DETAILED ACTION
Applicant’s arguments, filed 03/25/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application. Applicant has amended their claims, filed 03/25/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Applicant has added claim 21. Claims 1-21 are pending and hereby under examination.
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 .
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.
Claims 1-7, 9-13, and 15-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu (US 11147480 – cited by Applicant).
Upon further consideration, Liu was found to teach a risk tolerance profile wherein it was previously communicated that Liu did not teach a risk tolerance profile. As one example, Applicant defines the risk tolerance profile including “a high-level risk tolerance threshold defining a user’s willingness to risk having analyte levels exceed a recommended high analyte level range, and a low-level risk tolerance threshold defining the user’s willingness to risk having the analyte levels fall below a recommended low analyte level range” (Paragraph 0018). Applicant provides more examples in claim 7, including a time range for low/high level analyte events and an acceptable number of low/high level events. Accordingly, a risk tolerance profile is interpreted as one or more indicators of a patient’s willingness (a patient’s preference) to be at, above, or below an analyte level or analyte level range; their willingness to be in or outside that level/range for an amount of time; and/or their willingness for their analyte level to be above or below a range a number of times. Thus, if a value(s) that the risk tolerance profile is based on (e.g., risk tolerance level as in claim 1) is determined partly on a patient’s input or preference in setting that value, that value would define a patient’s “willingness” to be at that level. Therefore, that value would define a patient’s profile of risk tolerance.
Regarding claim 1, Liu teaches a non-transitory computer readable storage medium storing a program comprising instructions that, when executed by at least one processor of a computing device, cause the at least one processor to perform operations including:
receiving sensor data generated by an analyte sensor configured to monitor at least one analyte (Col 11, line 52 – Col 12, line 24);
determining a risk tolerance profile for a user based, at least in part, on one or more risk tolerance levels corresponding to one or more analyte level ranges (Col 12, lines 27-29, threshold band for the CGM that is predetermined, user-specific, or dynamically determined, wherein a “threshold band” is interpreted as a glucose level range; Col 12, line 62 – Col 13, line 13, the threshold band is determined based on attributes about a user includes medical/physical history and/or demographics in combination with a machine learning model that updates/retrains itself based on updated attributes; Col 13, lines 23-29, wherein a user attribute is further defined as a social attribute, medical history/condition, patient preference, etc.); Examiner notes that the threshold band corresponds to a glucose threshold range and is determined based on, among other factors, patient preference. As such, the “risk tolerance level” is interpreted as the patient’s preference to stay within the threshold band, thereby defining a risk tolerance profile.
determining at least one analyte level criteria for the user for the analyte based, at least in part, on the determined risk tolerance profile and the sensor data (Col 12, lines 25-29, wherein a TIR value is determined based on an amount of time a user’s glucose level is within a threshold band, wherein the threshold band is determined based on a user’s preference as described above; Col 15, line 53 – Col 15, line 33, wherein a TIR state is determined based on the TIR values, a TIR state defined as good or bad based on the amount of time the user’s glucose level is within a threshold band);
determining, using a decision support model (Col 7, lines 31-60, wherein different models may be used to optimize results, detect patterns, extract information, etc.), at least one decision support output based, at least in part, on the at least one analyte level criteria (Col 12, lines 7-24, generating alerts based on the glucose data (e.g., too high / low blood sugar levels or showing an unfavorable trend); and
providing the at least one decision support output to the user (Col 12, lines 7-24, wherein a display is used to view glucose readings and/or associated data).
Regarding claim 2, Liu further discloses wherein the at least one analyte level criteria is an optimal level range for the at least one analyte (Col 28, line 60 – Col 29, line 9, wherein optimal glucose levels are determined based on a user’s state and CGM trends).
