ_k) of the user based on a sum of the weighted glucose levels (see [0006], [0008], [0036], [0041], [0048-0050], and [0057], wherein the above described VARMAX based multivariate model with known weights/model parameters is appended j-steps into the future in order to estimate/predict future glucose levels based upon past glucose measurements, insulin infusion rate, and physiological data unique to the patient, therefore producing a customized prediction model); use the customized glucose prediction model in determining a basal insulin delivery dosage by the insulin delivery device (Fig. 1, [0007], [0037], [0039], [0053], and [0058-0061], wherein controller 40 operates as part of a closed-loop system to maintain patient glucose levels within desired levels by providing an insulin infusion rate – i.e. a basal insulin delivery dosage for continuous, closed-loop treatment, rather than a bolus, which is administered reactionary to blood glucose spikes – to the insulin pump portion 50 as a function of the predicted future glucose level obtained via the VARMA/VARMAX based multivariate model); and cause the delivery of the determined basal insulin delivery dosage from the reservoir to the user (Fig. 1 and [0037], ln 1-3).
Regarding claim 2, Cinar further discloses that, wherein the past glucose level readings (y1,k-i) of the user are a first set of glucose level readings, the processor is further configured to modify the glucose prediction model in view a second set of past glucose levels (y1,k) of the user that are more recent than the first set of glucose level readings, and use the modified glucose prediction model in determining a next basal insulin delivery dosage by the insulin delivery device (see [0006], ln 15-17, [0040], [0045-0050], and [0055-0061]). Here, the glucose prediction model is recursively updated – i.e. modified -- at each new sampling time k using the weighted least squares method of [0045-0047] to incorporate the newly collected information at that time, thereby dynamically capturing the subject’s glucose variation. The updated glucose prediction model is then appended into the future in order to obtain a predicted glucose level estimate at a given time in the future, as described in [0048-0050] and [0057]. This prediction is then used to determine a next basal insulin delivery dosage in the manner described above in re claim 1 and in [0058-0061].
Regarding claim 3, Cinar further discloses that the processor is further configured to: update the customizing of the glucose prediction model (i.e. update the weights/estimated model parameters) based on glucose levels received since the customizing (see above discussion in re claim 2, wherein the weights/estimated model parameters a11,i,k are recursively updated via the weighted least squares method of [0045-0047]); use the updated customized glucose prediction model in determining a new basal insulin delivery dosage by the insulin delivery device; and cause the insulin delivery device to deliver the determined new basal insulin delivery dosage ([0006], ln 15-17, [0036-0037], [0040], [0053], and [0059-0061], wherein the updated glucose prediction model is used to determine a next basal insulin delivery dosage in the manner described above in re claim 2).
Regarding claim 4, Cinar further discloses that the customizing of the glucose prediction model comprises calculating weight coefficient values used in the glucose prediction model (see [0045-0047] and [0055-0056], and associated equations, wherein a weighted recursive least squares method is used to recursively customize and update, based on personal and continuously collected data, the coefficient values of the weights/estimated model parameters a11,i,k of the glucose prediction model).
Regarding claim 5, Cinar further discloses that the customizing entails using linear regression analysis (see [0043-0047] and [0055-0056]) to calculate coefficient values (weights/estimated model parameters a11,i,k contained in the matrix polynomial Ak(q-1) at sampling instant k) that substantially minimize an error (modelling error ek) between predicted glucose levels that are predicted from past glucose levels of the user (estimated ẏk) and corresponding actual glucose level readings of the user (yk) (see [0043-0047], wherein actual glucose level at sampling instant k is y1,k within the vector yk, and wherein estimated/predicted glucose level at sampling instant k is ẏ1,k within the vector ẏk).
Regarding claim 6, Cinar further discloses that the glucose prediction model is linear (see [0043-0047] and [0055-0056], wherein the VARMA/VARMAX based model of equations (1), (3), and (14) is linear, [0064], wherein a linear time-series model proved to be reliable for predicting future glucose levels; and see example embodiments of [0079] and [0083], in which linear modules are used).
