Detailed Notice
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/11/2025 has been entered.
Status of Claims
Claims 1-20 are pending.
Claims 1, 8, and 15 are amended.
Claims 1-20 are rejected.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 7-8, 13, and 15 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5, 12, and 20 of U.S. Patent No. 11,596,359 B2 in view of Roy et al. (US 20110237917 A1), hereinafter Roy in further view of Agrawal et al. (US 20210038163 A1), hereinafter Agrawal.
Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-3, 7-8, 13, and 15 are similar to claims 1-3, 5, 12, and 20 of U.S. Patent No. 11,596,359 B2 as shown below:
Application No. 18/153,948
U.S. Patent No. 11,596,359 B2
1. (Currently Amended) A system comprising:
one or more processors;
and one or more processor-readable media storing instructions which, when executed by the one or more processors, cause performance of:
identifying an error metric associated with an input variable to a translation model that applies a weighting to the input variable to estimate a glucose level of a user;
determining a reference output of the translation model based on a reference input value for the input variable to the translation model;
generating a modulated value for the input variable based on the reference input value and the error metric;
determining a simulated output of the translation model based on the modulated value for the input variable to the translation model;
updating the translation model when a difference between the simulated output and the reference output is greater than a threshold by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the translation model based on the reduced weighting;
determining an output glucose value using the updated translation model and one or more values of the input variable derived from one or more electrical signals outputted by a glucose sensor of the user;
and causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value.
20. A system comprising:
1. A processor-implemented method comprising:
identifying an error metric associated with an input variable associated with a translation model, the translation model providing an output glucose value that is influenced by a value for the input variable and a weighting applied to the input variable;
determining a reference output glucose value of the translation model by providing reference input values for the input variable to the translation model;
generating modulated values for the input variable based on the reference input values using the error metric;
determining a simulated output glucose value of the translation model by providing the modulated values for the input variable to the translation model;
updating the translation model when a difference between the simulated output glucose value and the reference output glucose value is greater than a threshold, resulting in an updated translation model which mitigates error associated with the input variable;
providing an output glucose value of the updated translation model based on one or more subsequent values for the input variable derived from one or more electrical signals output by an instance of a sensing element capable of providing electrical signals influenced by glucose levels in a body of a patient; and
providing a notification pertaining to a glucose level in the body of the patient, wherein the notification is generated based at least in part on the output glucose value of the updated translation model.
2. (Original) The system of claim 1, wherein: the translation model comprises an estimation model for providing an estimated glucose value; determining the reference output comprises determining a reference glucose value using the reference input value; determining the simulated output comprises determining a simulated glucose value using the modulated value; and updating the translation model comprises updating the estimation model to reduce the difference between the simulated glucose value and the reference glucose value by assigning the reduced weighting to the input variable in the estimation model.
2. The processor-implemented method of claim 1, wherein the translation model comprises an estimation model for providing an estimated glucose value, wherein:
determining the reference output glucose value comprises determining a reference set of measurement glucose values using the reference input values;
determining the simulated output glucose value comprises determining a simulated set of measurement glucose values using the modulated values; and
updating the translation model comprises updating the estimation model to reduce the difference between the simulated set of measurement glucose values and the reference set of measurement glucose values by applying the reduced weighting to the input variable in the estimation model.
3. (Previously Presented) The system of claim 2, wherein: the estimation model comprises a sensor glucose estimation model for providing the estimated glucose value; the reference glucose value comprises an estimated glucose value determined by the sensor glucose estimation model as a function of the reference input value and the weighting applied to the input variable; and the simulated glucose value comprises an estimated glucose value determined by the sensor glucose estimation model as a function of the modulated value and the weighting applied to the input variable.
3. The processor-implemented method of claim 2, wherein:
the sensing element comprises an interstitial glucose sensing arrangement;
the estimation model comprises a sensor glucose estimation model for providing an estimated glucose value;
the reference set of measurement glucose values comprises a reference set of estimated glucose measurement values determined by the sensor glucose estimation model as a function of the reference input values and the weighting applied to the input variable; and
the simulated set of measurement glucose values comprises a simulated set of estimated glucose measurement values determined by the sensor glucose estimation model as a function of the modulated values and the weighting applied to the input variable.
4. (Original) The system of claim 2, wherein the translation model comprises an insight model for generating a notification.
