DETAILED ACTION
Status of Claims
This is the first office action on the merits in response to the application filed on 19 February 2025.
Claims 1-15 were canceled and claims 16-35 were added in a preliminary amendment filed 19 February 2025.
Claim(s) 16-35 are currently pending and have been examined.
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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on 19 February 2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
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.
Claim(s) 22 is/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. Claims not listed below are rejected for dependency.
Claim 22 recites wherein the selected metrics include. There is no reference to either “selected metrics” or “metrics” in either claim 22 or the claim it depends upon. Due to the presence of this limitation further narrowing an element not included in the claim, one of ordinary skill in the art would not be able to determine the boundaries of the claim, rendering the claim indefinite.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 16-35 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 16, which is representative of claims 20 and 24, recites in part, a method for predicting a status of an infusion site, the method comprising:
applying,
operating,
based on the output, determining that the infusion site has failed or is likely to have failed.
The preceding recitation of the claim has had strikethroughs applied to the additional elements beyond the abstract idea to more clearly demonstrate the limitations setting forth the abstract idea. The remaining limitations describe a concept of evaluating data to predict whether an infusion site has failed. This concept describes a mental process that a care provider should follow to monitor an insulin pump similar to the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in the methods of organizing human activity sub-grouping. As such, these limitation set forth a method of organizing human activity. Therefore the claims are determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claim 16 recites the additional element of an electronic controller. Claim 20 recites the additional element of a non-transitory computer-readable medium. Claim 24 recites the additional element of a controller including a processor and memory. These additional elements are all recited at an extremely high level of generality and are interpreted as generic computing devices used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not integrate the abstract idea into a practical application. The claims further recite the additional element of a trained machine learning model. At the level of generality claimed, this additional element amounts to instructions to implement the abstract idea with a computing device. As previously noted, such additional elements do not integrate an abstract idea into a practical application. There are no further additional elements. When considered as a combination, the additional elements amount to instructions to implement the abstract idea with a computing device. As such the combination of additional elements does not integrate the abstract idea into a practical application. Because the additional elements, individually and as a combination, do not integrate the claims into a practical application the claims are determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea.
As previously noted, the claims recite additional elements that amount to instructions to implement the abstract idea with generic computing devices. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not amount to significantly more. There are no further additional elements. When considered as a combination, the additional elements continue to amount to instructions to implement the abstract idea with generic computing devices. As such, the combination of additional elements does not amount to significantly more. Therefore, when considered individually and as a combination, the additional elements of the independent claims do not amount to significantly more than the abstract idea. Thus the independent claims are not patent eligible.
Dependent claims 17-19, 21-23, and 25-33 further describe the above identified abstract idea, but the claims continue to recite an abstract idea, albeit a narrowed one. Dependent claim 19 also recites calculating a linear regression. This limitation describes a mathematical concept. Thus claim 19 set forth concepts that fall within different groupings of abstract ideas. As such, per MPEP 2106.04(II)(B), these concepts are considered together as a single abstract idea for further analysis. Dependent claims 18, 19, 21, 22, 25, 27-30, 32 and 33 recite no further additional elements. The previously identified additional elements, individually and as a combination, continue to fail to integrate the narrowed abstract idea into a practical application for the same reasons as provided above. As such these claims are determined to be directed to an abstract idea. Further, the previously identified additional elements, individually and as a combination, continue to fail to amount to significantly more than the narrowed abstract idea for the same reasons as provided above. Claims 17 and 26 recites the additional element of a graphical user interface. At the level of generality claimed, this additional element amounts to data output for the abstract idea. As such, this additional element is considered insignificant extra-solution activity and does not integrate the narrowed abstract idea into a practical application. When considered in combination with the previously identified additional elements, the additional elements only generally link the abstract idea and insignificant extra-solution to a technological environment involving a computing device. As such the additional elements