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
This Office Action represents the first action on the merits.
Claim(s) 1-20 are pending
Priority
This Application claims priority to Provisional Application 63617722 filed 04 January 2024.
Information Disclosure Statement
The Information Disclosure Statement(s) (lDS) submitted on 02 June 2025 is/are in compliance with the provisions of 37 CFR 1.97 and has/have been fully considered by the Examiner.
Claim Objections
Claims 1, 13 objected to because of the following informalities: “And” should be added to the beginning of the last limitation of the claims. Therefore, for Claim 1, 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 displaying, as a user interface element on the graphical user interface, the modified predicted wellness event sequence.” Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: "a guardrail component configured to" in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The Specification at Para. 0055 ties the component to a computer. The specification at Para. 106 provides a sufficient algorithm for the component.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 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. The claim recites a system and method, which are within a statutory category. The limitations of:
Claims 1, 13 (Claim 1 being representative)
generate, based on analyte data received, a predicted wellness event sequence associated with the subject;
receive, a 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;
display, as a user interface element, the modified predicted wellness event sequence.
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 claimed invention amounts to managing personal behavior or interaction between people. The claim encompasses receiving, identifying, modifying, and displaying data in the manner described in the identified abstract idea, supra. 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.
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. 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 event sequences. 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 event sequences 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 link 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.
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 event sequences 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. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is 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 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 Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
Claims 1-2,7,9,12-14,19 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Frank et al (US Publication No. 20220354395) in view of BUDIMAN et al (Foreign Publication EP-3125761-B1).
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 from the in vivo analyte sensor over a predetermined period of time, the system comprising:
a trained machine learning model configured to generate, based on the analyte data received from the in vivo analyte sensor, a predicted wellness event sequence associated with the subject [Frank at Para. 0065 teaches with respect to the observation device data 214, the sensor identification 216 represents information that uniquely identifies the sensor 202 from other sensors, such as other sensors of other wearable glucose monitoring devices 104, other sensors implanted previously or subsequently in the skin 206, and so on; Frank at Para. 0094 teaches in this example 400, the prediction system 114 is depicted including preprocessing manager 402 and machine learning model 404, which are configured to generate a prediction of the diabetes classification 116 based on the glucose measurements 110 of the person 102];
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)];
a display configured to display, as a user interface element on the graphical user interface, the modified predicted wellness event sequence [Frank at Para. 0132 teaches FIG. 7 depicts an example of an implementation 700 of a user interface displayed for reporting a diabetes prediction of a user along with other information produced in connection with the diabetes prediction (interpret to combine with modified predicted wellness event sequence of BUDIMAN)].
Frank 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;
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 machine learning model of Frank with the false positive event of BUDIMAN with the motivation to improve the reliability of existing start-of-meal markers manually entered by the user.
Regarding Claim 2
Frank/BUDIMAN teach the system of claim 1,
Frank/BUDIMAN 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 7
Frank/BUDIMAN teach the system of claim 6,
Frank/BUDIMAN 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 9
Frank/BUDIMAN teach the system of claim 1,
Frank/BUDIMAN 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 12
Frank/BUDIMAN teach the system of claim 1,
Frank/BUDIMAN 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 from the in vivo analyte sensor over a predetermined period of time, the method comprising:
generating, by a trained machine learning model, based on a portion of the analyte data received from the in vivo analyte sensor, a predicted wellness event sequence associated with the subject [Frank at Para. 0065, 0094 (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)];
displaying, as a user interface element on the graphical user interface, the modified predicted wellness event sequence [Frank at Para. 0132 (see Claim 1 for explanation)].
Frank 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;
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 machine learning model of Frank with the false positive event of BUDIMAN with the motivation to improve the reliability of existing start-of-meal markers manually entered by the user.
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 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.
Claims 3, 10-11, 15 rejected under 35 U.S.C. 103(a) as being unpatentable over Frank, BUDIMAN as applied to claim 1, 13 above, and further in view of Derdzinski et al (US Publication No. 20210378563).
Regarding Claim 3
Frank/BUDIMAN teach the system of claim 1,
Frank/BUDIMAN do not 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 teaches 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. 0099 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].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Frank, BUDIMAN with the times of Derdzinski with the motivation to improve accuracy of glucose measurement predictions.
Regarding Claim 10
Frank/BUDIMAN teach the system of claim 1,
Frank/BUDIMAN do not teach wherein the predetermined period of time comprises a 1 minute interval.
Derdzinski teaches wherein the predetermined period of time comprises a 1 minute interval [Derdzinski at Para. 0043 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)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Frank, BUDIMAN, Fox with the interval of Derdzinski with the motivation to improve accuracy of glucose measurement predictions.
Regarding Claim 11
Frank/BUDIMAN teach the system of claim 1,
Frank/BUDIMAN do not teach wherein the predetermined period of time comprises a 24 hour interval [Derdzinski at Para. 0043 (see Claim 10 for explanation)].
Derdzinski teaches wherein the predetermined period of time comprises a 24 hour interval.
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Frank, BUDIMAN, Fox with the interval of Derdzinski with the motivation to improve accuracy of glucose measurement predictions.
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.
Claims 4, 16 rejected under 35 U.S.C. 103(a) as being unpatentable over Frank, BUDIMAN as applied to claim 1, 13 above, and further in view of Fox et al (US Publication No. 20150190100).
Regarding Claim 4
Frank/BUDIMAN teach the system of claim 1,
Frank/BUDIMAN 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, BUDIMAN 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 5, 17 rejected under 35 U.S.C. 103(a) as being unpatentable over Frank, BUDIMAN, Fox as applied to claim 4, 16 above, and further in view of Derdzinski et al (US Publication No. 20210378563).
Regarding Claim 5
Frank/BUDIMAN/Fox teach the system of claim 4,
Frank/BUDIMAN/Fox do not 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 teaches 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)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Frank, BUDIMAN, Fox with the times of Derdzinski with the motivation to improve accuracy of glucose measurement predictions.
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 6, 8, 18, 20 rejected under 35 U.S.C. 103(a) as being unpatentable over Frank, BUDIMAN as applied to claim 1, 13 above, and further in view of Pickus et al (US Publication No. 20220165432).
Regarding Claim 6
Frank/BUDIMAN teach the system of claim 1,
Frank/BUDIMAN 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/BUDIMAN 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, BUDIMAN with the display of Pickus with the motivation to improve accuracy in identifying or predicting glucose-based events.
Regarding Claim 8
Frank/BUDIMAN teach the system of claim 1,
Frank/BUDIMAN 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, BUDIMAN 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 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.
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:
DENG et al (Foreign Publication WO-2022235618-A1) discloses methods and systems for preventing a glycemic event.
HUANG et al (Foreign Publication WO-2023034820-A1) discloses systems and methods for predictive glucose.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN C EDOUARD whose telephone number is (571)270-0107. The examiner can normally be reached M-F 730 - 430.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on (571) 272 - 6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683