Regarding claim 3, Liu further teaches wherein the optimal level range comprises a high-level analyte threshold that defines an upper boundary for the at least one analyte and a low-level analyte threshold that defines a lower boundary for the at least one analyte (Col 28, line 60 – Col 29, line 9, wherein optimal glucose levels are determined; Examiner notes that an optimal range to keep the user’s glucose level in inherently discloses an upper boundary and a lower boundary for the analyte level).
Regarding claim 4, Liu further teaches receiving user input that indicates the high-level analyte threshold and the low-level analyte threshold, wherein the high-level analyte threshold and the low-level analyte threshold are determined based on the user input (Col 8, lines 36-54; Col 12, lines 30-61, wherein the machine learning model receives inputs to determine threshold bands for glucose, and wherein the threshold band may be user-specific; Col 17, line 54 – Col 18, line 3, wherein an optimal glucose variability cutoff is determined based on inputs).
Regarding claim 5, Liu further teaches wherein the optimal level range is determined by:
defining a threshold time period (Col 16, lines 13-33, wherein a user’s glucose level is within a threshold band of time; Examiner notes that “defining a threshold time period” is being interpreted based on Applicant’s specification paragraph 0142, wherein a threshold time period may be a time period that includes sufficient data to determine a trend); and
upon determining that the sensor data covers the threshold time period, generating trends using the sensor data to determine the high-level analyte threshold and the low-level analyte threshold (Col 16, lines 13-33, wherein a good vs bad TIR state is determined, which determines optimal cutoff values for glucose levels).
Regarding claim 6, Liu further teaches wherein the optimal level range is determined by:
defining a threshold time period (Col 12, lines 37-39); and
upon determining that the sensor data does not cover the threshold time period, generating trends using sensor data of a cohort to determine the high-level analyte threshold and the low-level analyte threshold (Col 12, lines 30-61, wherein a threshold band is determined based on a cohort of patients). Regarding claim 7, Liu further teaches wherein the one or more risk tolerance levels include a time range for high-level analyte events and/or low-level analyte events (Col 16, lines 13-33, wherein a TIR ratio (a TIR the user’s glucose level is within a threshold band) cutoff is dynamically determined based on patient attributes, such as patient preference as described above, or tailored to be a value that is optimal for the user).
Regarding claim 8, Liu further teaches wherein the risk tolerance profile comprises a high-level risk tolerance threshold defining a user's willingness to risk having analyte levels exceed a recommended high analyte level range, and a low-level risk tolerance threshold defining the user's willingness to risk having the analyte levels fall below a recommended low analyte level range (Col 12, lines 27-29, threshold band for the CGM that is predetermined, user-specific, or dynamically determined, wherein a “threshold band” is interpreted as a glucose level range; Col 12, line 62 – Col 13, line 13, the threshold band is determined based on attributes about a user includes medical/physical history and/or demographics in combination with a machine learning model that updates/retrains itself based on updated attributes; Col 13, lines 23-29, wherein a user attribute is further defined as a social attribute, medical history/condition, patient preference, etc.; Examiner notes that by using patient preference to determine a threshold band for a user’s glucose level, a user is necessarily choosing a “high-level threshold” or a “low-level threshold” that they are not willing to exceed (i.e., their willingness is low / non-existent)).
Regarding claim 9, Liu teaches a method for determining decision support outputs using user-specific analyte level criteria, the method comprising:
receiving sensor data generated by an analyte sensor configured to monitor an analyte (Col 11, line 52 – Col 12, line 24);
determining a risk tolerance profile for a user based, at least in part, on one or more risk tolerance levels corresponding to one or more analyte level ranges (Col 12, lines 27-29, threshold band for the CGM that is predetermined, user-specific, or dynamically determined, wherein a “threshold band” is interpreted as a glucose level range; Col 12, line 62 – Col 13, line 13, the threshold band is determined based on attributes about a user includes medical/physical history and/or demographics in combination with a machine learning model that updates/retrains itself based on updated attributes; Col 13, lines 23-29, wherein a user attribute is further defined as a social attribute, medical history/condition, patient preference, etc.); Examiner notes that the threshold band corresponds to a glucose threshold range and is determined based on, among other factors, patient preference. As such, the “risk tolerance level” is interpreted as the patient’s preference to stay within the threshold band, thereby defining a risk tolerance profile.