Regarding claim 8, Cinar discloses a method performed by a processor of an electronic device (see in re claim 1), comprising: determining values of weights of a user of an insulin delivery device based on a glucose history of the user (see [0043-0047] and [0055-0056], wherein a VARMA/VARMAX based multivariate model is used for glucose prediction, wherein for a sample time k and for all data points in the window of estimation i=1 to i=nA, the estimated model parameter a11,i,k of matrix Ai,k is a weight applied to a past glucose level y1,k-I, these weighted past glucose levels y1,k-I being summed to produce an estimated/predicted glucose value y1,k for sample time k – see equation (1) and (2)); applying the determined weights to the past glucose levels to produce weighted past glucose levels and determining a predicted glucose level (ẏ1,k within the vector ẏk for sample time k, or for future times k+j, predicted future glucose levels ẏ1,k+j|k;θ_k within the vector ẏk+j|k;θ_k) for a user at a given time (sample time k or future time k+j) as a sum of the weighted past glucose levels (see [0043-0049] and [0055-0057], wherein for each past sample time k, estimated model parameters/weights from a11,i,k may be applied to preceding glucose levels y1,k-i in the same manner as described by equations 1-5, wherein model parameters/weights from matrix Ai,k are applied to past glucose levels, in order to thereby obtain a predicted glucose level ẏ1,k at each sample time k by summation of the weighted past glucose levels preceding the given sample time k, as shown in equation 1, and wherein such process may be appended a number j steps into the future, in order to thereby obtain a predicted glucose level ẏ1,k+j|k;θ_k at future time k+j); and using the predicted glucose level of the user to control delivery of insulin to the user by the insulin delivery device (see [0035-0037] and [0058-0061], wherein the rate/amount of insulin delivered to the user by the insulin delivery device may be controlled in response to the predicted glucose levels for future sample times).
Regarding claim 9, Cinar further discloses that the determining the values of the weights for the past glucose levels of the user of the insulin delivery device based on the glucose history of the user comprises: for selected ones of the glucose levels (actual glucose levels y1,k within the vector yk) in the glucose history that includes glucose levels and associated times at which the glucose levels were sensed (sample times k), calculating predicted glucose levels (ẏ1,k within the vector ẏk) from weighted glucose levels in the glucose history for times that immediately precede the times of the selected ones of the glucose levels (sample times k-i) in the glucose history (see [0043-0047] and [0055-0056], wherein for each sample time k, weights/estimated model parameters from Ai,k may be applied to preceding glucose levels y1,k-i in the same manner as described by equations 1-5, wherein model parameters/weights from matrix Ai,k are applied to past glucose levels, in order to thereby obtain a predicted glucose level ẏ1,k at each sample time k by summation of the weighted past glucose levels preceding the given sample time k, as shown in equation 1).
Regarding claim 10, Cinar further discloses that the determining of the values of the weights entails performing least squares regression analysis with the past glucose levels and predicted glucose levels that are predicted from the past glucose levels (see [0045-0047] and [0055-0056], wherein a weighted recursive least squares method is used to recursively determine and update, based on personal and continuously collected data, the coefficient values of the weights/estimated model parameters a11,i of the glucose prediction model).
Regarding claim 11, Cinar further discloses that a given one of the predicted glucose levels (ẏ1,k at given sample time k) is calculated as a sum of the weighted glucose levels in the glucose history for times that immediately precede a time of the given one of the predicted glucose levels (see [0043-0047] and [0055-0056], wherein for each sample time k, estimated weights/model parameters from Ai,k may be applied to preceding glucose levels y1,k-i in the same manner as described by equations 1-5, wherein model parameters/weights from matrix Ai,k are applied to past glucose levels, in order to thereby obtain a predicted glucose level ẏ1,k at each sample time k by summation of the weighted past glucose levels preceding the given sample time k, as shown in equation 1).