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5. (Original) The system of claim 4, wherein updating the translation model comprises: updating the insight model for generating the notification as a function of the estimated glucose value provided by the updated estimation model having the reduced weighting applied to the input variable.
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6. (Original) The system of claim 1, wherein updating the translation model comprises: reducing the weighting applied to the input variable; determining an updated reference output based at least in part on the reduced weighting; and determining an updated simulated output based at least in part on the reduced weighting applied to the modulated value such that the difference between the simulated output and the reference output is less than the threshold.
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7. (Original) The system of claim 1, wherein the input variable comprises an output electrical current, an electrode voltage, or an electrochemical impedance spectroscopy (EIS) value determined based on electrical signals provided by an interstitial glucose sensing arrangement.
12. The processor-implemented method of claim 10, wherein:
the sensing element comprises an interstitial glucose sensing arrangement;
the estimation model comprises a sensor glucose estimation model for providing an estimated glucose value;
the reference set of estimated measurement values comprises a reference set of estimated glucose measurement values determined by the sensor glucose estimation model as a function of the reference input values and the weighting applied to the input variable;
the simulated set of estimated measurement values comprises a simulated set of estimated glucose measurement values determined by the sensor glucose estimation model as a function of the modulated values and the weighting applied to the input variable; and
the input variable is determined based on the electrical signals provided by the interstitial glucose sensing arrangement and comprises an output electrical current, an electrode voltage, or an electrochemical impedance spectroscopy (EIS) value.
8. (Currently Amended) A processor-implemented method comprising: identifying an error metric associated with an input variable to a translation model that applies a weighting to the input variable to estimate a glucose level of a user; determining a reference output of the translation model based on a reference input value for the input variable to the translation model; generating a modulated value for the input variable based on the reference input value and the error metric; determining a simulated output of the translation model based on the modulated value for the input variable to the translation model; updating the translation model when a difference between the simulated output and the reference output is greater than a threshold by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the translation model based on the reduced weighting; determining an output glucose value using the updated translation model and one or more values of the input variable derived from one or more electrical signals outputted by a glucose sensor of the user; and causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value.
13. A processor-implemented method comprising:
identifying an error metric associated with an input variable influenced by electrical signals provided by a glucose sensing element, the electrical signals being influenced by a glucose level in a body of a patient;
determining modulated values for the input variable using the error metric;
determining a reference output glucose value of a translation model using reference input values for the input variable, wherein the reference output glucose value of the translation model is influenced by the reference input values and a weighting applied to the input variable;
determining a simulated output glucose value of the translation model using the modulated values for the input variable, wherein the simulated output glucose value of the translation model is influenced by the modulated values for the input variable and the weighting;
when a difference between the simulated output glucose value and the reference output glucose value is greater than a threshold, updating, by one or more processors, the translation model , resulting in an updated translation model which mitigates error associated with the input variable; and
providing the updated translation model to a device monitoring the glucose level in the body of the patient using the glucose sensing element, wherein the device generates an output glucose value of the updated translation model based on one or more subsequent values for the input variable derived from one or more subsequent electrical signals output by the glucose sensing element in response to the glucose level in the body of the patient, wherein the device provides a notification pertaining to a glucose level in the body of the patient, the notification generated based at least in part on the output glucose value of the updated translation model.
9. (Original) The processor-implemented method of claim 8, wherein: the translation model comprises an estimation model for providing an estimated glucose value; determining the reference output comprises determining a reference glucose value using the reference input value; determining the simulated output comprises determining a simulated glucose value using the modulated value; and updating the translation model comprises updating the estimation model to reduce the difference between the simulated glucose value and the reference glucose value by assigning the reduced weighting to the input variable in the estimation model.
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10. (Previously Presented) The processor-implemented method of claim 9, wherein: the estimation model comprises a sensor glucose estimation model for providing the estimated glucose value; the reference glucose value comprises an estimated glucose value determined by the sensor glucose estimation model as a function of the reference input value and the weighting applied to the input variable; and the simulated glucose value comprises an estimated glucose value determined by the sensor glucose estimation model as a function of the modulated value and the weighting applied to the input variable.
X
11. (Original) The processor-implemented method of claim 9, wherein the translation model comprises an insight model for generating a notification.
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12. (Original) The processor-implemented method of claim 11, wherein updating the translation model comprises: updating the insight model for generating the notification as a function of the estimated glucose value provided by the updated estimation model having the reduced weighting applied to the input variable.
