DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/29/2025 has been entered.
Claims 19-22, 24, 26, 49-52, 65-66, and 69-77 are hereby the present claims under consideration.
Claim Rejections - 35 USC § 112(b)
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 19-22, 24, 26, 49-52, 65-66, and 69-77 are 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.
Claim 19 recites “a computing device comprising a learning module … to instances where historical measured glucose values converted from historical data streams from the continuous glucose sensor were determined to be not reflective of historical actual glucose concentrations” and “detecting, based on the comparing, whether an event in which …” but it is unclear if the “instances” of the learning module are the same as, related to, or different from the “event” detected based on the comparing. For the purposes of this examination, the limitation will be interpreted as referring to the same events.
Claim 19 recites “in response to detecting that the event has not occurred” but the previously recited detecting and comparing step appears to only determine when an event has occurred. Thus it is unclear what “detection” of an event not occurring is referring to. For the purposes of this examination, the limitation will be interpreted as any instance where no event is detected.
Claim 19 recites “the current measured glucose values” in lines 28 and 37-38 but it is unclear if these limitations are the same as, related to, or different from “a current data stream from the continuous glucose sensor”. For the purposes of this examination, the limitations will be interpreted as referring to the same glucose values.
Claims 20-22, 24, 26, 49-52, 65-66, and 69-77 are rejected by virtue of their dependence on claim 19.
Claim 51 recites “determining, based on comparing the current data stream and the one or more current additional data streams to historical data trends in the historical data set related to instances where historical measured glucose values converted from historical data streams from the continuous glucose sensor indicated, that a future time frame will result in future measured glucose values to be converted from a future data stream acquired from the continuous glucose sensor that will be reflective of future actual glucose concentrations of the user” but it is unclear if the “historical data trends” are the same as, related to, or different from “the instances” and/or “events” of claim 19. Additionally, it is unclear if this comparison step is being carried out the by the learning module. It is unclear if the learning module performed the step of identifying the “historical data trends in the historical data set” or if these trends are generated or otherwise identified through some other mechanism. For the purposes of this examination, the trends will be interpreted as being identified by the learning module.
Claim 69 recites “detecting … determining …” in lines 1-5, which is a method step in an action claim. A single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, because it creates confusion as to when direct infringement occurs. (MPEP 2173.05(p) citing In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 97 USPQ2d 1737 (Fed. Cir. 2011)). For the purposes of this examination, the limitations will be interpreted as further limiting the operations of the computing device.
Claim Rejections - 35 USC § 112(a)
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 19, 51, 66, and 70 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.
Claim 19 recites “a learning module trained using a historical data set corresponding to previously acquired data from the continuous glucose sensor and the one or more adjunct sensors to produce learned associative patterns in the historical data set related to instances where historical measured glucose values converted from historical data streams from the continuous glucose sensor were determined to be not reflective of historical actual glucose concentrations of the user” but the specification does not appear to fully support the claimed scope of the learning algorithm and its function of learning patterns associated with inaccurate glucose values. In particular, the structure of learning algorithm itself is not described. Additionally, the particular training methos utilized is not described in sufficient detail. Paragraphs 0070, 0080, 0104, 0111, and 0138 provide only generic statements of functionality and generic descriptions of the algorithm and training method. In particular, paragraph 0138 recites that the algorithm may be some form of artificial intelligence and may be trained using supervised learning, unsupervised learning, reinforcement learning or some combination thereof which does not describe a particular training method. Each of the recited method refer to broad categories of possible training methods. The specification appears to detail the possible inputs in paragraph 0080 but the specification does not detail how these input are processed to achieve the desired output of the identified patterns and events. As per MPEP 2161.01 It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. The specification does not recite the particular steps taken to train the algorithm or the structure of the machine learning model nor do they provide the particular steps taken to identify the events indicative of inaccurate glucose data.
Claim 19 recite “initiate a compensation operation to generate corrected glucose values from the current measured glucose values using a correction factor associated with the event” but the specification does not appear to describe what the compensation operation or correction factor are or how it is applied to yield corrected glucose values. The specification further does not appear to describe how the correction factor is produced or otherwise determined. In particular, paragraph 0033 merely recites that the compensation operation is applied to yield corrected glucose values within some margin of error of the actual glucose values but does not appear to describe what the operation is or what the margin of error is nor how it is known. Furthermore, paragraphs 0138-0140 provide mere statements of functionality and do not detail how the recited outputs are produced from the machine learning algorithm. In particular, the steps taken to determine a correction factor for a particular circumstance or situation reflected by the adjunct sensors does not appear to be disclosed. The disclosed examples of paragraphs 0114-0121 are not considered sufficient to support the claimed genus of applying an appropriate compensation operation in response to any event that affects sensor accuracy being detected. In particular the specification only provides examples of instances where glucose measurement may become inaccurate and does not specifically describe what the compensation operation for each of these events entails and how it may be determined for the various types of events. The process of generating the compensation operation or a description of what it entails is not disclosed for the described species nor is an underlying methodology of how the compensation operation is generated.