do not integrate the narrowed abstract idea into a practical application, and the claim is determined to be directed to an abstract idea. Further, as the additional elements only generally link the abstract idea and insignificant extra-solution to a technological environment involving a computing device, the additional elements do not amount to significantly more than the narrowed abstract idea. Claims 23 and 31 recites the additional element of retraining the machine learning model. However, this additional element also amounts to instructions to implement the narrowed abstract idea with a computing device. Claim 34 recites the additional element of a medication delivery device. At the level of generality claimed, this additional element amounts to necessary data gathering for the abstract idea which requires use of insulin delivery data. As such, this additional element is considered insignificant extra-solution activity and does not integrate the narrowed abstract idea into a practical application. When considered in combination with the previously identified additional elements, the additional elements only generally link the abstract idea and insignificant extra-solution to a technological environment involving a computing device. As such the additional elements do not integrate the narrowed abstract idea into a practical application, and the claim is determined to be directed to an abstract idea. Further, as the additional elements only generally link the abstract idea and insignificant extra-solution to a technological environment involving a computing device, the additional elements do not amount to significantly more than the narrowed abstract idea. Claim 35 recites the additional element of a glucose measurement device. At the level of generality claimed, this additional element amounts to necessary data gathering for the abstract idea which requires use of insulin delivery data. As such, this additional element is considered insignificant extra-solution activity and does not integrate the narrowed abstract idea into a practical application. When considered in combination with the previously identified additional elements, the additional elements only generally link the abstract idea and insignificant extra-solution to a technological environment involving a computing device. As such the additional elements do not integrate the narrowed abstract idea into a practical application, and the claim is determined to be directed to an abstract idea. Further, as the additional elements only generally link the abstract idea and insignificant extra-solution to a technological environment involving a computing device, the additional elements do not amount to significantly more than the narrowed abstract idea. Because the dependent claims remain directed to an abstract idea without reciting significantly more, the dependent claims are not patent eligible.
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.
Claim(s) 16, 18-21, 23-25, 30, 31, 34, and 35 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Meneghetti et al. (Machine Learning-Based Anomaly Detection Algorithms to Alert Patients Using Sensor Augmented Pump of Infusion Site Failures).
Regarding Claim 16, 20, and 24: Meneghetti discloses a method for predicting a status of an infusion site, the method comprising: applying, by an electronic controller, a regression model to physiological glucose data and insulin delivery data to generate predictive data; operating, by the electronic controller, a trained machine learning model to process the predictive data to generate an output; and based on the output, determining that the infusion site has failed or is likely to have failed (“the incoming data are processed and numerical features describing the status of the pump user are derived. Then, the obtained features are fed to the unsupervised anomaly detection algorithm, which produces an anomaly score (AS) that measures how much the new data differs from the others previously observed. When the AS exceeds a threshold, an alert is generated to warn the patient of a possible infusion site failures.” See at least Page 4. Also: “in this work we resort to a manual feature engineering procedure, which uses data collected exclusively from the CGM sensor and the CSII pump.” See at least Page 4. Also: “a feature inspired by model-based fault detection techniques, i.e. the prediction residuals, obtained as the difference between the BG predicted by a model and the BG measured by the CGM. Therefore, prediction residuals are also used in this work as a feature. The prediction is obtained from a Kalman filter that uses autoregressive-moving-average models with exogenous inputs (armax). The prediction horizon is set at 2 hours ahead. The models are identified using the information derived from the CGM sensor and the pump log, with the MATLAB system identification toolbox.2 From the pump log, we used information on injected insulin and consumed meals (when input by the user).” See at least Page 4).
Regarding Claim 18: Meneghetti discloses the above limitations. Meneghetti further discloses wherein the physiological glucose data and insulin delivery data is aggregated over a time period beginning when the infusion site was first in use (“in the presence of a malfunctioning infusion set, we also expect to observe an increment of the average insulin infused as compared to usual, caused by the patient’s attempt to correct hyperglycemia (unsuccessfully). Therefore, it is convenient to monitor average insulin over both long and short time-windows. To capture different time scales, we defined a feature set that is composed of the moving average of the three considered signals (CGM, insulin, and prediction residuals) performed with six different time windows: 1 h, 3 h, 6 h, 12 h, 24 h and 48 h.” See at least Page 5. Also: See Figure 4).