determining at least one analyte level criteria for the user for the analyte based, at least in part, on the determined risk tolerance profile and the sensor data (Col 12, lines 25-29, wherein a TIR value is determined based on an amount of time a user’s glucose level is within a threshold band, wherein the threshold band is determined based on a user’s preference as described above; Col 15, line 53 – Col 15, line 33, wherein a TIR state is determined based on the TIR values, a TIR state defined as good or bad based on the amount of time the user’s glucose level is within a threshold band);
determining, using a decision support model (Col 7, lines 31-60, wherein different models may be used to optimize results, detect patterns, extract information, etc.), at least one decision support output based, at least in part, on the at least one analyte level criteria (Col 12, lines 7-24, generating alerts based on the glucose data (e.g., too high / low blood sugar levels or showing an unfavorable trend); and
providing the at least one decision support output to the user (Col 12, lines 7-24, wherein a display is used to view glucose readings and/or associated data).
Regarding claim 10, Liu further teaches wherein the at least one analyte level criteria is an optimal level range for the at least one analyte (Col 28, line 60 – Col 29, line 9, wherein optimal glucose levels are determined based on a user’s state and CGM trends).
Regarding claim 11, Liu further teaches wherein the optimal level range comprises a high-level analyte threshold that defines an upper boundary for the at least one analyte and a low-level analyte threshold that defines a lower boundary for the at least one analyte (Col 28, line 60 – Col 29, line 9, wherein optimal glucose levels are determined; Examiner notes that an optimal range to keep the user’s glucose level in inherently discloses an upper boundary and a lower boundary for the analyte level).
Regarding claim 12, Liu further teaches receiving user input that indicates the high-level analyte threshold and the low-level analyte threshold, wherein the high-level analyte threshold and the low-level analyte threshold are determined based on the user input (Col 8, lines 36-54; Col 12, lines 30-61, wherein the machine learning model receives inputs to determine threshold bands for glucose, and wherein the threshold band may be user-specific; Col 17, line 54 – Col 18, line 3, wherein an optimal glucose variability cutoff is determined based on inputs).
Regarding claim 13, Liu further teaches wherein the one or more risk tolerance levels include a time range for high-level analyte events and/or low-level analyte events (Col 16, lines 13-33, wherein a TIR ratio (a TIR the user’s glucose level is within a threshold band) cutoff is dynamically determined based on patient attributes, such as patient preference as described above, or tailored to be a value that is optimal for the user).
Regarding claim 14, Liu further teaches wherein the risk tolerance profile comprises a high-level risk tolerance threshold defining a user's willingness to risk having analyte levels exceed a recommended high analyte level range, and a low-level risk tolerance threshold defining the user's willingness to risk having the analyte levels fall below a recommended low analyte level range (Col 12, lines 27-29, threshold band for the CGM that is predetermined, user-specific, or dynamically determined, wherein a “threshold band” is interpreted as a glucose level range; Col 12, line 62 – Col 13, line 13, the threshold band is determined based on attributes about a user includes medical/physical history and/or demographics in combination with a machine learning model that updates/retrains itself based on updated attributes; Col 13, lines 23-29, wherein a user attribute is further defined as a social attribute, medical history/condition, patient preference, etc.; Examiner notes that by using patient preference to determine a threshold band for a user’s glucose level, a user is necessarily choosing a “high-level threshold” or a “low-level threshold” that they are not willing to exceed (i.e., their willingness is low / non-existent)).