Regarding claim 14, Cinar further discloses comparing the predicted glucose level to a low glucose level threshold; and where the predicted glucose level falls below the low glucose level threshold, taking corrective action ([0049], wherein a future predicted glucose level is compared against an assigned threshold of 60 mg/dl, and if it falls below the assigned threshold, a warning alarm/alert/notification is issued to the patient).
Regarding claim 15, Cinar further discloses that the corrective action comprises outputting an alert to the user ([0049]).
Regarding claim 16, Cinar discloses an electronic device (device/automatically controlled insulin pump 20 – see Fig. 1, [0002], and [0028-0030]) comprising: a storage (recordable medium, which records past glucose levels of the user) for storing computer programming instructions (including a recursive multivariate dynamic model for predicting future glucose level using data measured by a glucose/CGM sensor) for controlling operation of an insulin delivery device ([0035]); a processor for executing the computer programming instructions (Fig. 1, [0035], wherein controller 40 includes a processor in cooperation with the recordable medium, the processor implementing the algorithms of a prediction module 42, wherein the recursive prediction model is stored, an update module 44, and a control module 46 of the controller 40), the computer programming instruction for causing the processor to: determine values of weights for past glucose levels of a user of the insulin delivery device (see [0043-0047] and [0055-0056], wherein a VARMA/VARMAX based multivariate model is used for glucose prediction, wherein estimated model parameters/weights a11,i,k of matrix Ai,k are determined and applied to past glucose levels y1,k-i, as described above in re claims 1 and 8); apply the determined weights to the past glucose levels to produce weighted past glucose levels (see [0043-0047] and [0055-0056], wherein a VARMA/VARMAX based multivariate model is used for glucose prediction, wherein estimated model parameters/weights a11,i,k of matrix Ai,k are determined and applied to past glucose levels y1,k-i, as described above in re claims 1 and 8); use a glucose prediction model to predict future glucose levels of a user of the insulin delivery device (see [0035-0036], wherein prediction module 42 uses the model described in [0043-0061] to predict the future blood glucose level of the user based upon recorded past glucose levels, as described above in re claims 1 and 8); customize the glucose prediction model of the user based on a sum of the weighted past glucose levels (see Fig. 1, [0006], [0008], and [0036], wherein the model/algorithm for predicting future glucose level is based upon recorded past glucose levels and physiological data of the user, and therefore is customized to the user, and see implementation details of [0043-0061], discussed above in re claims 1 and 8, wherein such is apparent, and wherein it is explained that the glucose prediction model of the user is based on a sum of the weighted past glucose levels); use the customized glucose prediction model to predict future glucose levels of the user (see Fig. 1, [0035-0036], [0048], and [0057-0061] – see above discussion in re claims 1 and 8); and use at least one of the predicted future glucose levels in determining a basal delivery dosage of insulin to be delivered to the user from the insulin delivery device (Fig. 1, [0007], [0037], [0039], [0053], and [0057-0061], wherein controller 40 operates as part of a closed-loop system to maintain patient glucose levels within desired levels by providing an insulin infusion rate – i.e. a basal insulin delivery dosage for continuous, closed-loop treatment, rather than a bolus, which is administered reactionary to blood glucose spikes – to the insulin pump portion 50 as a function of predicted future glucose level).
Regarding claim 17, Cinar further discloses that the electronic device is a closed-loop, integrated system comprising an insulin delivery device (insulin pump portion 50) and a management device for the insulin delivery device (controller 40) (see Fig. 1 and [0035-0037]).
Regarding claim 18, Cinar further discloses that the computer programming instructions include instructions for causing the processor to update the customizing of the glucose prediction model based on recent glucose levels of the user (see in re claim 2).