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13. (Original) The processor-implemented method of claim 8, wherein updating the translation model comprises: reducing the weighting applied to the input variable; determining an updated reference output based at least in part on the reduced weighting; and determining an updated simulated output based at least in part on the reduced weighting applied to the modulated value such that the difference between the simulated output and the reference output is less than the threshold.
5. The processor-implemented method of claim 1, wherein the translation model comprises an insight model for characterizing an aspect of the glucose level in the body of the patient, wherein:
the input variable comprises an estimated value output by an estimation model;
identifying the error metric comprises determining the error metric associated with the estimated value output by the estimation model;
determining the reference output glucose value comprises providing reference values from the estimation model for the input variable to the insight model to obtain reference characterizations for the aspect of the glucose level in the body of the patient;
generating the modulated values comprises determining simulated values for the input variable based on the reference values from the estimation model using the error metric;
determining the simulated output glucose value comprises providing the simulated values for the input variable to the insight model to obtain simulated characterizations for the aspect of the glucose level in the body of the patient;
updating the translation model comprises updating the insight model with the reduced weighting applied to the estimated value output by the estimation model when a difference between the simulated characterizations and the reference characterizations is greater than the threshold; and
the notification is generated based at least in part on an output of the updated insight model when one or more subsequent values for an estimated value derived by the estimation model from one or more electrical signals output by the instance of the sensing element are input to the updated insight model with the reduced weighting.
16. The processor-implemented method of claim 15, wherein updating the translation model comprises:
iteratively reducing the weighting factor,
iteratively determining an updated reference output based at least in part on the reduced weighting factor applied to the one or more reference values for the intermediate variable, and
iteratively determining an updated simulated output based at least in part on the reduced weighting factor applied to the one or more modulated values for the intermediate variable until the difference between the simulated output glucose value and the reference output glucose value is less than the threshold.
14. (Original) The processor-implemented method of claim 8, wherein the input variable comprises an output electrical current, an electrode voltage, or an electrochemical impedance spectroscopy (EIS) value determined based on electrical signals provided by an interstitial glucose sensing arrangement.
X
15. (Currently Amended) One or more non-transitory processor-readable media storing instructions which, when executed by one or more processors, cause performance of: identifying an error metric associated with an input variable to a translation model that applies a weighting to the input variable to estimate a glucose level of a user; determining a reference output of the translation model based on a reference value for the input variable to the translation model; generating a modulated value for the input variable based on the reference value and the error metric; determining a simulated output of the translation model based on the modulated value for the input variable to the translation model; and updating the translation model when a difference between the simulated output and the reference output is greater than a threshold by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the translation model based on the reduced weighting; determining an output glucose value using the updated translation model and one or more values of the input variable derived from one or more electrical signals outputted by a glucose sensor of the user; and causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value.
20. A system comprising:
1. A processor-implemented method comprising:
identifying an error metric associated with an input variable associated with a translation model, the translation model providing an output glucose value that is influenced by a value for the input variable and a weighting applied to the input variable;
determining a reference output glucose value of the translation model by providing reference input values for the input variable to the translation model;
generating modulated values for the input variable based on the reference input values using the error metric;
determining a simulated output glucose value of the translation model by providing the modulated values for the input variable to the translation model;
updating the translation model when a difference between the simulated output glucose value and the reference output glucose value is greater than a threshold, resulting in an updated translation model which mitigates error associated with the input variable;
providing an output glucose value of the updated translation model based on one or more subsequent values for the input variable derived from one or more electrical signals output by an instance of a sensing element capable of providing electrical signals influenced by glucose levels in a body of a patient; and
providing a notification pertaining to a glucose level in the body of the patient, wherein the notification is generated based at least in part on the output glucose value of the updated translation model.
16. (Previously Presented) The one or more non-transitory processor-readable media of claim 15, wherein: the translation model comprises an estimation model for providing an estimated glucose value; determining the reference output comprises determining a reference glucose value using the reference value for the input variable; determining the simulated output comprises determining a simulated glucose value using the modulated value; and updating the translation model comprises updating the estimation model to reduce the difference between the simulated glucose value and the reference glucose value by assigning the reduced weighting to the input variable in the estimation model.
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17. (Previously Presented) The one or more non-transitory processor-readable media of claim 16, wherein: the estimation model comprises a sensor glucose estimation model for providing the estimated glucose value; the reference glucose value comprises an estimated glucose value determined by the sensor glucose estimation model as a function of the reference value of the input variable and the weighting applied to the input variable; and the simulated glucose value comprises an estimated glucose value determined by the sensor glucose estimation model as a function of the modulated value and the weighting applied to the input variable.
