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 .
DETAILED ACTION
In the amendment filed June 15, 2026:
Claims 1, 13 are amended
Claims 1-20 are pending
Claim Objections
Claim 13 is objected to because of the following informalities: “And” should be added to the beginning of the last limitation of the claims. Therefore, for Claim 13, it should read “and a display configured to display, as a user interface element on the graphical user interface, the modified predicted wellness event sequence.” For Claim 13, it should read “and updating display of the modified predicted wellness event sequence as a user interface element on a graphical user interface to include a visual annotation corresponding to the predicted wellness event, wherein the visual annotation is aligned with the analyte value on the user interface element.” Appropriate correction is required.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a system and method, which are within a statutory category.
Step 2A1
The limitations of:
Claims 1, 13 (Claim 1 being representative)
classify an analyte value in the analyte data set as a predicted wellness event based on an evaluation of a first subset of analyte values prior to the analyte value and a second subset of analyte values subsequent to the analyte value;
and generate a predicted wellness event sequence comprising a plurality of predicted wellness events including the predicted wellness event, wherein each predicted wellness event of the plurality of wellness events is associated with a respective analyte value in the analyte data set;
receive, from the trained machine learning model, the predicted wellness event sequence;
identify, in the predicted wellness event sequence based on an analysis of time-ordered events within the predicted wellness event sequence, at least one of a false positive event or a false negative event in the predicted wellness event sequence;
and modify the predicted wellness event sequence based on the at least one of the false positive event or the false negative event, wherein the trained machine learning model is further configured to update display the modified predicted wellness event sequence as a user interface element on a graphical user interface to include a visual annotation corresponding to the predicted wellness event, wherein the visual annotation is aligned with the analyte value on the user interface element,
as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to predict wellness events in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “classifying, generating, receiving, identifying and modifying” as indicated supra.
Other than reciting generic computer components (discussed infra), i.e., a system implemented by a data processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional element of a guardrail component that implements the identified abstract idea. The guardrail component is not described by the applicant and is recited at a high-level of generality (i.e., a generic server performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component (see 112(f) interpretation). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim further recites the additional element of using a trained machine learning model to predict wellness events. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to predict wellness events merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (decision tree analysis, gradient boosting, adaptive boosting, artificial neural networks or variants thereof, linear discriminant analysis, nearest neighbor analysis, support vector machines, supervised or unsupervised classification, and others) and thus fails to add an inventive concept to the claims.
The claims further recite the additional elements of a graphical user interface, in vivo analyte sensor and display. The graphical user interface, in vivo analyte sensor and display merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a guardrail component to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained machine learning model to predict wellness events was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (decision tree analysis, gradient boosting, adaptive boosting, artificial neural networks or variants thereof, linear discriminant analysis, nearest neighbor analysis, support vector machines, supervised or unsupervised classification, and others). This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a graphical user interface, in vivo analyte sensor and display were determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. As such the claims are not patent eligible.
Claims 2-12,14-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim(s) 2, 14 merely describe(s) sequences containing one or more spikes within a time period, which further defines the abstract idea.
Claim(s) 3, 15 merely describe(s) the start and end times for spikes, which further defines the abstract idea.
Claim(s) 4, 16 merely describe(s) sequences containing one or more crashes within a time period, which further defines the abstract idea.
Claim(s) 5, 17 merely describe(s) the start and end times for crashes, which further defines the abstract idea.
Claim(s) 6, 18 merely describe(s) what the false positive event contains, which further defines the abstract idea.
Claim(s) 7, 19 merely describe(s) what the time-ordered events contain, which further defines the abstract idea.
Claim(s) 8, 20 merely describe(s) identifying and displaying missed events, which further defines the abstract idea.
Claim(s) 8 also includes the additional element of “a retrospective update module” which is analyzed the same as the “guardrail component” and does not provide a practical application or significantly more for the same reasons.