Regarding Claim 19: Meneghetti discloses the above limitations. Meneghetti further discloses calculating a linear regression based on the physiological glucose data and insulin delivery data that is aggregated, wherein the predictive data is based, at least in part, on the linear regression (“a feature inspired by model-based fault detection techniques, i.e. the prediction residuals, obtained as the difference between the BG predicted by a model and the BG measured by the CGM. Therefore, prediction residuals are also used in this work as a feature. The prediction is obtained from a Kalman filter that uses autoregressive-moving-average models with exogenous inputs (armax). The prediction horizon is set at 2 hours ahead. The models are identified using the information derived from the CGM sensor and the pump log, with the MATLAB system identification toolbox.2 From the pump log, we used information on injected insulin and consumed meals (when input by the user).” See at least Page 4).
Regarding Claim 21 and 25: Meneghetti discloses the above limitations. Meneghetti further discloses wherein the output is a value indicating a likelihood of infusion site failure (“an anomaly score (AS) that measures how much the new data differs from the others previously observed. When the AS exceeds a threshold, an alert is generated to warn the patient of a possible infusion site failures.” See at least Page 4).
Regarding Claim 23 and 31: Meneghetti discloses the above limitations. Meneghetti further discloses wherein the trained machine learning model is customized for a patient by retraining the machine learning model using prior physiological glucose data and insulin delivery data of the patient (“The methodology relies on unsupervised anomaly detection algorithms … the method is personalized: the decision is performed based on the historical data collected during the personal use of CGM and insulin pump thus it is patient specific”. See at least Page 2. Also: “the obtained features are fed to the unsupervised anomaly detection algorithm, which produces an anomaly score (AS) that measures how much the new data differs from the others previously observed.” See at least Page 4. Also: “we tested a method for automatic detection of infusion site failures in real-time using unsupervised anomaly detection algorithms, which can detect anomalies through the analysis of patient historical data.” See at least Page 6).
Regarding Claim 30: Meneghetti discloses the above limitations. Meneghetti further discloses wherein the physiological glucose data comprises summarized data based on a raw glucose data (“To capture different time scales, we defined a feature set that is composed of the moving average of the three considered signals (CGM, insulin, and prediction residuals) performed with six different time windows: 1 h, 3 h, 6 h, 12 h, 24 h and 48 h. This procedure produces a feature set containing 6 × 3 = 18 features.” See at least Page 5).
Regarding Claim 34: Meneghetti discloses the above limitations. Meneghetti further discloses a medication delivery device configured to deliver insulin to the patient and generate the insulin delivery data (“in this work we resort to a manual feature engineering procedure, which uses data collected exclusively from the CGM sensor and the CSII pump.” See at least Page 4).
Regarding Claim 35: Meneghetti discloses the above limitations. Meneghetti further discloses a glucose measurement device in communication with the controller and configured to generate the physiological glucose data (“in this work we resort to a manual feature engineering procedure, which uses data collected exclusively from the CGM sensor and the CSII pump.” See at least Page 4).
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(s) 17 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meneghetti et al. (Machine Learning-Based Anomaly Detection Algorithms to Alert Patients Using Sensor Augmented Pump of Infusion Site Failures) in view of Estes (US 2014/0249500 A1).
Regarding Claim 17: Meneghetti discloses the above limitations. Meneghetti further discloses generating an alert signal indicating that the infusion site has failed (“When the AS exceeds a threshold, an alert is generated to warn the patient of a possible infusion site failures.” See at least Page 4). Meneghetti does not expressly disclose displaying an alert on a graphical user interface in response to the alert signal. However, Estes teaches displaying an alert on a graphical user interface in response to the alert signal (the method may include outputting an alert via the user interface of the infusion pump system in response to the infusion pump system detecting an infusion set error. See at least [0012]).
Meneghetti provides a system which detects infusion site failures and outputs an alert, upon which the claimed invention’s display of an alert on an interface can be seen as an improvement. However, Estes demonstrates that the prior art already knew of displaying infusion set errors on an interface. One of ordinary skill in the art could have trivially applied the techniques of Estes to the system of Meneghetti. Further, one of ordinary skill in the art would have recognized that such an application of Estes would have resulted in a system which would provide a patient with a visual indication of an infusion set error. As such, the application of Estes and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Meneghetti and the teachings of Estes.