X
18. (Original) The one or more non-transitory processor-readable media of claim 16, wherein the translation model comprises an insight model for generating a notification.
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19. (Original) The one or more non-transitory processor-readable media of claim 18, wherein updating the translation model comprises: updating the insight model for generating the notification as a function of the estimated glucose value provided by the updated estimation model having the reduced weighting applied to the input variable.
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20. (Original) The one or more non-transitory processor-readable media of claim 15, wherein updating the translation model comprises: reducing the weighting applied to the input variable; determining an updated reference output based at least in part on the reduced weighting; and determining an updated simulated output based at least in part on the reduced weighting applied to the modulated value such that the difference between the simulated output and the reference output is less than the threshold.
X
Regarding claim 1, 8, and 15, U.S. Patent No. 11,596,359 B2 does not teach “generating a modulated value for the input variable based on the reference value using and the error metric; and updating the translation model when a difference between the simulated output and the reference output is greater than a threshold; resulting in an updated translation model which mitigates error associated with the input variable determining an output glucose value using the updated translation model and one or more values of the input variable derived from one or more electrical signals outputted by a glucose sensor of the user; and causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value”.
However, Roy et al. (US 20110237917 A1), hereinafter Roy, teaches generating a modulated value for the input variable based on the reference input value and the error metric ([0031], [0093], [0094], [0106], [0147], [0156], [0164], [0173], and [0174]); updating the translation model when a difference between the simulated output and the reference output is greater than a threshold ([0106], [0156], and [0157]-[0162]); determining an output glucose value using the updated translation model and one or more values of the input variable derived from one or more electrical signals outputted by a glucose sensor of the user (FIG. 11, [0054], [0069], [0116]: “As illustrated, filter and/or calibration unit 456 may include one or more processors 1102 and at least one memory 1104. In certain example embodiments, memory 1104 may store or otherwise include instructions 1106 and/or sample-measurement data 1108. Sample- measurement data 1108 may include, by way of example but not limitation, blood glucose reference samples measured via a blood sample, blood glucose sensor measurements, blood glucose sample-sensor measurement pairs, combinations thereof”, [0117], and [0156]: “and an update stage, in which a current predicted stage of the system is updated/corrected based on a weighted error generated between a model prediction and a true measurement”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify U.S. Patent No. 11,596,359 B2 to incorporate the teachings of Roy and account for methods, apparatuses, etc. for calibrating a glucose monitoring sensor and/or an insulin delivery system including, by way of example but not limitation, calibration that is at least partially automatic and/or a calibration of sensor current measurements during operation (Roy, [0002]).
U.S. Patent No. 11,596,359 B2 and Roy do not teach causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value.
However, Agrawal et al. (US 20210038163 A1), hereinafter Agrawal, teaches causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value (Agrawal, Abstract, [0009]: “infusion and management system can include an insulin infusion device configured to deliver insulin to a user; a blood glucose meter; a source of user activity data; and a processor-based computing device that supports data communication with the insulin infusion device... The processor-readable medium comprises executable instructions configurable to cause the processor device to perform a method for estimating glucose values of a user... a first set of inputs can be received and processed via an estimation model for a user to generate a set of estimated glucose values that track actual glucose values”, [0029], [0058]: “deliver insulin to the body 501 of the patient based on the difference between the sensed glucose value and the target glucose value”, [0067], and [0072]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify U.S. Patent No. 11,596,359 B2 and Roy to incorporate the teachings of Agrawal and account for users knowing their blood glucose levels in any given point, and for patients to determine/witness when it may be necessary to administer insulin outside their scheduled administrations (Agrawal, Abstract and [0004]).
U.S. Patent No. 11,596,359 B2, Roy, and Agrawal do not teach by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the translation model based on the reduced weighting.