Regarding claim 19, Cinar further discloses that the computer programming instructions include instructions for causing the processor to adjust the predicted glucose levels of the user to account for noise (see [0047] and [0087], wherein the forgetting factor λ is variable, and may be modified or maintained to be at a large value in order to reduce the model’s sensitivity to noise by giving equal weight to recent and older data, such as by not reducing at the first instant of change; and see example embodiment of [0093], ln 1-4, wherein measured and recorded past glucose level data may be smoothed using a low-pass filter to reduce noise in the data prior to being used within the predictive model).
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.
Claim(s) 1-7 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Palerm (U.S. Pat. Pub. No. 2015/0165119 A1) in view of Cinar.
Regarding claim 1, Palerm discloses an insulin delivery device (fluid infusion device 200, comprising the control system 500 of Fig. 5 which may be implemented within the control electronics 224 – see [0042] and [0053]), comprising: a reservoir (reservoir 205) for storing insulin (see Fig. 2-3 and [0033]); a non-transitory storage medium for (see [0044] and [0047-0052], wherein the motor control module 512 of the pump control system 520 may comprise a data storage element or memory for storing computer programming instructions and past glucose levels of a user of the insulin delivery device); a processor configured for executing computer programming instructions (see [0047]) to cause the processor to: customize a glucose prediction model of the user for predicting future glucose levels of the user based on a weighted sum determined using the past glucose level readings of the user (see [0048] and [0051-0052]); use the customized glucose prediction model in determining a basal insulin delivery dosage by the insulin delivery device ([0026], [0031], and [0049]); and cause the delivery of the determined basal insulin delivery dosage from the reservoir to the user ([0026] and [0044-0045]).
Palerm fails to teach a specific glucose prediction model (suggesting that a truncated Taylor series expansion or a recursive prediction algorithm may be used), nor does Palerm teach a glucose prediction model configured to cause the processor to: determine values of weights for the past glucose levels of the user; apply the determined weights to the past glucose levels to produce weighted past glucose levels; and customize a glucose prediction model of the user for predicting future glucose levels of the user based on a sum of the weighted past glucose levels the past glucose level readings of the user.
Cinar exhibits an insulin delivery device (device/automatically controlled insulin pump 20 – see Fig. 1, [0002], and [0028-0030]) similar to that of Palerm, comprising: a reservoir for storing insulin ([0038], ln 2-4); a non-transitory storage medium (recordable medium, which records past glucose levels of the user) for storing computer programming instructions (including a recursive multivariate dynamic model for predicting future glucose level using data measured by a glucose/CGM sensor) and past glucose levels (recorded glucose measurements from the CGM sensor) of a user of the insulin delivery device ([0035-0036] and [0041]); and a processor for executing the computer programming instructions (Fig. 1, [0035-0041], wherein controller 40 includes a processor in cooperation with the recordable medium, the processor implementing the algorithms of a prediction module 42, wherein the recursive prediction model is stored, an update module 44, and a control module 46 of the controller 40). Cinar teaches that such instructions may include a multivariate recursive dynamic glucose prediction model (the VARMA based model of [0043-0048] and equations (1) – (11)) configured to cause the processor to: determine values of weights for the past glucose levels of the user (see [0043-0047], wherein a VARMA based multivariate model is used for glucose prediction, wherein for a sample time k and for all data points in the window of estimation i=1 to i=nA, the estimated model parameter a11,i,k of matrix Ai,k is a weight applied to a past glucose level y1,k-I, these weighted past glucose levels y1,k-I being summed to produce an estimated/predicted glucose value y1,k for sample time k – see equation (1) and (2)); apply the determined weights to the past glucose levels to produce weighted past glucose levels (see [0043-0047] and above comments); and customize a glucose prediction model of the user for predicting future glucose levels (ẏ1,k+n|k;θ_k within the vector ẏk+n|k;θ_k) of the user based on a sum of the weighted glucose levels (see [0006], [0008], [0036], [0041], and [0048-0050], wherein the above described VARMA based multivariate model with known weights/model parameters is appended n-steps into the future in order to estimate/predict future glucose levels based upon past glucose measurements and physiological data unique to the patient, therefore producing a customized prediction model).