Regarding claim 15, Liu teaches a computing device for determining decision support outputs using user-specific analyte level criteria, the computing device comprising:
a network interface (Fig. 2, network 32);
a processor operatively connected to the network interface (Fig. 3, processors 301-1 and 304-1;
a memory (Fig. 3, memory 301-2 and 304-2) storing a program comprising instructions that, when executed by the processor (Col 8, lines 19-35), cause the computing device to:
receive, using the network interface (Col 14, lines 40-54), sensor data generated by an analyte sensor configured to monitor at least one analyte (Col 11, line 52 – Col 12, line 24);
determine a risk tolerance profile for a user based, at least in part, on one or more risk tolerance levels corresponding to one or more analyte level ranges (Col 12, lines 27-29, threshold band for the CGM that is predetermined, user-specific, or dynamically determined, wherein a “threshold band” is interpreted as a glucose level range; Col 12, line 62 – Col 13, line 13, the threshold band is determined based on attributes about a user includes medical/physical history and/or demographics in combination with a machine learning model that updates/retrains itself based on updated attributes; Col 13, lines 23-29, wherein a user attribute is further defined as a social attribute, medical history/condition, patient preference, etc.); Examiner notes that the threshold band corresponds to a glucose threshold range and is determined based on, among other factors, patient preference. As such, the “risk tolerance level” is interpreted as the patient’s preference to stay within the threshold band, thereby defining a risk tolerance profile.
determine at least one analyte level criteria for the user for the analyte based, at least in part, on the determined risk tolerance profile and the sensor data (Col 12, lines 25-29, wherein a TIR value is determined based on an amount of time a user’s glucose level is within a threshold band, wherein the threshold band is determined based on a user’s preference as described above; Col 15, line 53 – Col 15, line 33, wherein a TIR state is determined based on the TIR values, a TIR state defined as good or bad based on the amount of time the user’s glucose level is within a threshold band);
determine, using a decision support model (Col 7, lines 31-60, wherein different models may be used to optimize results, detect patterns, extract information, etc.), at least one decision support output based, at least in part, on the at least one analyte level criteria (Col 12, lines 7-24, generating alerts based on the glucose data (e.g., too high / low blood sugar levels or showing an unfavorable trend); and
provide the at least one decision support output to the user (Col 12, lines 7-24, wherein a display is used to view glucose readings and/or associated data).
Regarding claim 16, Liu further teaches wherein the at least one analyte level criteria is an optimal level range for the at least one analyte (Col 28, line 60 – Col 29, line 9, wherein optimal glucose levels are determined based on a user’s state and CGM trends).
Regarding claim 17, Liu further teaches wherein the optimal level range comprises a high-level analyte threshold that defines an upper boundary for the at least one analyte and a low-level analyte threshold that defines a lower boundary for the at least one analyte (Col 28, line 60 – Col 29, line 9, wherein optimal glucose levels are determined; Examiner notes that an optimal range to keep the user’s glucose level in inherently discloses an upper boundary and a lower boundary for the analyte level).
Regarding claim 18, Liu further teaches receiving user input that indicates the high-level analyte threshold and the low-level analyte threshold, wherein the high-level analyte threshold and the low-level analyte threshold are determined based on the user input (Col 8, lines 36-54; Col 12, lines 30-61, wherein the machine learning model receives inputs to determine threshold bands for glucose, and wherein the threshold band may be user-specific; Col 17, line 54 – Col 18, line 3, wherein an optimal glucose variability cutoff is determined based on inputs).
Regarding claim 19, Liu further teaches wherein the one or more risk tolerance levels include a time range for high-level analyte events and/or low-level analyte events (Col 16, lines 13-33, wherein a TIR ratio (a TIR the user’s glucose level is within a threshold band) cutoff is dynamically determined based on patient attributes, such as patient preference as described above, or tailored to be a value that is optimal for the user).