However, Weiner teaches by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the translation model based on the reduced weighting (Weiner, [0239]-[0240] and [0245]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Roy and Agrawal to incorporate the teachings of Weiner and account for a method for determining motional branch current in an ultrasonic transducer of an ultrasonic surgical device over multiple frequencies of a transducer drive signal. The method may comprise, at each of a plurality of frequencies of the transducer drive signal, oversampling a current and voltage of the transducer drive signal, receiving, by a processor, the current and voltage samples, and determining, by the processor, the motional branch current based on the current and voltage samples, a static capacitance of the ultrasonic transducer and the frequency of the transducer drive signal (Weiner, Abstract and [0013]-[0021]).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The amended independent claims recite “causing delivery administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value”, however the specification does not disclose administering/administration of an amount of insulin. The closest paragraphs in the specification disclose [0022]: “That said, in other embodiments, the medical device 102 could be realized as an infusion device configured to deliver a fluid, such as insulin, to the body of the patient” and [0030]: “obtain insulin delivery dosage amounts and corresponding timestamps from the infusion device, and then upload the insulin delivery data to the remote device 114”. The “delivery” aspect of paragraph [0022], under broadest reasonable interpretation, does not equate to administering the medication (insulin) to the patient.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Roy et al. (US 20110237917 A1), hereinafter Roy, in view, of Agrawal et al. (US 20210038163 A1), hereinafter Agrawal, and Wiener et al. (US 20180116706 A9), hereinafter Weiner.
Regarding claim 1 Roy teaches a system comprising: one or more processors ([0030]-[0031]); and one or more processor-readable media storing instructions which, when executed by the one or more processors, cause performance of ([0030]-[0031] and [0034]-[0035]): identifying an error metric associated with an input variable to a translation model that applies a weighting to the input variable to estimate a glucose level of a user ([0024]-[0026], [0030], and [0031]); determining a reference output of the translation model based on a reference input value for the input variable to the translation model (FIG. 11 and [0025]-[0028]); generating a modulated value for the input variable based on the reference input value and the error metric ([0031], [0093], [0094], [0106], [0147], [0156], [0164], [0173], and [0174]); determining a simulated output of the translation model based on the modulated value for the input variable to the translation model ([0065], [0140], [0143], [0145], and [0150]-[0156]); updating the translation model when a difference between the simulated output and the reference output is greater than a threshold ([0106], [0156], and [0157]-[0162]); determining an output glucose value using the updated translation model and one or more values of the input variable derived from one or more electrical signals outputted by a glucose sensor of the user (FIG. 11, [0054], [0069], [0116]: “As illustrated, filter and/or calibration unit 456 may include one or more processors 1102 and at least one memory 1104. In certain example embodiments, memory 1104 may store or otherwise include instructions 1106 and/or sample-measurement data 1108. Sample-measurement data 1108 may include, by way of example but not limitation, blood glucose reference samples measured via a blood sample, blood glucose sensor measurements, blood glucose sample-sensor measurement pairs, combinations thereof’, [0117], and [0156]: “and an update stage, in which a current predicted stage of the system is updated/corrected based on a weighted error generated between a model prediction and a true measurement”);
Roy does not teach causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value.
However, Agrawal teaches causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value (Agrawal, Abstract, [0009]: “infusion and management system can include an insulin infusion device configured to deliver insulin to a user; a blood glucose meter; a source of user activity data; and a processor-based computing device that supports data communication with the insulin infusion device... The processor-readable medium comprises executable instructions configurable to cause the processor device to perform a method for estimating glucose values of a user... a first set of inputs can be received and processed via an estimation model for a user to generate a set of estimated glucose values that track actual glucose values”, [0029], [0058]: “deliver insulin to the body 501 of the patient based on the difference between the sensed glucose value and the target glucose value”, [0067], and [0072]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Roy to incorporate the teachings of Agrawal and account for users knowing their blood glucose levels in any given point, and for patients to determine/witness when it may be necessary to administer insulin outside their scheduled administrations (Agrawal, Abstract and [0004]).
Roy and Agrawal do not teach by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the translation model based on the reduced weighting.
However, Weiner teaches by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the translation model based on the reduced weighting (Weiner, [0239]-[0240] and [0245]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Roy and Agrawal to incorporate the teachings of Weiner and account for a method for determining motional branch current in an ultrasonic transducer of an ultrasonic surgical device over multiple frequencies of a transducer drive signal. The method may comprise, at each of a plurality of frequencies of the transducer drive signal, oversampling a current and voltage of the transducer drive signal, receiving, by a processor, the current and voltage samples, and determining, by the processor, the motional branch current based on the current and voltage samples, a static capacitance of the ultrasonic transducer and the frequency of the transducer drive signal (Weiner, Abstract and [0013]-[0021]).