Claim 51 recites “determining, based on comparing the current data stream and the one or more current additional data streams to historical data trends in the historical data set related to instances where historical measured glucose values converted from historical data streams from the continuous glucose sensor indicated, that a future time frame will result in future measured glucose values to be converted from a future data stream acquired from the continuous glucose sensor that will be reflective of future actual glucose concentrations of the user” but the specification does not appear to recite a method for determining that future data values will or will not be reflective of future actual glucose concentrations. In particular, paragraphs 0187-0188 seem to merely provide a generic recitation of functionality but do not provide the particular method used to make such a determination. Furthermore, Paragraph 0135 further provides a mere statement of functionality that the current signals can be used in conjunction with the learned historical patterns to determine that a future data stream will be reflective of an actual glucose value. The specification does not appear to detail how future timeframes of actual glucose values are identified. Additionally, the specification does not appear to detail how such trends are identified in the historical data.
Claim 66 recites “wherein the learning module is trained using the historical data set corresponding to the previously acquired data from the continuous glucose sensor and the one or more adjunct sensors to produce a plurality of correction factors comprising the correction factor” but the specification does not appear to describe how the learning module is trained to produce the correction factors. The specification does not appear to describe the particular method carried out by the learning module to determine the correction factors. Paragraphs 0138, 0140, 0142, and 0178 provide only generic statements of functionality that the correction factors are generated by the machine learning algorithm. The particular method of generation nor the particular method of training and the structure of the learning algorithm do not appear to be disclosed. It is insufficient that one of ordinary skill in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. The specification provides only the input and outputs of the learning module and generic statements of training.
Claim 70 recites “a confidence level” but the specification does not appear to describe the particular method for determining the confidence level of the corrected glucose values. Paragraph 0125 provides only a generic statement of functionality that a confidence level is provided. The specification does not appear to describe the particular method of generating this confidence level or what factors are considered in its generation.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 19, 24, 26, 65-66, 69, and 73-77 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Harley-Trochimczyk US Patent Application Publication Number US 2019/0223766 A1 hereinafter Harley.
Regarding claim 19, Harley discloses a glucose sensor system (Abstract) comprising:
a continuous glucose sensor for interstitial implantation into skin of a user (Paragraph 0363: continuous analyte sensor may be a glucose sensor; Fig. 2C, the sensor is interstitially implanted into a user; Paragraph 0388: glucose from interstitial fluid);
one or more adjunct sensors comprising at least one of a pressure sensor, an accelerometer, or a heart rate sensor (Paragraph 0356: one or more additional sensors including heart rate, activity sensors such as accelerometers, and/or pressure sensors);
an insulin pump (Paragraph 0343: medication delivery pump); and
a computing device comprising a learning module trained using a historical data set corresponding to previously acquired data from the continuous glucose sensor and the one or more adjunct sensors to produce learned associative patterns in the historical data set related to instances where historical measured glucose values converted from historical data streams from the continuous glucose sensor were determined to be not reflective of historical actual glucose concentrations of the user (Paragraph 0394: the system may compensate for the effects of temperature, or other one or more other sensor signals, using a learned or defined relationship between the input signals and the analyte levels estimated; Paragraph 0437: an algorithm or model to determine the compensated analyte values trained from subject or population data. The inputs include temperature glucose values, heart rate, activity signals, and a plurality of other variables. The model may output a glucose level or a compensated glucose level. Paragraphs 0483-0484: the model may use previous data from a host or population for learning: Paragraphs 0451-0455: the learned and identified patterns may include temperature patterns and/or state patterns. The state patterns may be determined from heart rate and activity information as well as a plurality of other data types. The patterns indicate when compensation should be applied and what type as well as determining reliability measures; Paragraphs 0456-460: a model may be selected or modified based on a detected condition, the detected condition may be detected from the adjunct sensor data. Thus the algorithm is trained based on historic data and can detect patterns that indicate certain conditions where the glucose calculation must be calibrated to provide accurate glucose values. The learned patterns may be temperature data and/or data from the other adjunct sensors to identify various states),
wherein the computer device stores instructions in non-transitory memory (Paragraph 0356: a memory) that, when executed, cause the computing device to perform operations comprising:
retrieving a current data stream from the continuous glucose sensor (Paragraphs 0356-0357: the received glucose sensor signal);
retrieving one or more current additional data streams from the one or more adjunct sensors (Paragraphs 0356-0357: the temperature data and the one or more signals from additional sensors);
comparing, using the learning module, the current data stream and the one or more current additional data streams to the learned associative patterns (Paragraphs 0437 and 0451-0455: comparing the temperature signal and/or adjunct sensor signal to a pattern to determine the compensation to be applied based on the pattern and/or state; Paragraphs 0460 and 0463: the pattern may be used to detect a state.)
detecting, based on the comparing, whether an event in which current measured glucose values converted from the current data stream are not reflective of current actual glucose concentrations of the user has occurred (Paragraphs 0356-0357: the sensed data are used to determine if a compensated glucose value should be determined and what compensation operation to apply; Paragraphs 0451-0455: the incoming data is matched to a pattern or state; Paragraphs 0456-460 and 0463: the detected state or pattern is used to select or modify a model);
in response to detecting that the event has not occurred (Paragraph 0353: the standard conversion of raw or filtered data into calibrated data; Paragraph 0466: detect that an event, or lack thereof, does not warrant compensation. This limitation is further considered to be inherently disclosed by the detection of events where compensation is required. All other time points are considered a “detection” of no event occurring.)