Regarding Claim 26: Meneghetti discloses the above limitations. Meneghetti further discloses wherein the instructions further cause the processor to generate an alert signal indicating that the infusion site has failed when the likelihood is above a threshold (“When the AS exceeds a threshold, an alert is generated to warn the patient of a possible infusion site failures.” See at least Page 4). Meneghetti does not expressly disclose a user interface arranged to receive the alert signal and responsively generate an alert on the user interface. However, Estes teaches a user interface arranged to receive the alert signal and responsively generate an alert on the user interface (the method may include outputting an alert via the user interface of the infusion pump system in response to the infusion pump system detecting an infusion set error. See at least [0012]).
Meneghetti provides a system which detects infusion site failures and outputs an alert, upon which the claimed invention’s display of an alert on an interface can be seen as an improvement. However, Estes demonstrates that the prior art already knew of displaying infusion set errors on an interface. One of ordinary skill in the art could have trivially applied the techniques of Estes to the system of Meneghetti. Further, one of ordinary skill in the art would have recognized that such an application of Estes would have resulted in a system which would provide a patient with a visual indication of an infusion set error. As such, the application of Estes and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Meneghetti and the teachings of Estes.
Claim(s) 32 and 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meneghetti et al. (Machine Learning-Based Anomaly Detection Algorithms to Alert Patients Using Sensor Augmented Pump of Infusion Site Failures) in view of Loutseiko et al. (US 2018/0177946 A1).
Regarding Claim 32: Meneghetti discloses the above limitations. Meneghetti does not expressly disclose wherein the controller further includes a rule-based algorithm. However, Loutseiko teaches wherein the controller further includes a rule-based algorithm (the monitoring application 612 detects or otherwise identifies a site loss condition when an updated reference value calculated based on obtained sensed measurement values during a current fasting period deviates from a fasting reference value by more than a threshold amount. See at least [0075]).
Meneghetti provides a system which uses models to detects infusion site failures, upon which the claimed invention’s use of a rule to determine a site failure can be seen as an improvement. Loutseiko demonstrates that the prior art already knew of rule based determinations of an infusion site’s failure. One of ordinary skill in the art could have easily incorporated the rule based site failure detection of Loutseiko into the system of Meneghetti. Further, one of ordinary skill in the art would have recognized that such an application of Loutseiko would have resulted in a system which would use both model and rule based failure detection and would thus be more robust. As such, the application of Loutseiko and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Meneghetti and the teachings of Loutseiko.
Regarding Claim 33: Meneghetti and Loutseiko make obvious the above limitations. Additionally, Loutseiko teaches wherein the rule-based algorithm is invoked after a pre-determined period of time (the site loss detection process 900 is persistently performed once the lifetime of an infusion set exceeds a threshold amount. See at least [0082]). The motivation to combine Meneghetti and Loutseiko is the same as explained under claim 32 above, and is incorporated herein.
Non-obvious Subject Matter
Claim(s) 27-29 is/are understood to claim novel and non-obvious subject matter.
The following is a statement of reasons for the indication of novel and non-obvious subject matter:
Claim 27 recites wherein the predictive data comprises p values of selected metrics. Claim 27 depends upon claim 24 which recites apply a regression algorithm to physiological glucose data and insulin delivery data to generate predictive data and further recites operate a trained machine learning model to process the predictive data. Thus the claim generates the p values as part of a regression model. In that context, one of ordinary skill in the art would understand these p values to be metrics of statistical significance for the regression coefficients associated with the selected metrics. This interpretation is reinforced by the specification which states “processing the data using the regression algorithm includes calculating certain metrics (described herein) within consecutive one-hour time windows and then fitting a linear regression of the obtained sequence of metrics against time and calculating p values, which represent the significance of calculated time linear coefficients” ([0067]) and “In certain embodiments, the predictive data comprises regression coefficient p values“ ([0068]). The prior art does not appear to suggest using p values of regression coefficients as inputs to a trained machine learning model in either the field of insulin pump control or anomaly detection more broadly. As such, the prior art cannot reasonably make obvious the claimed invention. As such, Examiner acknowledges claim 27 and the claims depending on it to be novel and non-obvious.
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found in the PTO-892 Notice of References Cited.
Facchinetti et al. (US 2013/0231543 A1) provides additional description of GCM and insulin data based failure detection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bion A Shelden whose telephone number is (571)270-0515. The examiner can normally be reached M-F, 12pm-10pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached at (571) 272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Bion A Shelden/Primary Examiner, Art Unit 3685 2026-01-22