Based on the teachings and example of Cinar, which teaches a multivariate recursive dynamic model (VARMA based model of [0043-0048] and equations (1) – (11)) which is advantageously customized to the user, considers both glucose level and physiology parameters of the user for improved accuracy, and may be operated automatically (no manual user input required – see [0028] and [0040-0041]), and because Palerm explicitly suggests that a truncated Taylor series expansion or a recursive prediction algorithm type model may be used (the model of Cinar being a recursive prediction algorithm that is built upon a truncated Taylor series expansion – see equation (1) and [0043-0047]), it would have been obvious to one of ordinary skill in the art to embody the customized glucose prediction model of Palerm as the multivariate recursive dynamic model of Cinar, such that the processor of Palerm may thereby be configured to determine values of weights for the past glucose levels of the user; apply the determined weights to the past glucose levels to produce weighted past glucose levels; and customize a glucose prediction model of the user for predicting future glucose levels of the user based on a sum of the weighted past glucose levels the past glucose level readings of the user, among other steps and functions of the multivariate recursive dynamic model of Cinar.
Regarding claim 2, Cinar further teaches that, wherein the past glucose level readings (y1,k-i) of the user are a first set of glucose level readings, the processor is further configured to modify the glucose prediction model in view a second set of past glucose levels (y1,k) of the user that are more recent than the first set of glucose level readings, and use the modified glucose prediction model in determining a next basal insulin delivery dosage by the insulin delivery device (see [0006], ln 15-17, [0040], and [0045-0050]). Here, the glucose prediction model is recursively updated – i.e. modified -- at each new sampling time k using the weighted least squares method of [0045-0047] to incorporate the newly collected information at that time, thereby dynamically capturing the subject’s glucose variation. The updated glucose prediction model is then appended into the future in order to obtain a predicted glucose level estimate at a given time in the future, as described in [0048-0050]. Because the multivariate recursive dynamic glucose prediction model of Cinar is incorporated into the insulin delivery device of Palerm in the above modification, it follows as part of that modification that Palerm may also be modified such to exhibit these claimed features. This prediction may then used to determine a next basal insulin delivery dosage in the manner described in Palerm above in re claim 1 (see in re claim 1).
Regarding claim 3, Cinar further teaches that the processor may be further configured to: update the customizing of the glucose prediction model (i.e. update the weights/estimated model parameters) based on glucose levels received since the customizing (see above discussion in re claim 2, wherein the weights/estimated model parameters a11,i,k are recursively updated via the weighted least squares method of [0045-0047]); use the updated customized glucose prediction model in determining a new basal insulin delivery dosage by the insulin delivery device; and cause the insulin delivery device to deliver the determined new basal insulin delivery dosage ([0006], ln 15-17, [0036-0037], [0040], [0053], and [0059-0061], wherein the updated glucose prediction model is used to determine a next basal insulin delivery dosage in the manner described above in re claim 2). Because the multivariate recursive dynamic glucose prediction model of Cinar is incorporated into the insulin delivery device of Palerm in the above modification, it follows as part of that modification that Palerm may also be modified such to exhibit these claimed features.
Regarding claim 4, Cinar further teaches that the customizing of the glucose prediction model comprises calculating weight coefficient values used in the glucose prediction model (see [0045-0047], and associated equations, wherein a weighted recursive least squares method is used to recursively customize and update, based on personal and continuously collected data, the coefficient values of the weights/estimated model parameters a11,i,k of the glucose prediction model). Because the multivariate recursive dynamic glucose prediction model of Cinar is incorporated into the insulin delivery device of Palerm in the above modification, it follows as part of that modification that Palerm may also be modified such to exhibit these claimed features.