activating the insulin pump to initiate insulin delivery to the user as a function of the current measured glucose values (Paragraphs 0417, 0476, and 0487: activating the insulin pump. The activation may be based at least in part on the compensated values which also indicates that the pump may be activated without using compensated values); and
in response to detecting that the event has occurred (Paragraphs 0451-0455: the detection of a particular pattern or state):
initiating a compensation operation to generate corrected glucose values from the current measured glucose values using a correction factor associated with the event (Paragraph 0357: the temperature signals and one or more additional signals may be used to determine a specific sensitivity value or may determine a compensated glucose value. The system may apply a look-up table or algorithm to the raw glucose data to produce a compensated glucose level; Paragraphs 0456-0460 and 0463: the model selected or modified based on the detected pattern or state), and
activating the insulin pump as a function of the corrected glucose values (Paragraphs 0417, 0476, and 0487: activating the insulin pump. The activation may be based at least in part on the compensated values)
It is noted that Harley Paragraphs 0456-460 teach that a model may be selected or modified based on a detected condition, the detected condition may be detected from the adjunct sensor data other than the temperature data as recited in paragraph 0455. Paragraphs 0460 and 0463 teach that the compensation operation is referred to as temperature compensation but is not limited to the compensation of only temperature effects. Effects from other data streams may be included in the temperature compensation such as the effects induced by pressure changes and changes in activity levels. Thus where Harley refers to temperature compensation, it is understood that the compensation may also include compensation for other effects detected from other data streams such as pressure and activity.
Regarding claim 24, Harley discloses the glucose sensor system of claim 19. Harley further discloses the system wherein the operations comprise: preventing at least one of an audible alarm or vibrational alarm from being activated in response to the corrected glucose values not exceeding the adjusted a glucose concentration threshold; and activating the at least one of the audible alarm or vibrational alarm in response to the corrected glucose values exceeding the adjusted glucose concentration threshold for a predetermined amount of time (Paragraph 0373: alarms are generated when hyper or hypoglycemia conditions are detected or predicted. Alarms are not triggered when glucose is within the normal range. The predetermined period of time may be any time. The alarms may be visual, audible, or vibratory ).
Regarding claim 26, Harley discloses the glucose sensor system of claim 19. Harley further discloses the system wherein: the learned associated patterns of data are first learned associated patterns of data, the historical data streams are first historical data streams, and the historical measured glucose values are first historical measured glucose values; and the historical data set comprises second learned associative patterns of data related to instances where second historical measured glucose values converted from second historical data streams from the continuous glucose sensor were determined to be reflective of the historical actual glucose concentrations (Paragraph 0394: the system may compensate for the effects of temperature, or other one or more other sensor signals, using a learned or defined relationship between the input signals and the analyte levels estimated; Paragraph 0437: an algorithm or model to determine the compensated analyte values trained from subject or population data. The inputs include temperature glucose values, heart rate, activity signals, and a plurality of other variables. The model may output a glucose level or a compensated glucose level. Paragraphs 0483-0484: the model may use previous data from a host or population for learning: Paragraphs 0451-0455: the learned and identified patterns may include temperature patterns and/or state patterns. The state patterns may be determined from heart rate and activity information as well as a plurality of other data types. The patterns indicate when compensation should be applied and what type as well as determining reliability measures; Paragraphs 0456-460: a model may be selected or modified based on a detected condition, the detected condition may be detected from the adjunct sensor data. Paragraphs 0460-0463 describe three different patterns or states which may be detected which each correspond to their own compensation operation and thus there are a plurality of historical patterns and corresponding physiological responses used in the training data.).
Regarding claim 65, Harley discloses the glucose sensor system of claim 19. Harley further discloses the system wherein the current data stream is indicative of calibrated sensor values outputted from the continuous glucose sensor (Paragraph 0353: the raw or filtered sensors data is converted into calibrated data; Paragraph 0358: calibrated data is distinct from temperature-compensated data; Paragraph 0511: the calibration value is received and used to update the temperature compensation model which produces temperature compensated values).
Regarding claim 66, Harley discloses the glucose sensor system of claim 19. Harley further discloses the system wherein the learning module is trained using the historical data set corresponding to the previously acquired data from the continuous glucose sensor and the one or more adjunct sensors to produce a plurality of correction factors comprising the correction factor (Paragraph 0394: the system may compensate for the effects of temperature, or other one or more other sensor signals, using a learned or defined relationship between the input signals and the analyte levels estimated; Paragraph 0437: an algorithm or model to determine the compensated analyte values trained from subject or population data. The inputs include temperature glucose values, heart rate, activity signals, and a plurality of other variables. The model may output a glucose level or a compensated glucose level. Paragraphs 0483-0484: the model may use previous data from a host or population for learning: Paragraphs 0451-0455: the learned and identified patterns may include temperature patterns and/or state patterns. The state patterns may be determined from heart rate and activity information as well as a plurality of other data types. The patterns indicate when compensation should be applied and what type as well as determining reliability measures; Paragraphs 0456-460: a model may be selected or modified based on a detected condition, the detected condition may be detected from the adjunct sensor data. The selection and/or adjustment of a model is considered an application of a correction factor. Paragraphs 0460-0463 describe three different patterns or states which may be detected which each correspond to their own compensation operation and thus there are a plurality of historical patterns and corresponding physiological responses used in the training data, and a plurality of models to be selected and/or adjustments to be made to the models).