Claim(s) 9-11 merely describe(s) intervals, which further defines the abstract idea.
Claim(s) 12 merely describe(s) what the system is implanted on, which further defines the abstract idea.
Claim(s) 12 also includes the additional element of “a mobile device” which is analyzed the same as the “in vivo analyte sensor” and does not provide a practical application or significantly more for the same reasons.
Claim Rejections - 35 USC § 103
The Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3,5,9-15,17 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Frank et al (US Publication No. 20220354395) in view of Derdzinski et al (US Publication No. 20250344967) in view of BUDIMAN et al (Foreign Publication EP-3125761-B1) in view of Biesinger et al (US Publication No. 11464461).
Regarding Claim 1
Frank teaches a system for generating a graphical user interface comprising user interface elements representative of a subject's wellness based at least in part on analyte data received from an in vivo analyte sensor, wherein the analyte data represents data collected as an analyte data set from the in vivo analyte sensor over a predetermined period of time, the system comprising:
a trained machine learning model configured to [Frank at Para. 0031 teaches the glucose measurements collected during the observation period and/or data derived by pre-processing the glucose measurements is provided as input to the trained machine learning model]:
classify an analyte value in the analyte data set as a predicted wellness event based on an evaluation of a first subset of analyte values prior to the analyte value and a second subset of analyte values subsequent to the analyte value [Frank at Para. 0028 teaches once the machine learning model is trained, it is used to predict a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device worn by the user during an observation period spanning multiple days; Frank at Para. 0054 teaches in the illustrated example 100, the observation analysis platform 108 also includes prediction system 114. The prediction system 114 represents functionality to process the glucose measurements 110 to generate diabetes predictions, such as to predict whether the person 102 has diabetes (e.g., Type 2 diabetes, GDM, cystic fibrosis diabetes, and so on) or is at risk for developing diabetes (e.g., prediabetes), and/or whether the person 102 is predicted to experience adverse effects associated with diabetes (e.g., comorbidity, dysglycemia, macrosomia requiring a C-section, and neonatal hypoglycemia, to name just a few). As discussed in more detail below, the prediction system 114 uses machine learning to predict diabetes classifications];
a guardrail component configured to:
receive, from the trained machine learning model, the predicted wellness event sequence [Frank at Para. 0153 teaches a prediction of a diabetes classification is received as output from the machine learning model (block 1010) (diabetes classification interpreted as predicted wellness event sequence)];
Frank does not teach and generate a predicted wellness event sequence comprising a plurality of predicted wellness events including the predicted wellness event, wherein each predicted wellness event of the plurality of wellness events is associated with a respective analyte value in the analyte data set;
identify, in the predicted wellness event sequence based on an analysis of time-ordered events within the predicted wellness event sequence, at least one of a false positive event or a false negative event in the predicted wellness event sequence;
and modify the predicted wellness event sequence based on the at least one of the false positive event or the false negative event,
wherein the trained machine learning model is further configured to update display of the modified predicted wellness event sequence as a user interface element on a graphical user interface to include a visual annotation corresponding to the predicted wellness event, wherein the visual annotation is aligned with the analyte value on the user interface element.
Derdzinski teaches and generate a predicted wellness event sequence comprising a plurality of predicted wellness events including the predicted wellness event, wherein each predicted wellness event of the plurality of wellness events is associated with a respective analyte value in the analyte data set [Derdzinski at Para. 0094 teaches generally, the event prediction 414 output by the prediction manager 408 is representative of a prediction of whether a particular type of event will occur for the person during a time interval for which the glucose measurement prediction 416 is to be generated (e.g., a time interval subsequent to a time interval defined by the time sequenced glucose measurements 410). The glucose measurement prediction 416 may be representative of an output prediction generated by one of the stacked machine learning models 412 of the prediction manager 408, which in turn may be trained, or an underlying model may be learned, based on one or more training approaches and using one or more of historical glucose measurements 118, additional data 404, or output predictions generated by other ones of the stacked machine learning models 412; Derdzinski at Para. 0095 teaches the glucose measurement predictions 416 output by one of the machine learning models 412 may be provided as input to one or more of the other machine learning models 412 to generate the event prediction 414. For instance, a machine learning model 412 trained to generate an event prediction 412 may be configured to identify a pattern in the glucose measurement prediction 416 that correlates to historical information for the person 102, such as a pattern of glucose level changes that correspond to glucose level changes associated with the person 102's response to exercise, response to eating a meal, response to stress, response to insulin administration, response to sleep, combinations thereof, and so forth];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the classifications of Frank with the predictions of Derdzinski with the motivation to improve an accuracy of glucose measurement predictions.