Regarding claim 2 Roy further teaches the translation model comprises an estimation model for providing an estimated glucose value; determining the reference output comprises determining a reference glucose value using the reference input value; determining the simulated output comprises determining a simulated glucose value using the modulated value ([0030]-[0031], [0065], [0140], [0143], [0145], and [0150]-[0156]); and updating the translation model comprises updating the estimation model to reduce the difference between the simulated glucose value and the reference glucose value by assigning the reduced weighting to the input variable in the estimation model ([0030]-[0031], [0106], [0156], [0157]-[0162], and [0179]).
Regarding claim 3 Roy further teaches the estimation model comprises a sensor glucose estimation model for providing the estimated glucose value; the reference glucose value comprises an estimated glucose value determined by the sensor glucose estimation model as a function of the reference input value and the weighting applied to the input variable; and the simulated glucose value comprises an estimated glucose value determined by the sensor glucose estimation model as a function of the modulated value and the weighting applied to the input variable ([0031], [0093]-[0097], [0115], [0117]-[0118], and [0157]-[0162]).
Regarding claim 4 Roy further teaches the translation model comprises an insight model for generating a notification ([0069], [0061], and [0180]).
Regarding claim 5 Roy further teaches updating the translation model comprises: updating the insight model for generating the notification as a function of the estimated glucose value provided by the updated estimation model having the reduced weighting applied to the input variable ([0025]-[0037], [0069], [0061], and [0180]).
Regarding claim 6 Roy further teaches updating the translation model comprises: reducing the weighting applied to the input variable; determining an updated reference output based at least in part on the reduced weighting; and determining an updated simulated output based at least in part on the reduced weighting applied to the modulated value such that the difference between the simulated output and the reference output is less than the threshold ([0030]-[0031], [0108], [0109], [0160], and [0162]).
Regarding claim 7 Roy further teaches the input variable comprises an output electrical current, an electrode voltage, or an electrochemical impedance spectroscopy (EIS) value determined based on electrical signals provided by an interstitial glucose sensing arrangement ([0107], [0115], [0135], and [0139]).
Regarding claim 8 Roy teaches a processor-implemented method comprising: identifying an error metric associated with an input variable to a translation model, that applies a weighting to the input variable to estimate glucose level of a user ([0024]-[0026], [0030], and [0031]); determining a reference output of the translation model based on a reference input value for the input variable to the translation model (FIG. 11 and [0025 ]-[0028]); generating a modulated value for the input variable based on the reference input value and the error metric ([0031], [0093], [0094], [0106], [0147], [0156], [0164], [0173], and [0174]); determining a simulated output of the translation model based on the modulated value for the input variable to the translation model ([0065], [0140], [0143], [0145], and [0150]-[0156]); updating the translation model when a difference between the simulated output and the reference output is greater than a threshold ([0106], [0156], and [0157]-[0162]); determining an output glucose value using the updated translation model and one or more values of the input variable derived from one or more electrical signals outputted by a glucose sensor of the user (FIG. 11, [0054], [0069], [0116]: “As illustrated, filter and/or calibration unit 456 may include one or more processors 1102 and at least one memory 1104. In certain example embodiments, memory 1104 may store or otherwise include instructions 1106 and/or sample-measurement data 1108. Sample-measurement data 1108 may include, by way of example but not limitation, blood glucose reference samples measured via a blood sample, blood glucose sensor measurements, blood glucose sample-sensor measurement pairs, combinations thereof”, [0117], and [0156]: “and an update stage, in which a current predicted stage of the system is updated/corrected based on a weighted error generated between a model prediction and a true measurement”).
Roy does not teach causing delivery of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value.
However, Agrawal teaches causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value (Agrawal, Abstract, [0009]: “infusion and management system can include an insulin infusion device configured to deliver insulin to a user; a blood glucose meter; a source of user activity data; and a processor-based computing device that supports data communication with the insulin infusion device... The processor-readable medium comprises executable instructions configurable to cause the processor device to perform a method for estimating glucose values of a user... a first set of inputs can be received and processed via an estimation model for a user to generate a set of estimated glucose values that track actual glucose values”, [0029], [0058]: “deliver insulin to the body 501 of the patient based on the difference between the sensed glucose value and the target glucose value”, [0067], and [0072]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Roy to incorporate the teachings of Agrawal and account for users knowing their blood glucose levels in any given point, and for patients to determine/witness when it may be necessary to administer insulin outside their scheduled administrations (Agrawal, Abstract and [0004]).