Regarding claim 69, Harley discloses the glucose sensor system of claim 66. Harley further discloses the system wherein: detecting, based on the comparing, whether the event has occurred comprises: detecting whether any of a plurality of events comprising the event has occurred, wherein each of the plurality of events is associated with a corresponding one of the plurality of correction factors (Paragraphs 0451-0463: the matching of the input data to a pattern and/or state and the model selection/modification associated with the determined model and/or state.).
Regarding claim 73, Harley discloses the glucose sensor system of claim 19. Harley further discloses the system comprising: the one or more adjunct sensors are a plurality of adjunct sensors, the plurality of adjunct sensors comprising: (i) at least one of the pressure sensor, the accelerometer, or the heart rate sensor; and (ii) a temperature sensor (Paragraph 0356: the temperature sensors and the one or more additional sensors including heart rate, activity sensors such as accelerometers, and/or pressure sensors).
Regarding claim 74, Harley discloses the glucose sensor system of claim 73. Harley further discloses the system wherein: the event is associated with a local rise in temperature in a region around the glucose sensor system (Paragraphs 0460 and 0462: the compensation may be based on a condition of heat flux and direction. The compensation may be different for temperature increases than for temperature decreases).
Regarding claim 75, Harley discloses the glucose sensor system of claim 19. Harley further discloses the system, wherein: the one or more adjunct sensors comprises the accelerometer; and the event is associated with movement of the user (Paragraphs 0460 and 0463: the compensation operation may be based on a condition of activity such as running measured from the accelerometer).
Regarding claim 76, Harley discloses the glucose sensor system of claim 19. Harley further discloses the system, wherein: the one or more adjunct sensors comprises the accelerometer; and the event is associated with a posture of the user (Paragraph 0460: the compensation operation may be based on a condition of posture as determined by a 3-axis accelerometer).
Regarding claim 77, Harley discloses the glucose sensor system of claim 19. Harley further discloses the system, wherein: the one or more adjunct sensors comprises the pressure sensor; and the event is associated with pressure applied to an area of the skin of the user in which the continuous glucose sensor is implanted (Paragraph 0460: the compensation operation may be based on a condition of pressure such as when the patient lies on the glucose sensor).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Harley-Trochimczyk US Patent Application Publication Number US 2019/0223766 A1 hereinafter Harley as applied to claim 19 above and further in light of the teachings and/or suggestions of Harley.
Regarding claim 20, Harley discloses the glucose sensor system of claim 19. Harley further at least suggests the system comprising: a display operably linked to the computing device (Paragraph 0347: display devices); and wherein the operations comprise: causing the display to present values indicative of the corrected glucose values and an indication that the presented values correspond to the corrected glucose values (Paragraph 0368: the data presented on the display may be a variety of data including the calibrated or compensated data and the data sent to the display device may be customized; Paragraph 0379: the display may provide a variety of types of data. Thus Harley teaches the display of various data types including the raw and compensated sensors data. Such a display at least suggests that the type of data displayed is labelled so that they may be differentiated from each other.).
Regarding claim 21, Harley teaches the glucose sensor system of claim 20. Harley further at least suggests the system wherein the indication that the presented values correspond to the corrected glucose values comprises one or more of: a flashing indicator associated with the presented values, a color associated with the presented values that is different from a color associated with the current measured glucose values, or a textual message indicating that the presented values correspond to the corrected glucose values (Paragraph 0368: the data provided to the display may be customizable; Paragraph 0348: the display may include a numeric value, an arrow, or a color code. These recitations are considered sufficient to at least suggest some form of indication being presented with the compensated data such as it being presented in a different color or with a textual label since, as described above, the presentation of a plurality of different data types if considered to at least suggest some form or labelling or differentiation being present between the various data types presented.)
Claims 22 and 52 are rejected under 35 U.S.C. 103 as being unpatentable over Harley-Trochimczyk US Patent Application Publication Number US 2019/0223766 A1 hereinafter Harley as applied to claim 19 above and further in view of Garcia US Patent Application Publication Number US 2017/0071511 A1 hereinafter Garcia in view of Roy US Patent Application Publication Number US 2018/0174675 A1 hereinafter Roy
Regarding claim 22, Harley teaches the glucose sensor system of claim 19. Harley fails to further teach the system wherein the operations comprise: preventing a calibration operation from being initiated during a current time frame when the compensation operation is performed to yield the corrected glucose values; and rescheduling the calibration operation for another time in response to preventing the calibration operation from being initiated during the current time frame.