Frank/Derdzinski does not teach identify, in the predicted wellness event sequence based on an analysis of time-ordered events within the predicted wellness event sequence, at least one of a false positive event or a false negative event in the predicted wellness event sequence;
and modify the predicted wellness event sequence based on the at least one of the false positive event or the false negative event,
wherein the trained machine learning model is further configured to update display of the modified predicted wellness event sequence as a user interface element on a graphical user interface to include a visual annotation corresponding to the predicted wellness event, wherein the visual annotation is aligned with the analyte value on the user interface element.
BUDIMAN teaches identify, in the predicted wellness event sequence based on an analysis of time-ordered events within the predicted wellness event sequence, at least one of a false positive event or a false negative event in the predicted wellness event sequence [BUDIMAN at Para. 0028 teaches referring back to FIG. 1, after the determination of local optima of acceleration (130), data analysis continues to identify and remove false meal start and peak candidates. In a first stage of analysis and removal, adjacent candidates of the same type are removed (140). That is, since a meal start event cannot be adjacent in time to another meal start event, and similarly, a peak meal response event cannot be adjacent in time to another peak meal response event, during the first stage of analysis and removal, adjacent candidates of the same type are identified and removed from the data set under consideration];
and modify the predicted wellness event sequence based on the at least one of the false positive event or the false negative event [BUDIMAN at Para. 0028],
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Frank, Derdzinski with the false events identification of BUDIMAN with the motivation to improve the reliability of existing start-of-meal markers manually entered by the user.
Frank/Derdzinski/BUDIMAN does not teach wherein the trained machine learning model is further configured to update display of the modified predicted wellness event sequence as a user interface element on a graphical user interface to include a visual annotation corresponding to the predicted wellness event, wherein the visual annotation is aligned with the analyte value on the user interface element.
Biesinger teaches wherein the trained machine learning model is further configured to update display of the modified predicted wellness event sequence as a user interface element on a graphical user interface to include a visual annotation corresponding to the predicted wellness event, wherein the visual annotation is aligned with the analyte value on the user interface element [Biesinger at Para. 22 teaches the diabetes management platform can classify the excursion as being indicative of a glycemic event. Moreover, the diabetes management platform may identify an appropriate annotation based on the glycemic event. Said another way, the diabetes management platform may identify the annotation based on the signal feature detected in the physiological data. In some embodiments, the diabetes management platform causes display of the physiological data as a glucose trace over time to an interface generated by a computer program. In such embodiments, the diabetes management platform can cause display of the annotation on the interface. The glucose trace and the annotation can be aligned with respect to a common time axis, thereby visually alerting a viewer of potential changes in the glycemic condition (also referred to as the “glycemic health state”) of the subject].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Frank, Derdzinski, BUDIMAN with the display of Biesinger with the motivation to improve the glycemic health state of the subject.
Regarding Claim 2
Frank/Derdzinski/BUDIMAN/Biesinger teach the system of claim 1,
Frank/Derdzinski/BUDIMAN/Biesinger further teach wherein the predicted wellness event sequence comprises one or more predicted glucose spikes within the predetermined period of time [BUDIMAN at Para. 0028 (see Claim 1 for explanation)].
