DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on August 24, 2026 has been entered.
Response to Amendment
The amendment filed August 24, 2026 has been entered.
Claims 1-4, 6-7, 10-12, 15, 16, 19-24 are presented for examination.
Applicant’s amendments necessitate new grounds of rejection under 35 U.S.C. 112(b).
The rejection under 35 U.S.C. 101 has not been withdrawn.
Applicant’s amendments necessitate new grounds of rejection under 35 U.S.C. 103.
Response to Arguments
Applicant's arguments filed April 6, 2026 regarding the rejection of the claims under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Regarding Applicant’s argument:
“The Office alleges that the human mind is capable of predicting a range of values from observed trend values, such as predicting temperatures based on past weather data. See Office Action at p. 3. However, the Office's example is far more abstract than the claimed invention. The
claims do not recite somehow making a general prediction about future glucose levels. Rather, the claims recite specific inputs (glucose data from an in vivo glucose sensor, and meal information comprising food and portion sizes) for a machine learning model that provides a specific output of a range of future glucose levels over a period of one or more hours including minimum and maximum values based on a confidence metric. At the level of detail claimed, the claims cannot practically be performed in the human mind. Therefore, the claims do not recite an abstract idea as alleged by the Office and are patent eligible at Step 2A, prong I..”
Applicant fails to identify why, at the level of detail claimed, the claims cannot be practically be performed in the human mind. The claimed inputs are extra-solution data-gathering. Producing a prediction of glucose trends (including maximum and minimum prediction bounds) based on inputs can be performed as a mental evaluation. The pre-trained learning model is described as a well-understood element for carrying out the evaluation via a series of evaluation steps embodied on a generic computer processor.
Regarding Applicant’s argument:
“The claims should alternatively be found to be patent eligible at Step 2A, prong II, as the claims provide an improvement to glucose monitoring systems. The Specification discloses the problem that legacy tools allow users to examine past meals but do not predict or analyze the impact of meals on future glucose levels. Specification (US2025/0120619A), [0009]. The claimed invention provides a solution to the problem in the art by allowing users to enter meal information for a planned meal, including foods and portion sizes, and provides a visualization of a predicted glucose response for the meal. Further, the visualization includes an interactive visual object for receiving adjustment of the foods and/or portion sizes to update the prediction to view the impact of the changes.”
As stated in the previous Office Action, the alleged improvement of “allow[ing] the user to analyze the impact of current meals and other events on future glucose levels” via an updated visualization is not an improvement, but rather the result of post-solution output of the results of iterative data gathering and analysis.
The limitation of “an interactive visual object” can be interpreted as, e.g., an alternative screen shown on the display of a generic processing device when executing data-gathering steps. For instance, a drop-down display, clickable icon, or web page viewed from a computer screen may each be considered an “interactive visual object.”
Regarding Applicant’s argument directed to the rejection of the claims under 35 U.S.C. 103:
Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-4 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.
Re. Claim 2: Applicant’s amendment removes antecedent basis for “the visualization application” and “the glucose monitoring application” recited in claim 2.
Re. Claim 3: Applicant’s amendment removes antecedent basis for “the visualization application” recited in claim 3.
Re. Claim 4: Applicant’s amendment removes antecedent basis for “the visualization application” recited in claim 4.
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 1-4, 6, 7, 10-12, 15, 16, and 19-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Each claim has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of the claims recites steps or instructions for ascertaining and processing data to measure a blood pressure of a mammal subject, which is grouped as a mental process. Accordingly, each of the claims recites an abstract idea.
Independent claims 1 and 15 similarly recite non-readable computer medium limitations or method limitations comprising:
receive glucose data collected by a sensor control device, wherein the sensor control device comprises sensor electronics coupled to an in vivo glucose sensor comprising a portion configured to be positioned in a body of a user to collect information about glucose levels (additional element, data gathering);
receive a user selection of meal information, wherein the meal information comprises one or more foods and a portion size for each of the one or more foods (data gathering);
predict, using a pre-trained machine learning model comprising a neural network, a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information entered by the user;
and output, to a display, a visualization of the glucose data (extra-solution activity, additional element),
the visualization comprising:
a line graph of glucose levels based on the glucose data collected by the sensor control device over time;
an indication of a most recent glucose level received from the sensor control device; and
the range of future glucose levels, wherein the range of future glucose levels is bounded by predicted minimum values and predicted maximum values over time,
wherein the range of future glucose levels is determined based on a confidence metric associated with each of the future glucose levels, and
wherein the range of future glucose levels is visually distinguishable from the line graph of glucose levels received from the sensor control device,
update, by the pre-trained machine learning model, the predicted range of future glucose levels at a regular interval based on an interval at which glucose data is received from the sensor control device (evaluation or judgement) (evaluation or judgement and/or extra-solution activity);
output an interactive visual object for receiving an adjustment to the meal information (details related to data-gathering);
update, by the pre-trained machine learning model, the predicted range of future glucose levels based on receipt of the adjustment of the meal information (subsequent evaluation)
As indicated above, the independent claim recites at least one step or instruction grouped as a mental process. Therefore, each of the independent claims recites an abstract idea. Each limitation, aside from language reciting generic computer components, can be grouped as a mental process (see italicized portions above), and is addressed as follows:
The limitation of predict… a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information entered by the user merely requires a user to obtain requisite data and perform evaluation thereon to yield a prediction of future glucose values. The limitation of using a pre-trained neural network is addressed later.
The limitation of providing specific visualization details (i.e., “the visualization comprising”) is merely a series of judgements as to how obtained should be displayed, whereby their eventual display is merely extra-solution output of results of analysis of received data.