Garcia teaches systems and methods to calibrate an analyte concentration sensor within a biological system, generally using only a signal from the analyte concentration sensor. For example, at a steady state, the analyte concentration value within the biological system is known, and the same may provide a source for calibration. Similar techniques may be employed with slow-moving averages (Abstract). Thus Garcia is reasonably pertinent to the problem at hand.
Garcia teaches that continuous glucose sensors may be self-calibrated based on steady-state readings (Paragraphs 0011 0018-0019 and 0024).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the self-calibration methods taught by Garcia into the system of Harley because such self-calibration measures may help improve sensor accuracy and negate drift. Furthermore, it would be obvious to one of ordinary skill in the art to configure the system of Harley in view of Garcia to implement the steady state self-calibration during periods where the glucose sensor data is not being compensated. Such a configuration would be obvious because the compensation operations of Harley such as those described in paragraphs 460-0463 of Harley are implements in response to events that alter the accuracy of the measured glucose values. Additionally, the events during which the glucose values are compensated are transitory and are thus unlikely to extend for a required duration to reach a steady state value and/or are events which themselves affect the glucose concentration, such as exercise, which preclude the event from reaching a steady state of glucose concentration. Thus it would seem that periods where the glucose value of Harley is being compensated are ill suited to reach steady-state levels required for the self-calibration of Garcia. As such, it would be obvious to configure the system to prevent self-calibration during periods of temperature compensation.
Harley in view of Garcia fails to further teach the system comprising: and rescheduling the calibration operation for another time in response to preventing the calibration operation from being initiated during the current time frame.
Roy teaches a method of operating an infusion device capable of delivering fluid to a patient involves predicting, by a control system associated with the infusion device, a future occurrence of an event based at least in part on historical data associated with the patient, and prior to the future occurrence of the event, automatically adjusting a control parameter for operating the infusion device based at least in part on the event and automatically operating an actuation arrangement of the infusion device to deliver the fluid to the patient based at least in part on a current measurement of the physiological condition and the adjusted control parameter (Abstract). Thus, Roy is reasonably pertinent to the problem at hand.
Roy teaches a system which analyzes a user’s historical data to generate behavior patterns and their corresponding physiological response. The behavior patterns may be used to predict when the user is going to perform an activity that will affect their response or sensitivity to administered insulin such as eating a meal, exercising, or other target activities. The prediction of such activities allows the adjustment of closed-loop therapy in anticipation of the activities (Paragraphs 0030-0034). Thus, Roy teaches a system which learns patterns of user behavior and adjusts the system in response to the patterns.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the behavior pattern learning of Roy into the system of Harley in view of Garcia because such a learned behavior pattern would allow the system of Harley in view of Garcia to preemptively administer insulin for anticipated meal times and further allows the system to predict when the user will be performing activities that affect their insulin sensitivity. Such actions, such as exercising, may also affect the measurement accuracy of the system as taught by Harley (Paragraph 0463) and being able to predict these events may allow the system to better compensate for their occurrences. Additionally, it would be obvious to one of ordinary skill in the art to apply this learned behavior monitoring to the system of Harley in view of Garcia in order to schedule and/or reschedule a self-calibration event for periods where it is predicted that the user will not be partaking in any activity that affects insulin levels or sensor sensitivity so that the desired steady-state readings may be achieved for the self-calibration operation.
Regarding claim 52, Harley teaches the glucose sensor system of claim 19. Harley fails to further teach the system wherein the operations comprise: in response to determining that the current measured glucose values are not reflective of the current actual glucose concentrations: preventing a calibration operation from being performed; and rescheduling the calibration operation.
Garcia teaches that continuous glucose sensors may be self-calibrated based on steady-state readings (Paragraphs 0011 0018-0019 and 0024).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the self-calibration methods taught by Garcia into the system of Harley because such self-calibration measures may help improve sensor accuracy and negate drift. Furthermore, it would be obvious to one of ordinary skill in the art to configure the system of Harley in view of Garcia to implement the steady state self-calibration during periods where the glucose sensor data is not being compensated, or periods where the measured glucose values are not reflective of the actual glucose values. Such a configuration would be obvious because the compensation operations of Harley such as those described in paragraphs 460-0463 of Harley are implements in response to events that alter the accuracy of the measured glucose values. Additionally, the events during which the glucose values are compensated are transitory and are thus unlikely to extend for a required duration to reach a steady state value and/or are events which themselves affect the glucose concentration, such as exercise, which preclude the event from reaching a steady state of glucose concentration. Thus it would seem that periods where the glucose value of Harley is being compensated are ill suited to reach steady-state levels required for the self-calibration of Garcia. As such, it would be obvious to configure the system to prevent self-calibration during periods of temperature compensation.
Harley in view of Garcia fails to further teach the system comprising: rescheduling the calibration operation.