Regarding Claim 3
Frank/Derdzinski/BUDIMAN/Biesinger teach the system of claim 2,
Frank/Derdzinski/BUDIMAN/Biesinger further teach wherein the predicted wellness event sequence comprises spike data including one or more predicted spike start times and one or more predicted spike end times within the predetermined period of time [Derdzinski at Para. 0096 teaches each machine learning model 412 implemented in the stacked configuration by prediction manager 408 may be implemented in a variety of different ways without departing from the spirit or scope of the described techniques. Each machine learning model 412, for instance, may receive as input labeled streams of observed glucose values collected over an interval of time to produce an anticipated output. The streams of estimated glucose values are labeled to indicate whether or not a particular event occurred during the particular interval of time, along with timestamps defining a start and end of the particular event, as well as glucose levels and changes thereof preceding the particular event, during the particular event, and following the particular event].
Regarding Claim 5
Frank/Derdzinski/BUDIMAN/Biesinger/Fox teach the system of claim 4,
Frank/Derdzinski/BUDIMAN/Biesinger/Fox further teach wherein the predicted wellness event sequence comprises crash data including one or more predicted crash start times and one or more predicted crash spike end times [Derdzinski at Para. 0099 (see Claim 3 for explanation)].
Regarding Claim 9
Frank/Derdzinski/BUDIMAN/Biesinger teach the system of claim 1,
Frank/Derdzinski/BUDIMAN/Biesinger further teach wherein the predetermined period time comprises a 5 minute interval [Frank at Para. 0041 teaches as used herein, the term “continuous” used in connection with glucose monitoring may refer to an ability of a device to produce measurements substantially continuously, such that the device may be configured to produce the glucose measurements 110 at intervals of time (e.g., every hour, every 30 minutes, every 5 minutes, and so forth), responsive to establishing a communicative coupling with a different device (e.g., when a computing device establishes a wireless connection with the wearable glucose monitoring device 104 to retrieve one or more of the measurements), and so forth].
Regarding Claim 10
Frank/Derdzinski/BUDIMAN/Biesinger teach the system of claim 1,
Frank/Derdzinski/BUDIMAN/Biesinger further teach wherein the predetermined period of time comprises a 1 minute interval [Derdzinski at Para. 0041 teaches Alternatively or additionally, the CGM system 104 may communicate the glucose measurements 118 to the computing device 108 at designated intervals (e.g., every 30 seconds, every minute, every 5 minutes, every hour, every 6 hours, every day, and so forth)].
Regarding Claim 11
Frank/Derdzinski/BUDIMAN/Biesinger teach the system of claim 1,
Frank/Derdzinski/BUDIMAN/Biesinger further teach wherein the predetermined period of time comprises a 24 hour interval [Derdzinski at Para. 0043 (see Claim 10 for explanation)].
Regarding Claim 12
Frank/Derdzinski/BUDIMAN/Biesinger teach the system of claim 1,
Frank/Derdzinski/BUDIMAN/Biesinger further teach wherein the system is implemented as a mobile device [Frank at Para. 0051 teaches Alternately or additionally, provision of the glucose measurements 110 to the observation analysis platform 108 may involve the wearable glucose monitoring device 104 communicating the glucose measurements 110 over one or more wireless connections. For example, the wearable glucose monitoring device 104 may wirelessly communicate the glucose measurements 110 to external computing devices, such as a mobile phone, tablet device, laptop, smart watch, other wearable health tracker, and so on].