Regarding claim 5, Cinar further teaches that the customizing entails using linear regression analysis (see [0043-0047]) to calculate coefficient values (weights/estimated model parameters a11,i,k contained in the matrix polynomial Ak(q-1) at sampling instant k) that substantially minimize an error (modelling error ek) between predicted glucose levels that are predicted from past glucose levels of the user (estimated ẏk) and corresponding actual glucose level readings of the user (yk) (see [0043-0047], wherein actual glucose level at sampling instant k is y1,k within the vector yk, and wherein estimated/predicted glucose level at sampling instant k is ẏ1,k within the vector ẏk). Because the multivariate recursive dynamic glucose prediction model of Cinar is incorporated into the insulin delivery device of Palerm in the above modification, it follows as part of that modification that Palerm may also be modified such to exhibit these claimed features.
Regarding claim 6, Cinar further teaches that the glucose prediction model is linear (see [0043-0047], wherein the VARMA based model of equations (1), (3), and (14) is linear, [0064], wherein a linear time-series model proved to be reliable for predicting future glucose levels; and see example embodiments of [0079] and [0083], in which linear modules are used). Because the multivariate recursive dynamic glucose prediction model of Cinar is incorporated into the insulin delivery device of Palerm in the above modification, it follows as part of that modification that Palerm may also be modified such to exhibit these claimed features.
Regarding claim 7, Cinar further teaches that the glucose prediction model may ignore how much insulin has been delivered to the user (see [0039-0041], [0050], and [0085]). Here, the VARMA based glucose prediction model of equations (1) – (11) and [0043-0048] is based solely upon past glucose level and physiological signal data, ignoring how much insulin has been delivered to the user. This embodiment of Cinar’s glucose prediction model which is not based on insulin rate fits cleanly into the framework of Palerm, which discloses the need for a glucose prediction model based upon measured glucose level, with no mention of any need for the model to consider how much insulin has already been administered.
Regarding claim 16, Palerm discloses an electronic device (fluid infusion device 200, comprising the control system 500 of Fig. 5 which may be implemented within the control electronics 224 – see [0042] and [0053]), comprising: a storage for storing computer programming instructions for controlling operation of an insulin delivery device (see [0044] and [0047-0052], wherein the motor control module 512 of the pump control system 520 may comprise a data storage element or memory for storing computer programming instructions and past glucose levels of a user of the insulin delivery device); a processor for executing the computer programming instructions (see [0047]), the computer programming instruction for causing the processor to: customize a glucose prediction model of the user for predicting future glucose levels of the user based on a weighted sum determined using the past glucose level readings of the user (see [0048] and [0051-0052]); use the customized glucose prediction model in determining a basal insulin delivery dosage by the insulin delivery device ([0026], [0031], and [0049]); and cause the delivery of the determined basal insulin delivery dosage from the reservoir to the user ([0026] and [0044-0045]).
Palerm fails to teach a specific glucose prediction model (suggesting that a truncated Taylor series expansion or a recursive prediction algorithm may be used), nor does Palerm teach a glucose prediction model configured to cause the processor to: determine values of weights for past glucose levels of a user of the insulin delivery device; apply the determined weights to the past glucose levels to produce weighted past glucose levels; use a glucose prediction model to predict future glucose levels of the user of the insulin delivery device; customize the glucose prediction model of the user based on a sum of the weighted past glucose levels; use the customized glucose prediction model to predicts future glucose levels of the user.