Roy teaches a system which analyzes a user’s historical data to generate behavior patterns and their corresponding physiological response. The behavior patterns may be used to predict when the user is going to perform an activity that will affect their response or sensitivity to administered insulin such as eating a meal, exercising, or other target activities. The prediction of such activities allows the adjustment of closed-loop therapy in anticipation of the activities (Paragraphs 0030-0034). Thus, Roy teaches a system which learns patterns of user behavior and adjusts the system in response to the patterns.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the behavior pattern learning of Roy into the system of Harley in view of Garcia because such a learned behavior pattern would allow the system of Harley in view of Garcia to preemptively administer insulin for anticipated meal times and further allows the system to predict when the user will be performing activities that affect their insulin sensitivity. Such actions, such as exercising, may also affect the measurement accuracy of the system as taught by Harley (Paragraph 0463) and being able to predict these events may allow the system to better compensate for their occurrences. Additionally, it would be obvious to one of ordinary skill in the art to apply this learned behavior monitoring to the system of Harley in view of Garcia in order to schedule and/or reschedule a self-calibration event for periods where it is predicted that the user will not be partaking in any activity that affects insulin levels or sensor sensitivity so that the desired steady-state readings may be achieved for the self-calibration operation.
Claims 51 is rejected under 35 U.S.C. 103 as being unpatentable over Harley-Trochimczyk US Patent Application Publication Number US 2019/0223766 A1 hereinafter Harley as applied to claim 19 above and further in view of Roy US Patent Application Publication Number US 2018/0174675 A1 hereinafter Roy in view of Garcia US Patent Application Publication Number US 2017/0071511 A1 hereinafter Garcia
Regarding claim 51, Harley teaches the glucose sensor system of claim 19. Harley fails to further teach the system wherein the operations comprise: determining, based on comparing the current data stream and the one or more current additional data streams to historical data trends in the historical data set related to instances where historical measured glucose values converted from historical data streams from the continuous glucose sensor indicated , that a future time frame will result in future measured glucose values to be converted from a future data stream acquired from the continuous glucose sensor that will be reflective of future actual glucose concentrations of the user; and in response to determining that the future measured glucose values will be reflective of the future actual glucose concentrations, schedule a calibration operation to be performed during the future time frame.
Roy teaches a system which analyzes a user’s historical data to generate behavior patterns and their corresponding physiological response. The behavior patterns may be used to predict when the user is going to perform an activity that will affect their response or sensitivity to administered insulin such as eating a meal, exercising, or other target activities. The prediction of such activities allows the adjustment of closed-loop therapy in anticipation of the activities (Paragraphs 0030-0034). Thus, Roy teaches a system which learns patterns of user behavior and adjusts the system in response to the patterns.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the behavior pattern learning of Roy into the system of Harley because such a learned behavior pattern would allow the system of Harley to preemptively administer insulin for anticipated meal times and further allows the system to predict when the user will be performing activities that affect their insulin sensitivity. Such actions, such as exercising, may also affect the measurement accuracy of the system as taught by Harley (Paragraph 0463) and being able to predict these events may allow the system to better compensate for their occurrences. The recited capability of Harley in view of Roy is considered to teach the limitation of “determining, based on comparing the current data stream and the one or more current additional data streams to historical data trends in the historical data set related to instances where historical measured glucose values converted from historical data streams from the continuous glucose sensor indicated , that a future time frame will result in future measured glucose values to be converted from a future data stream acquired from the continuous glucose sensor that will be reflective of future actual glucose concentrations of the user” because current data of Harley is compared to the learned patterns of Roy and the allows the system to predict if there is an upcoming event which will require compensating the measured glucose values, such as exercise, or if no such event is upcoming and thus no such compensation is anticipated to be required.
Harley in view of Roy fails to further teach the system comprising: in response to determining that the future measured glucose values will be reflective of the future actual glucose concentrations, schedule a calibration operation to be performed during the future time frame.
Garcia teaches that continuous glucose sensors may be self-calibrated based on steady-state readings (Paragraphs 0011 0018-0019 and 0024).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the self-calibration methods taught by Garcia into the system of Harley in view of Roy because such self-calibration measures may help improve sensor accuracy and negate drift. Furthermore, it would be obvious to one of ordinary skill in the art to configure the system of Harley in view of Roy further in view of Garcia to implement the steady state self-calibration during periods where the glucose sensor data is not being compensated. Such a configuration would be obvious because the compensation operations of Harley such as those described in paragraphs 460-0463 of Harley are implemented in response to events that alter the accuracy of the measured glucose values. Additionally, the events during which the glucose values are compensated are transitory and are thus unlikely to extend for a required duration to reach a steady state value and/or are events which themselves affect the glucose concentration, such as exercise, which preclude the event from reaching a steady state of glucose concentration. Thus it would seem that periods where the glucose value of Harley is being compensated are ill suited to reach steady-state levels required for the self-calibration of Garcia. As such, it would be obvious to configure the system to schedule or perform self-calibration during periods where no compensation is occurring or is anticipated to occur in the near future as determined by the behavior patterns of Harley in view of Roy.
Claim 49 is rejected under 35 U.S.C. 103 as being unpatentable over Harley-Trochimczyk US Patent Application Publication Number US 2019/0223766 A1 hereinafter Harley as applied to claim 19 above and further in view of Yu US Patent Application Publication Number US 2020/0337610 A1 hereinafter Yu.