Regarding Claim 13
Frank teaches a method generating a graphical user interface comprising user interface elements representative of a subject's wellness based at least in part on analyte data received from an in vivo analyte sensor, wherein the analyte data represents data collected as an analyte data set from the in vivo analyte sensor over a predetermined period of time, the method comprising:
classifying an analyte value in the analyte data set as a predicted wellness event based on an evaluation of a first subset of analyte values prior to the analyte value and a second subset of analyte values subsequent to the analyte value [Frank at Para. 0028, 0054 (see Claim 1 for explanation)];
receiving, from the trained machine learning model, the predicted wellness event sequence [Frank at Para. 0153 (see Claim 1 for explanation)];
Frank does not teach generating, by the trained machine learning model a predicted wellness event sequence comprising a plurality of predicted wellness events including the predicted wellness event, wherein each predicted wellness event of the plurality of wellness events is associated with a respective analyte value in the analyte data set;
identifying, in the predicted wellness event sequence based on an analysis of time-ordered events within the predicted wellness event sequence, at least one of a false positive event or a false negative event in the predicted wellness event sequence;
and modifying the predicted wellness event sequence based on the at least one of the false positive event or the false negative event;
updating display of the modified predicted wellness event sequence as a user interface element on a graphical user interface to include a visual annotation corresponding to the predicted wellness event, wherein the visual annotation is aligned with the analyte value on the user interface element.
Derdzinski teaches generating, by the trained machine learning model a predicted wellness event sequence comprising a plurality of predicted wellness events including the predicted wellness event, wherein each predicted wellness event of the plurality of wellness events is associated with a respective analyte value in the analyte data set [Derdzinski at Para. 0094, 0095 (see Claim 1 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the classifications of Frank with the predictions of Derdzinski with the motivation to improve an accuracy of glucose measurement predictions.
Frank/Derdzinski does not teach identifying, in the predicted wellness event sequence based on an analysis of time-ordered events within the predicted wellness event sequence, at least one of a false positive event or a false negative event in the predicted wellness event sequence;
and modifying the predicted wellness event sequence based on the at least one of the false positive event or the false negative event;
updating display of the modified predicted wellness event sequence as a user interface element on a graphical user interface to include a visual annotation corresponding to the predicted wellness event, wherein the visual annotation is aligned with the analyte value on the user interface element.
BUDIMAN teaches identifying, in the predicted wellness event sequence based on an analysis of time-ordered events within the predicted wellness event sequence, at least one of a false positive event or a false negative event in the predicted wellness event sequence [BUDIMAN at Para. 0028 (see Claim 1 for explanation)];
and modifying the predicted wellness event sequence based on the at least one of the false positive event or the false negative event [BUDIMAN at Para. 0028 (see Claim 1 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Frank, Derdzinski with the false events identification of BUDIMAN with the motivation to improve the reliability of existing start-of-meal markers manually entered by the user.
Frank/Derdzinski/BUDIMAN does not teach updating display of the modified predicted wellness event sequence as a user interface element on a graphical user interface to include a visual annotation corresponding to the predicted wellness event, wherein the visual annotation is aligned with the analyte value on the user interface element.
Biesinger teaches updating display of the modified predicted wellness event sequence as a user interface element on a graphical user interface to include a visual annotation corresponding to the predicted wellness event, wherein the visual annotation is aligned with the analyte value on the user interface element [Biesinger at Para. 22 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Frank, Derdzinski, BUDIMAN with the display of Biesinger with the motivation to improve the glycemic health state of the subject.
Regarding Claim 14
Claim(s) 14 is/are analogous to Claim(s) 2, thus Claim(s) 14 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
Regarding Claim 15
Claim(s) 15 is/are analogous to Claim(s) 3, thus Claim(s) 15 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
Regarding Claim 17
Claim(s) 17 is/are analogous to Claim(s) 5, thus Claim(s) 17 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 5.
Claims 4,16 are rejected under 35 U.S.C. 103(a) as being unpatentable over Frank, Derdzinski, BUDIMAN, Biesinger as applied to claims 1, 13 above, and further in view of Fox et al (US Publication No. 20150190100).
Regarding Claim 4
Frank/Derdzinski/BUDIMAN/Biesinger teach the system of claim 1,
Frank/Derdzinski/BUDIMAN/Biesinger do not teach wherein the predicted wellness event sequence comprises one or more predicted glucose crashes within the predetermined period of time.