Roy and Agrawal do not teach by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the translation model based on the reduced weighting.
However, Weiner teaches by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the translation model based on the reduced weighting (Weiner, [0239]-[0240] and [0245]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Roy and Agrawal to incorporate the teachings of Weiner and account for a method for determining motional branch current in an ultrasonic transducer of an ultrasonic surgical device over multiple frequencies of a transducer drive signal. The method may comprise, at each of a plurality of frequencies of the transducer drive signal, oversampling a current and voltage of the transducer drive signal, receiving, by a processor, the current and voltage samples, and determining, by the processor, the motional branch current based on the current and voltage samples, a static capacitance of the ultrasonic transducer and the frequency of the transducer drive signal (Weiner, Abstract and [0013]-[0021]).
Regarding claim 9 Roy further teaches the translation model comprises an estimation model for providing an estimated glucose value; determining the reference output comprises determining a reference glucose value using the reference input value; determining the simulated output comprises determining a simulated glucose value using the modulated value ([0030]-[0031], [0065], [0140], [0143], [0145], and [0150]-[0156]); and updating the translation model comprises updating the estimation model to reduce the difference between the simulated glucose value and the reference glucose value by assigning the reduced weighting to the input variable in the estimation model ([0030]-[0031], [0106], [0156], [0157]-[0162], and [0179]).
Regarding claim 10 Roy further teaches the estimation model comprises a sensor glucose estimation model for providing the estimated glucose value; the reference glucose value comprises an estimated glucose value determined by the sensor glucose estimation model as a function of the reference input value and the weighting applied to the input variable; and the simulated glucose value comprises an estimated glucose value determined by the sensor glucose estimation model as a function of the modulated value and the weighting applied to the input variable ([0031], [0093 ]-[0097], [0115], [0117]-[0118], and [0157]-[0162]).
Regarding claim 11 Roy further teaches the translation model comprises an insight model for generating a notification ([0069], [0061], and [0180]).
Regarding claim 12 Roy further teaches updating the translation model comprises: updating the insight model for generating the notification as a function of the estimated glucose value provided by the updated estimation model having the reduced weighting applied to the input variable ([0025]-[0037], [0069], [0061], and [0180]).
Regarding claim 13 Roy further teaches updating the translation model comprises: reducing the weighting applied to the input variable; determining an updated reference output based at least in part on the reduced weighting; and determining an updated simulated output based at least in part on the reduced weighting applied to the modulated value such that the difference between the simulated output and the reference output is less than the threshold ([0030]-[0031], [0108], [0109], [0160], and [0162]).
Regarding claim 14 Roy further teaches the input variable comprises an output electrical current, an electrode voltage, or an electrochemical impedance spectroscopy (EIS) value determined based on electrical signals provided by an interstitial glucose sensing arrangement ([0107], [0115], [0135], and [0139]).
Regarding claim 15 Roy teaches one or more non-transitory processor-readable media storing instructions which, when executed by one or more processors (Abstract), cause performance of: identifying an error metric associated with an input variable to a translation model that applies a weighting to the input variable to estimate a glucose level of a user ([0024]-[0026], [0030], and [0031]); determining a reference output of the translation model based on a reference value for the input variable to the translation model (FIG. 11 and [0025]-[0028]); generating a modulated value for the input variable based on the reference value and the error metric ([0031], [0093], [0094], [0106], [0147], [0156], [0164], [0173], and [0174]); determining a simulated output of the translation model based on the modulated value for the input variable to the translation model ([0065], [0140], [0143], [0145], and [0150]-[0156]); and updating the translation model when a difference between the simulated output and the reference output is greater than a threshold ([0106], [0156], and [0157]-[0162]); determining an output glucose value using the updated translation model and one or more values of the input variable derived from one or more electrical signals outputted by a glucose sensor of the user (FIG. 11, [0054], [0069], [0116]: “As illustrated, filter and/or calibration unit 456 may include one or more processors 1102 and at least one memory 1104. In certain example embodiments, memory 1104 may store or otherwise include instructions 1106 and/or sample-measurement data 1108. Sample-measurement data 1108 may include, by way of example but not limitation, blood glucose reference samples measured via a blood sample, blood glucose sensor measurements, blood glucose sample-sensor measurement pairs, combinations thereof’, [0117], and [0156]: “and an update stage, in which a current predicted stage of the system is updated/corrected based on a weighted error generated between a model prediction and a true measurement”).