Regarding claim 49, Harley discloses the glucose sensor system of claim 19. Harley fails to further disclose the system wherein each of the one or more adjunct sensors and the continuous glucose sensor are positioned on the user within a same area defined by a radius that is 2 cm or less.
Yu teaches a non-invasive glucose monitoring apparatus comprises at least one microstrip transmission line (MLIN) component comprising: a microstrip conductor that is arranged relative to a ground plane such that a body part of a user, such as a finger or wrist, is receivable in a space defined between the microstrip conductor and the ground plane, the microstrip transmission line component having an input port; a signal input component for transmitting an input signal to the input port; and a concentration determining component configured to: determine at least one parameter of an output signal of the microstrip transmission line component; and determine, based on a comparison of the at least one parameter to at least one respective calibration curve, a glucose concentration of the user (Abstract). Thus, Yu falls within the same field of endeavor as Applicant’s invention.
Yu teaches a system wherein each of the one or more adjunct sensors and the continuous glucose sensor are all positioned on the user within a same area defined by a radius R, where radius R is 2 cm or less (Paragraph 0084)
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Harley to incorporate the teachings of Yu by configuring the one or more adjunct sensors and the continuous glucose sensor to be positioned on the user within a same area defined by a radius R, where radius R is 2 cm or less because such a sensor configuration would result in a compact system that would be more user friendly.
Claim 50 is rejected under 35 U.S.C. 103 as being unpatentable over Harley-Trochimczyk US Patent Application Publication Number US 2019/0223766 A1 hereinafter Harley as applied to claim 19 above and further in view of Mohammed US Patent Application Publication Number US 2021/0050089 A1 hereinafter Mohammed.
Regarding claim 50, Harley discloses the glucose sensor system of claim 19. Harley fails to further disclose the system wherein retrieving the one or more current additional data streams from the one or more adjunct sensors comprises: retrieving the one or more current additional data streams from the one or more adjunct sensors at intervals of between 10-20 seconds.
Mohammed teaches a patient health management platform accesses a metabolic profile for a patient and bio signals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform receives patient data recorded during the current time period comprising food items consumed, medications taken, and symptoms experienced by the patient. The platform implements a machine-learned metabolic model to determine a metabolic state of the patient at a conclusion of the current time period by comparing a true representation of the metabolic state and a prediction of the metabolic state. The true representation and the prediction are determined based on the recorded bio signals and the recorded patient data, respectively. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve their metabolic state and provides the patient-specific treatment recommendation to the patient device for display to the patient (Abstract). Thus, Mohammed falls within the same field of endeavor as Applicant’s invention.
Mohammed teaches a system including retrieving the one or more current additional data streams from the one or more adjunct sensors comprises: retrieving the one or more current additional data streams from the one or more adjunct sensors at intervals of between 10-20 seconds. (Paragraph 0055)
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Harley to incorporate the teachings of Mohammed by adding retrieving data from the one or more adjunct sensors at intervals of between 10-20 seconds. Such an update time may be beneficial by reducing computational requirements required for real-time sensor monitoring while still providing physiological measurements with an acceptable time resolution to record physiological events.
Claims 70-72 are rejected under 35 U.S.C. 103 as being unpatentable over Harley-Trochimczyk US Patent Application Publication Number US 2019/0223766 A1 hereinafter Harley as applied to claim 19 above and further in view of Duke US Patent Application Publication Number US 2015/0273147 A1 hereinafter Duke.
Regarding claim 70, Harley teaches the glucose sensor system of claim 20. Harley fails to further teach the system wherein: the operations comprises: causing the display to present an indication of a confidence level of the corrected glucose values.
Duke teaches a system and method for determining a basal rate adjustment based on risk associated with a glucose state of a person with diabetes. A method may include detecting a glucose state of the person based on a received glucose measurement signal and determining a current risk metric associated with the detected glucose state. The method may include identifying a reference glucose state and a reference risk metric associated with the reference glucose state, and calculating an adjustment to a basal rate of a therapy delivery device based on the current risk metric associated with the detected glucose state and the reference risk metric associated with the reference glucose level (Abstract). Thus, Duke falls within the same field of endeavor as Applicant’s invention.
Duke teaches a system which: calculates a confidence level of estimated glucose values (Paragraph 0048: the calculation of a probability of accuracy). Duke further teaches the display of a risk value calculated from the probability of glucose state accuracy (Paragraph 0050). An obvious variation of Duke would be to display the probability of sensor accuracy value in addition to the risk value. Such a variation would be obvious because the display of different data types may be configured based on a particular use case or user preference. It would thus be obvious to alter the display to present any raw, intermediate, and/or final calculated measure based on the user’s preference and particular use case.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to incorporate the uncertainty measure of Duke into the system of Harley because the confidence level calculation and display thereof of the obvious variation of Duke would allow the system of Harley to communicate the confidence of any presented glucose value to the user. Such a confidence metric may prompt the user to take additional action such as manually confirming a presented measure with a separate glucose measurement prior to insulin injection or performing an activity.