Fox teaches wherein the predicted wellness event sequence comprises one or more predicted glucose crashes within the predetermined period of time [Fox at Para. 0056 teaches in one embodiment of the invention, a monitor anticipates a glucose crash by monitoring trends in glucose levels; Fox at Para. 0058 teaches in an illustrative embodiment, the monitor periodically measures glucose, analyzes the present trend, determines whether a glucose crash incident is probable and appropriately alerts the patient. At some frequent interval (e.g., but not limited to, once per minute), the device measures the glucose level, applies a smoothing filter to the result, and records the filtered value].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Frank, Derdzinski, BUDIMAN, Biesinger with the glucose crash prediction of Fox with the motivation to improve the user's condition.
Regarding Claim 16
Claim(s) 16 is/are analogous to Claim(s) 4, thus Claim(s) 16 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 4.
Claims 6-8,18-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Frank, Derdzinski, BUDIMAN, Biesinger as applied to claims 1, 13 above, and further in view of Pickus et al (US Publication No. 20220165432).
Regarding Claim 6
Frank/Derdzinski/BUDIMAN/Biesinger teach the system of claim 1,
Frank/Derdzinski/BUDIMAN/Biesinger further teach wherein the false positive event comprises a false glucose spike or a false glucose crash [BUDIMAN at Para. 0028 (see Claim 1 for explanation; interpreted as false glucose spike] … [ … ]
Frank/Derdzinski/BUDIMAN/Biesinger do not teach [ … ] … and the false negative event comprise a missed glucose spike or a missed glucose crash.
Pickus teaches [ … ] … and the false negative event comprise a missed glucose spike or a missed glucose crash [Pickus at Para. 0018 teaches an event engine of the glucose monitoring application is configured to process the glucose measurements to generate events associated with glucose monitoring, such as glycemic events (e.g., hyperglycemia and hypoglycemia), predicted glycemic events (e.g., upcoming low glucose or upcoming high glucose), and so on; Pickus at Para. 0021 teaches thus, to solve this problem of conventional systems, missing events that are missing from the event records during a first time period are identified by processing the glucose measurements using an event engine simulator that is a replication of the event engine].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Frank, Derdzinski, BUDIMAN, Biesinger with the display of Pickus with the motivation to improve accuracy in identifying or predicting glucose-based events.
Regarding Claim 7
Frank/Derdzinski/BUDIMAN/Biesinger/Pickus teach the system of claim 6,
Frank/Derdzinski/BUDIMAN/Biesinger/Pickus further teach wherein the analysis perform of the time-ordered events comprises at least one of: identify a false glucose spike predicted by the trained machine learning model; identify a false glucose crash predicted by the trained machine learning model; identify the missed glucose spike not predicted by the trained machine learning model; or identify the missed glucose crash not predicted by the trained machine learning model [BUDIMAN at Para. 0028 (see Claim 1 for explanation; interpreted as false glucose spike; interpreted to combine with machine learning model of Frank)].
Regarding Claim 8
Frank/Derdzinski/BUDIMAN/Biesinger teach the system of claim 1,
Frank/Derdzinski/BUDIMAN/Biesinger do not teach the system further comprising:
a retrospective update module configured to identify, based on the analyte data, a missed predicted wellness event in the modified predicted wellness sequence;
and wherein the display is further configured to display, as a second user interface element on the graphical user interface element, the missed predicted wellness event, and wherein the missed predicted wellness event is displayed concurrently with the modified predicted wellness sequence.