Cinar exhibits an electronic device (device/automatically controlled insulin pump 20 – see Fig. 1, [0002], and [0028-0030]) similar to that of Palerm, comprising: a storage (recordable medium, which records past glucose levels of the user) for storing computer programming instructions (including a recursive multivariate dynamic model for predicting future glucose level using data measured by a glucose/CGM sensor) for controlling operation of an insulin delivery device ([0035]); and a processor for executing the computer programming instructions (Fig. 1, [0035], wherein controller 40 includes a processor in cooperation with the recordable medium, the processor implementing the algorithms of a prediction module 42, wherein the recursive prediction model is stored, an update module 44, and a control module 46 of the controller 40). Cinar teaches that such instructions may include a multivariate recursive dynamic glucose prediction model (the VARMA based model of [0043-0048] and equations (1) – (11)) configured to cause the processor to: determine values of weights for past glucose levels of a user of the insulin delivery device (see [0043-0047], wherein a VARMA based multivariate model is used for glucose prediction, wherein estimated model parameters/weights a11,i,k of matrix Ai,k are determined and applied to past glucose levels y1,k-i, as described above in re claim 1); apply the determined weights to the past glucose levels to produce weighted past glucose levels (see [0043-0047], wherein a VARMA based multivariate model is used for glucose prediction, wherein estimated model parameters/weights a11,i,k of matrix Ai,k are determined and applied to past glucose levels y1,k-i, as described above in re claim 1); use a glucose prediction model to predict future glucose levels of a user of the insulin delivery device (see [0035-0036], wherein prediction module 42 uses the model described in [0043-0048] to predict the future blood glucose level of the user based upon recorded past glucose levels, as described above in re claim 1); customize the glucose prediction model of the user based on a sum of the weighted past glucose levels (see Fig. 1, [0006], [0008], and [0036], wherein the model/algorithm for predicting future glucose level is based upon recorded past glucose levels and physiological data of the user, and therefore is customized to the user, and see implementation details of [0043-0048], discussed above in re claim 1, wherein such is apparent, and wherein it is explained that the glucose prediction model of the user is based on a sum of the weighted past glucose levels); and use the customized glucose prediction model to predict future glucose levels of the user (see Fig. 1, [0035-0036], and [0048], and see above discussion in re claim 1).
Based on the teachings and example of Cinar, which teaches a multivariate recursive dynamic model (VARMA based model of [0043-0048] and equations (1) – (11)) which is advantageously customized to the user, considers both glucose level and physiology parameters of the user for improved accuracy, and may be operated automatically (no manual user input required – see [0028] and [0040-0041]), and because Palerm explicitly suggests that a truncated Taylor series expansion or a recursive prediction algorithm type model may be used (the model of Cinar being a recursive prediction algorithm that is built upon a truncated Taylor series expansion – see equation (1) and [0043-0047]), it would have been obvious to one of ordinary skill in the art to embody the customized glucose prediction model of Palerm as the multivariate recursive dynamic model of Cinar, such that the processor of Palerm may thereby be configured to determine values of weights for past glucose levels of a user of the insulin delivery device; apply the determined weights to the past glucose levels to produce weighted past glucose levels; use a glucose prediction model to predict future glucose levels of the user of the insulin delivery device; customize the glucose prediction model of the user based on a sum of the weighted past glucose levels; use the customized glucose prediction model to predicts future glucose levels of the user, among other steps and functions of the multivariate recursive dynamic model of Cinar.
Regarding claim 17, Palerm further discloses that the electronic device is an insulin delivery device (device/automatically controlled insulin pump 20 – see Fig. 1, [0002], and [0028-0030]).
Regarding claim 18, Cinar further teaches that, wherein the past glucose level readings (y1,k-i) of the user are a first set of glucose level readings, the processor is further configured to modify the glucose prediction model in view a second set of past glucose levels (y1,k) of the user that are more recent than the first set of glucose level readings, and use the modified glucose prediction model in determining a next basal insulin delivery dosage by the insulin delivery device (see [0006], ln 15-17, [0040], and [0045-0050]). Here, the glucose prediction model is recursively updated – i.e. modified -- at each new sampling time k using the weighted least squares method of [0045-0047] to incorporate the newly collected information at that time, thereby dynamically capturing the subject’s glucose variation. The updated glucose prediction model is then appended into the future in order to obtain a predicted glucose level estimate at a given time in the future, as described in [0048-0050]. Because the multivariate recursive dynamic glucose prediction model of Cinar is incorporated into the insulin delivery device of Palerm in the above modification, it follows as part of that modification that Palerm may also be modified such to exhibit these claimed features. This prediction may then used to determine a next basal insulin delivery dosage in the manner described in Palerm above in re claim 1 (see in re claim 1).