Regarding claim 71, Harley discloses the glucose sensor system of claim 19. Harley further discloses the system, wherein: in response to detecting that the event has not occurred, activating the insulin pump as a function of the current measured glucose values (Paragraph 0353: the standard conversion of raw or filtered data into calibrated data; Paragraph 0466: detect that an event, or lack thereof, does not warrant compensation. This limitation is further considered to be inherently disclosed by the detection of events where compensation is required. All other time points are considered a “detection” of no event occurring; Paragraphs 0417, 0476, and 0487: activating the insulin pump. The activation may be based at least in part on the compensated values which also indicates that the pump may be activated without using compensated values ) wherein in response to detecting that the event has occurred, activating the insulin pump as a function of the corrected glucose values (Paragraph 0357: the temperature signals and one or more additional signals may be used to determine a specific sensitivity value or may determine a compensated glucose value. The system may apply a look-up table or algorithm to the raw glucose data to produce a compensated glucose level; Paragraphs 0456-0460 and 0463: the model selected or modified based on the detected pattern or state; Paragraphs 0417, 0476, and 0487: activating the insulin pump. The activation may be based at least in part on the compensated values). Harvey further teaches the detection of hypo and hyper glycemic events (Paragraph 0373) but does not teach thresholds being used to actuate the pump.
Harley fails to further disclose the system comprising: activating the insulin pump as a function of a first glucose concentration threshold and the current measured glucose values; and activating the insulin pump as a function of a second glucose concentration threshold and the current compensated glucose values.
Duke teaches a system which: calculates an adjustment to the basal rate of delivery based on a risk of a first and second glycemic state. Duke teaches that the pump may be actuated in response to a risk of approaching an upper or lower threshold (Paragraphs 0073-0075). Thus Duke teaches that a pump may be activated as a function of o a first and second threshold and the measured and/or compensated glucose values.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to incorporate the basal adjustment function of Duke into the system of Harley because such an adjustment function would prevent the system from making sudden, dramatic changes in insulin delivery rates which may cause rapid changes in blood glucose levels that may be detrimental to the patient. The adjustment function allows the system to smoothly adapt the insulin basal rate to the fluctuating glucose levels.
Regarding claim 72, Harley in view of Duke teaches the glucose sensor system of claim 71. Modified Harley fails to further disclose the system, wherein: the second glucose concentration threshold is lower than the first glucose concentration threshold.
Duke teaches a first and second threshold associated with a first and second reference glucose state (Paragraph 0075). The first reference state threshold of Duke may be considered a “second glucose concentration threshold” and the second reference state threshold of Duke may be considered a “first glucose concentration threshold” the numbering of the thresholds are arbitrary.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to incorporate the first and second thresholds of Duke into the system of Harley in view of Duke where one is a minimum and one is a maximum threshold because defining the minimum and maximum desirable bounds of the patient’s glucose allows the system to adaptively adjust the basal insulin rate to maintain the patient’s glucose in the desired range. Thus one threshold will be higher than the other to define the desired range of glucose concentrations.
Response to Arguments
Applicant's arguments filed 08/11/2025 have been fully considered but they are not persuasive.
In regards to the rejections previously presented under 35 USC 112(a):
Applicant argues that the specification supports the claimed initiation of a compensation operation and cites paragraph 0138 which recites that the learning module is trained to deduce patterns in data and predict circumstances when the CGM sensor has become unreliable. Applicant further cites that such algorithms can be trained with user-specific data in paragraphs 0070, 0080, 0104, and 0111 and that the historic data includes the adjunct sensor data as per paragraph 0113. Applicant asserts that the specification provides numerous examples of learned patterns in paragraphs 0114-0121 and that the correction factor module necessarily generates different correction factors to modify a transfer function to produce the compensated glucose values as per paragraphs 0140 and 0142.
Applicant’s arguments have been fully considered but are not found to be persuasive because the claimed functions are only described in functional language in the specification. The particular steps taken to achieve the recited outcomes are not provided. Rather the specification relies upon the learning module and constituents thereof to perform each of the recites functions. While such a machine learning module may be created by a person of ordinary skill in the art and trained using the recited training date, this is insufficient to satisfy MPEP 2161.01. The specification must describe the particular algorithm or steps taken to achieve the recited function. The specification does not provide these required steps. Furthermore, the specification does not explicitly describe the steps taken to produce the machine learning model which could carry out these functions. The specification paragraph 0080 provides a list of input data and paragraph 0138 recites that the model is trained. These broad statements of functionality are not considered sufficient. The specific structure of the model utilized and details about how it was trained to perform the recited function are not provided. Thus the specification is considered to lack sufficient support for the claimed functions of the learning module.
The machine learning algorithm is described as a “black box” algorithm into which historical data is fed and out of which pattern recognition, correction factors and confidence level are received. The particular steps taken train the model and a description of its structure have not been disclosed. The particular steps taken to determine the correction factors for the variety of adjunct sensor situations have not been disclosed. The particular steps taken to determine the level of confidence that corresponds to a corrected glucose value have not been disclosed.
In regards to the rejections previously presented under 35 USC 103:
Applicant’s arguments with respect to claim 19 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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/MATTHEW ERIC OGLES/Examiner, Art Unit 3791 /JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791