Pickus teaches the system further comprising:
a retrospective update module configured to identify, based on the analyte data, a missed predicted wellness event in the modified predicted wellness sequence [Pickus at Para. 0093 teaches missing events that are missing from the event records are identified during the first time period by processing the glucose measurements using an event engine simulator (block 604)];
and wherein the display is further configured to display, as a second user interface element on the graphical user interface element, the missed predicted wellness event, and wherein the missed predicted wellness event is displayed concurrently with the modified predicted wellness sequence [Pickus at Para. 0081 teaches FIG. 4 depicts an example 400 of an implementation of a user interface displaying a plot of observed behavior associated with a computing environment over time and visualization of a range within which the observed behavior is not anomalous; Pickus at Para. 0083 teaches Here, the user interface 404 includes a graph 406 that plots indications of the first and second missing events 314, 316 over time. In particular, the graph 406 plots a number of missing events per day as indicated by the first and second missing events 314, 316].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Frank, Derdzinski, BUDIMAN, Biesinger with the display of Pickus with the motivation to improve accuracy in identifying or predicting glucose-based events.
Regarding Claim 18
Claim(s) 18 is/are analogous to Claim(s) 6, thus Claim(s) 18 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 6.
Regarding Claim 19
Claim(s) 19 is/are analogous to Claim(s) 7, thus Claim(s) 19 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7.
Regarding Claim 20
Claim(s) 20 is/are analogous to Claim(s) 8, thus Claim(s) 20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 8.
Response to Arguments
Claim Interpretation
Regarding the Interpretation(s) to Claims 1, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
The Office Action asserts that certain claim terms, including "guardrail component," are subject to interpretation under 35 U.S.C. § 112(f). Applicant respectfully disagrees. The recited terms convey sufficient structure to a person of ordinary skill in the art (POSITA) and do not invoke means-plus-function treatment. As such, § 112(f) is inapplicable.
Regarding (a), the Examiner respectfully disagrees. The Examiner has invoked 112(f) because “guardrail” appears to be a nonce term that performs a function. In performing the 112(f) interpretation the Examiner determined that a “guardrail component“ is tied to a computer in the Specification and thus the Specification describes sufficient structure for the “guardrail component” and no 112(a) or 112(b) issues exists. This is consistent with Applicant’s arguments in which the “guardrail component” is asserted to be software running on a computer. The Examiner is unsure why the Applicant is arguing this analysis.
Claim Objections
Regarding the objection(s) to Claims 1, the Applicant has amended the claims to overcome the basis/bases of objection. The issue is still present in Claim 13 and thus the objection is maintained.
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
The specification confirms that the claimed limitations reflect a specific mode of operation of the machine learning model and system, consistent with an improvement in how such systems process and present time-series analyte data. See, e.g., 102-103.
Regarding (a), the Examiner respectfully disagrees. The cited Paragraphs do not improve the model. They merely recite the data used in the model and the model output, which are not improving the model. The model is instead performing its normal functions. See also MPEP 2106.05(a)(I). The Recentive Analytics, Inc. v. Fox Corp. decision is directed to an ineligibility analysis rather than an eligibility test. Recentive held that non-specifically claimed training of an ML algorithm is insufficient to provide a practical application or significantly more because it does not result in “improving the mathematical algorithm or making machine learning better.” Recentive at 12. The decision further instructed that “[i]terative training using selected training material…are incident to the very nature of machine learning” and thus does not provide for an improvement. Recentive at 12.
These are concrete operational constraints to the claimed machine learning model and are analogous to Board findings that claims are eligible where they recite specific predictive model operations that "go to the heart of how the machine learning model itself operates." Ex parte Bush, Appeal 2025-002376, pg. 6 (November 17, 2025).
Regarding (b), the Examiner respectfully disagrees. The Examiner respectfully submits the cited non-precedential PTAB decisions are specific to the facts before the panel, have not be subject to appellate review, and are not binding on the present rejection. As such, the Examiner declines to address them. A list of precedential and/or informative PTAB decisions that the Applicant may rely upon are listed at the following website: https://www.uspto.gov/patents/ptab/precedential-informative-decisions
As amended, claims 1 and 13 should be found eligible for similar reasons as those articulated by the Bush Board. Here, the claims do not merely use a generic model to label wellness events. Instead, the claims recite steps that reflect how the machine learning model itself operates.