Regarding claim 20, Liu further teaches wherein the risk tolerance profile comprises a high-level risk tolerance threshold defining a user's willingness to risk having analyte levels exceed a recommended high analyte level range, and a low-level risk tolerance threshold defining the user's willingness to risk having the analyte levels fall below a recommended low analyte level range (Col 12, lines 27-29, threshold band for the CGM that is predetermined, user-specific, or dynamically determined, wherein a “threshold band” is interpreted as a glucose level range; Col 12, line 62 – Col 13, line 13, the threshold band is determined based on attributes about a user includes medical/physical history and/or demographics in combination with a machine learning model that updates/retrains itself based on updated attributes; Col 13, lines 23-29, wherein a user attribute is further defined as a social attribute, medical history/condition, patient preference, etc.; Examiner notes that by using patient preference to determine a threshold band for a user’s glucose level, a user is necessarily choosing a “high-level threshold” or a “low-level threshold” that they are not willing to exceed (i.e., their willingness is low / non-existent)).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Liu
Regarding claim 21, Liu discloses displaying one or more analyte ranges (Col 12, lines 7-24, wherein a display is used to view glucose readings and/or associated data) and setting a threshold band for a user’s glucose levels (Col 12, lines 27-29, threshold band for the CGM that is predetermined, user-specific, or dynamically determined, wherein a “threshold band” is interpreted as a glucose level range; Col 12, line 62 – Col 13, line 13, the threshold band is determined based on attributes about a user includes medical/physical history and/or demographics in combination with a machine learning model that updates/retrains itself based on updated attributes; Col 13, lines 23-29, wherein a user attribute is further defined as a social attribute, medical history/condition, patient preference, etc.). Liu does not explicitly disclose displaying the analyte ranges for the user and having the user specify a risk tolerance for the displayed one or more analyte level ranges.
Liu discloses input devices for a user to input data (Col 5, lines 51-59). As Liu is concerned with setting a glucose threshold band based on user input, one of ordinary skill would recognize that by displaying an analyte range to a user first, a user could then set a threshold of the analyte range to their preference. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Liu to display an analyte threshold and have a user set their preferred threshold band for their glucose levels in order to provide a user-specific glucose monitoring system.
Response to Arguments
Applicant’s arguments, see page 8, filed 03/25/2026, with respect to drawings objections have been fully considered and are persuasive. Applicant has corrected a typographical error of the word hyperparameter. The objection of the drawings has been withdrawn.
Applicant’s arguments, see pages 8-9, filed 03/25/2026, with respect to the 35 U.S.C. §112(b) rejection have been fully considered and are persuasive. Applicant has clarified that the analyte level criteria is based on the determined risk tolerance profile and the sensor data. The rejection of the claims has been withdrawn.
Applicant’s arguments, see pages 9-12, filed 03/25/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. §102(a)(1) and 35 U.S.C. §103 have been fully considered but are persuasive.
Applicant asserts that Liu and Rosinko, alone or in combination, fail to teach the amended features of claims 1, 9, and 15. As recited in claims 1, 9, and 15, a user tolerance profile is based on a risk tolerance level corresponding to one or more analyte level ranges. As described above, Liu teaches setting a threshold band as a glucose level range based on a user preference. This is interpreted to mean that a glucose level range is set based on a user’s preference for their glucose levels to be within a threshold band; thus, defining the user’s risk tolerance profile.
Applicant then asserts that Liu and/or Rosinko fail to teach the amended limitations of claim 7. Examiner disagrees. As described above, Liu teaches that a time in range (TIR) value and/or a TIR cutoff is also determined based on user preference. The cutoff is described as either good or bad, i.e., being within or outside of the threshold band. Examiner interprets this to mean that a time in range is set, based on a user preference, for a high-level glucose range and/or a low-level glucose range; thus, a TIR range is set for a glucose event.
Upon further consideration of the amendment to the claims, the rejection above has been updated in view of Liu.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOAH MICHAEL HEALY whose telephone number is (703)756-5534. The examiner can normally be reached Monday - Friday 8:30am - 5:30pm ET.
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/NOAH M HEALY/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791
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