Regarding claim 19, Palerm discloses that the computer programming instructions include instructions for causing the processor to adjust the predicted glucose levels of the user to account for noise ([0052]). Additionally, Cinar also teaches that the computer programming instructions include instructions for causing the processor to adjust the predicted glucose levels of the user to account for noise in a different manner (see [0047] and [0087], wherein the forgetting factor λ is variable, and may be modified or maintained to be at a large value in order to reduce the model’s sensitivity to noise by giving equal weight to recent and older data, such as by not reducing at the first instant of change; and see example embodiment of [0093], ln 1-4, wherein measured and recorded past glucose level data may be smoothed using a low-pass filter to reduce noise in the data prior to being used within the predictive model). Because the multivariate recursive dynamic glucose prediction model of Cinar is incorporated into the insulin delivery device of Palerm in the above modification, it follows as part of that modification that Palerm may also be modified such to exhibit these claimed features.
Regarding claim 20, Cinar further teaches that the glucose prediction model may ignore how much insulin has been delivered to the user (see [0039-0041], [0050], and [0085]). Here, the VARMA based glucose prediction model of equations (1) – (11) and [0043-0048] is based solely upon past glucose level and physiological signal data, ignoring how much insulin has been delivered to the user. This embodiment of Cinar’s glucose prediction model which is not based on insulin rate fits cleanly into the framework of Palerm, which discloses the need for a glucose prediction model based upon measured glucose level, with no mention of any need for the model to consider how much insulin has already been administered.
Claim(s) 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Cinar in view of Reifman (U.S. Pat. Pub. No. 2011/0160555 A1) and Bidet (U.S. Pat. Pub. No. 2021/0068669 A1).
Regarding claim 12, Cinar discloses the method of claim 8. While Cinar teaches comparing the predicted glucose level to a low glucose level threshold, and where the predicted glucose level falls below the low glucose level threshold, taking corrective action (see in re claim 14), Cinar fails to teach comparing the predicted glucose level to a high glucose level threshold; and where the predicted glucose level exceeds the high glucose level threshold, taking corrective action. Such a concept, however, follows naturally from the example of Cinar and from the well understood fact within the art that excessively high glucose levels (hyperglycemia) may be equally damaging to a patient’s health (see Bidet, [0012]). It is thus well understood within the art that there is a need for a warning system that can predict hyperglycemic events in the same manner that Cinar does for hypoglycemic events (see Bidet, [0021]). Further, such a method step is known within the art. Reifman exhibits a system and method for predicting future glucose levels of a patient based upon measured and recorded glucose level measurements of the patient, similar to that of Cinar (see Fig 1-2B, [0005-0008], and [0037-0045]). Reifman teaches the method step of comparing a predicted glucose level to a high glucose level threshold; and where the predicted glucose level exceeds the high glucose level threshold, taking corrective action by outputting an alert to the user (see Fig. 1, method step 140, [0008], and [0045]). Based on the teachings and example of Reifman, and because it is well understood within the art that there is a need for a warning system that can predict hyperglycemic events in the same manner that Cinar does for hypoglycemic events, as taught by Bidet, it would have been obvious to one of ordinary skill in the art prior to the filing date of the claimed invention to modify the method of Cinar to include comparing the predicted glucose level to a high glucose level threshold; and where the predicted glucose level exceeds the high glucose level threshold, taking corrective action in the form of outputting an alert to the user, as taught by Reifman, in addition to the similar process already performed by Cinar for low glucose levels, thereby aiding the user in avoiding damaging high glucose level (hyperglycemic) events.
Regarding claim 13, Cinar as modified by Reifman and Bidet exhibits that the corrective action comprises outputting an alert to the user (see in re claim 12).
Conclusion
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/ERIC A LANGE/Examiner, Art Unit 3783
/CHELSEA E STINSON/Supervisory Patent Examiner, Art Unit 3783
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