Roy does not teach causing delivery of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value.
However, Agrawal teaches causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value (Agrawal, Abstract, [0009]: “infusion and management system can include an insulin infusion device configured to deliver insulin to a user; a blood glucose meter; a source of user activity data; and a processor-based computing device that supports data communication with the insulin infusion device... The processor-readable medium comprises executable instructions configurable to cause the processor device to perform a method for estimating glucose values of a user... a first set of inputs can be received and processed via an estimation model for a user to generate a set of estimated glucose values that track actual glucose values”, [0029], [0058]: “deliver insulin to the body 501 of the patient based on the difference between the sensed glucose value and the target glucose value”, [0067], and [0072]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Roy to incorporate the teachings of Agrawal and account for users knowing their blood glucose levels in any given point, and for patients to determine/witness when it may be necessary to administer insulin outside their scheduled administrations (Agrawal, Abstract and [0004]).
Roy and Agrawal do not teach by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the translation model based on the reduced weighting.
However, Weiner teaches by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the translation model based on the reduced weighting (Weiner, [0239]-[0240] and [0245]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Roy and Agrawal to incorporate the teachings of Weiner and account for a method for determining motional branch current in an ultrasonic transducer of an ultrasonic surgical device over multiple frequencies of a transducer drive signal. The method may comprise, at each of a plurality of frequencies of the transducer drive signal, oversampling a current and voltage of the transducer drive signal, receiving, by a processor, the current and voltage samples, and determining, by the processor, the motional branch current based on the current and voltage samples, a static capacitance of the ultrasonic transducer and the frequency of the transducer drive signal (Weiner, Abstract and [0013]-[0021]).
Regarding claim 16 Roy further teaches the translation model comprises an estimation model for providing an estimated glucose value; determining the reference output comprises determining a reference glucose value using the reference value for the input variable; determining the simulated output comprises determining a simulated glucose value using the modulated value ([0030]-[0031], [0065], [0140], [0143], [0145], and [0150]-[0156]); and updating the translation model comprises updating the estimation model to reduce the difference between the simulated glucose value and the reference glucose value by assigning the reduced weighting to the input variable in the estimation model ([0030]-[0031], [0106], [0156], [0157]-[0162], and [0179]).
Regarding claim 17 Roy further teaches the estimation model comprises a sensor glucose estimation model for providing an estimated glucose value; the reference glucose value comprises the estimated glucose value determined by the sensor glucose estimation model as a function of the reference value of the input variable and the weighting applied to the input variable; and the simulated glucose value comprises an estimated glucose value determined by the sensor glucose estimation model as a function of the modulated value and the weighting applied to the input variable ([0037], [0093]-[0097], [0115], [0117]-[0118], and [0157]-[0162]).
Regarding claim 18 Roy further teaches the translation model comprises an insight model for generating a notification ([0069], [0061], and [0180]).
Regarding claim 19 Roy further teaches updating the translation model comprises: updating the insight model for generating the notification as a function of the estimated glucose value provided by the updated estimation model having the reduced weighting applied to the input variable ([0025]-[0031], [0069], [0061], and [0180]).
Regarding claim 20 Roy further teaches updating the translation model comprises: reducing the weighting applied to the input variable; determining an updated reference output based at least in part on the reduced weighting; and determining an updated simulated output based at least in part on the reduced weighting applied to the modulated value such that the difference between the simulated output and the reference output is less than the threshold ([0030]-[0031], [0108], [0109], [0160], and [0162]).
Response to Arguments
Applicant's arguments filed 07/11/2025 have been fully considered. Regarding the Non-Statutory Double Patenting Rejection, Applicant argues the amendments to the claims are sufficient to overcome the Rejection. Examiner respectfully disagrees. As shown above, the amendments to the independent claims are not rejected under U.S. Patent No. 11,596,359 B2, Roy, Agrawal, and (the new prior art) Weiner. Therefore, the Non-Statutory Double Patenting Rejection is maintained.
Regarding the 35 U.S.C. 101 Rejection, the amendment of “causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value” overcomes the 101 Rejection because the limitation recite a specific treatment/prophylaxis and specific classification or type of medication by administering medication to a patient of an amount of insulin. Therefore, the 35 U.S.C. 101 Rejection is withdrawn.
Applicant’s arguments with respect to the 35 U.S.C. 103 Rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/R.S.S./Examiner, Art Unit 3681
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681
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