Regarding (c), the Examiner respectfully declines to answer as it involves non-precedential PTAB decisions as preciously mentioned in (b).
Regarding (1), the machine learning model classifies "an analyte value in the analyte data set as a predicted wellness event based on an evaluation of a first subset of analyte values prior to the analyte value and a second subset of analyte values subsequent to the analyte value." This results in the claimed machine learning model having an improved ability to perform classification, rather than merely applying a generic machine learning technique.
Regarding (d), the Examiner respectfully disagrees. This does not improve the function of the machine learning model, as previously mentioned in (a). Training a machine learning model on specific data does not result in an improved machine learning model.
Regarding (2), the machine learning model "generat[es] a predicted wellness event sequence comprising a plurality of predicted wellness events including the predicted wellness event, wherein each predicted wellness event of the plurality of wellness events is associated with a respective analyte value in the analyte data set." This reflects the improvement to the functionality of the machine learning model with regard to how it generates the particular wellness event sequence
Regarding (e), the Examiner respectfully disagrees. This doesn’t improve the model. The model is just performing its normal functions.
Regarding (3), the machine learning model "update[s] display of the modified predicted wellness event sequence as a user interface element on a graphical user interface to include a visual annotation corresponding to the predicted wellness event, wherein the visual annotation is aligned with the current prediction time on the user interface element." This too goes to the heart of how the machine learning model itself operates and is not a generic machine learning step.
Regarding (f), the Examiner respectfully disagrees. This does not improve the functioning of the machine learning model. This is merely using a trained machine learning model to perform its normal functions, which include data gathering and data processing and displaying of data.
The amended claims are also eligible under Step 2B because they recite significantly more, including a non-conventional arrangement of components that result in improved system performance.
Regarding (g), the Examiner respectfully disagrees. There’s no non-convention arrangement present in the claims. Everything is located on the computer; there are no other locations claimed. The Examiner submits that there is nothing unconventional about performing data analysis using a machine learning model on a computer and then performing additional data analysis on the computer (the only location claimed).
Rejection under 35 U.S.C. §103
4. Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant' s arguments; however the arguments are not persuasive. Applicant argues:
For example, the Office Action relies on Frank's disclosure of machine learning model 404 as the claimed "trained machine learning model." Claims 1 and 13 are patentable over Frank because Frank's machine learning model 404 does not perform at least features (1) and (2). For example, Frank does not teach or suggest machine learning model 404 "classifying an analyte value in the analyte data set as a predicted wellness event based on an evaluation of a first subset of analyte values prior to the analyte value and a second subset of analyte values subsequent to the analyte value," as claimed.
Regarding (a), the Examiner respectfully disagrees. Given the broadest reasonable interpretation, Frank at Para. 0028 and 0054 teach the limitation cited. The machine learning model present in the prior art of Frank using glucose measurements as diabetes classifications, under broadest reasonable interpretation, teaches the limitation. Examiner respectfully points to the updated rejection for full explanation.
Nor does Frank teach or suggest machine learning model 404 "generating, by the trained machine learning model, a predicted wellness event sequence-comprising a plurality of predicted wellness events including the predicted wellness event, wherein each predicted wellness event of the plurality of wellness events is associated with a respective analyte value in the analyte data set" or "updating display of the modified predicted wellness event sequence as a user interface element on graphical user interface to include a visual annotation corresponding to the predicted wellness event, wherein the visual annotation is aligned with the current prediction time on the user interface element," as claimed.
Regarding (b), the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as necessitated by amendment.
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Rule et al (US Publication No. 20190336678) discloses systems for rapid and accurate analyte measurement.
Bennett et al (US Publication No. 20210391052) discloses a computer-implemented method of detecting a missed bolus.
Simpson et al (US Publication No. 20220384007) discloses a method of monitoring compliance with an insulin regimen prescribed for a diabetic patient.
THIS ACTION IS MADE FINAL, necessitated by amendment. 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 extension fee 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.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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