The limitation of update, by the pre-trained machine learning model, the predicted range of future glucose levels based on receipt of the adjustment of the meal information entails a user merely appending additional data and performing additional evaluations thereon to provide an updated result. The phrase “by the pre-trained machine learning model” merely entails carrying out such steps through implementation of a pre-trained machine learning model.
Alternatively or additionally, these steps describe the concept of using implicit mathematical formula(s) (i.e., evaluation of machine learning models) to derive a conclusion based on input of medical data, which corresponds to concepts identified as abstract ideas by the courts, such as in Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989). The concept of the recited steps above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas.
The dependent claims merely include limitations that either further define the abstract idea (e.g. limitations relating to the data gathered, decisions of extra-solution display, or particular steps which are entirely embodied in the mental process) and amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they are merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Thus, these concepts are similar to court decisions of abstract ideas of itself: collecting, displaying, and manipulating data (Int. Ventures v. Cap One Financial), collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group), collection, storage, and recognition of data (Smart Systems Innovations).
Step 2A, Prong 2
The above-identified abstract idea is not integrated into a practical application because the additional elements, either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use.
More specifically:
Independent claims 1 and 15 similarly recite the following additional elements:
computing device as in claim 1; one or more processors as in claim 15;
a sensor control device, wherein the sensor control device comprises sensor electronics coupled to an in vivo glucose sensor;
a display;
a pre-trained machine learning model comprising a neural network.
Such additional elements are generically recited elements which do not improve the functioning of a computer or any other technology or technical field.
The sensor control device comprising sensor electronics coupled to an in vivo glucose sensor encompasses electronics necessarily found in typical continuous glucose monitoring systems (i.e., a sensor portion and electronics to transfer data from an implanted/invasive sensor). The claim recites merely acquiring data from generically recited sensor components, having no operative connection to the computing device/one or more processors executing computer instructions besides communication of obtained data, which amounts to insignificant, extra-solution activity in the form of mere data gathering, which does not constitute an integration into a practical application. Although the sensors may imply particular structure, their use in the mental process is merely extra-solution. See MPEP 2106.05(b).III:
“Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more. See Bilski, 561 U.S. at 610, 95 USPQ2d at 1009 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 197 (1978)), and CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690 (Fed. Cir. 2011) (citations omitted)”
The computing device/one or more processors and display are also recited at a high-level of generality (i.e., as a generic processors and memory performing a generic computer function of performing calculations and storing data, respectively, and known data-output components commonly associated with generic computers) such that it amounts no more than mere instructions to apply the exception using a generic computer component. As per Applicant’s Specification, one or more processors may be found implicit in carrying out claimed operations via “a mobile phone, tablet, personal computing device, or other similar computing device capable of communicating with analyte sensor 110 over a communication link” (Paragraph 0073). Thus, the actions carried out by the claims are suitably carried out by a number generic computing devices listed by Applicant known to possess each of a display, an input component, power supply and communication protocol capabilities.
The limitations of each of a glucose monitoring application and visualization application recited in claim 15 are merely directed to either abstract ideas or extra-solution data processing carried out via generic computer elements.
The limitations reciting the use of a pre-trained machine learning model comprising a neural network provide nothing more than mere instructions to implement an abstract idea on a generic computer. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer:
(1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished;
(2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and
(3) the particularity or generality of the application of the judicial exception.
The limitation of “predict… a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information entered by the user” and is performed “using a pre-trained machine learning model comprising a neural network.” The trained machine learning model is used to generally apply the abstract idea without placing any limits on how the pre-trained machine learning model comprising a neural network functions. These limitations recite the outcome of “predict… a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information entered by the user” and do not include any details about how the prediction steps are accomplished. See MPEP 2106.05(f). The recitation of “using a pre-trained machine learning model comprising a neural network” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element of a pre-trained machine learning model comprising a neural network limits the identified judicial exception “predict… a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information entered by the user,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (pre-trained neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Thus, such additional elements do not serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment (processing and display of glucose data via known display devices), such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified generically recited elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea is not integrated into a practical application.
Moreover, the above-identified abstract idea is not integrated into a practical application under because the claimed method and system merely implements the above-identified abstract idea using rules (e.g., computer instructions) executed by a computer (computing device/one or more processors). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract ideas identified above in the independent claims (and their respective dependent claims) are not integrated into a practical application.
Dependent claims 2-4, 10-12, 16, 19, 20-24, merely recite limitations pertaining to decisions of how data is output post-solution, extra-solution data-gathering steps carried out via a generic computing device, or are directed to additional steps encompassed by the judicial exception or requisite data-gathering steps.
Dependent claims 6, 7 recite an additional element of a trusted computer system, which is also interpretable, without further definition provided in the claims pertaining to the descriptor “trusted,” as a generic computing device which carries out limitations of the abstract idea including generation of visualization data and extra-solution transmission of data.
Accordingly, the claims are each directed to an abstract idea.
Step 2B
None of the claims include additional elements that, when viewed as a whole, are sufficient to amount to significantly more than the abstract idea for at least the following reasons:
Independent claims 1 and 15 similarly recite the following additional elements:
a sensor control device comprising sensor electronics coupled to an in vivo glucose sensor comprising a portion configured to be positioned in a body of a user;
a receiving device in wireless communication with the sensor control device via a Bluetooth communication protocol, the receiving device comprising a display, an input component, and a power supply;
one or more processors in communication with the receiving device… a memory storing a glucose monitoring application and a visualization application;
a pre-trained machine learning model comprising a neural network.
As per Applicant’s discussion of background devices (Paragraphs 0004-0006), the sensor control device comprising sensor electronics coupled to an in vivo glucose sensor comprising a portion configured to be positioned in a body of a user encompasses electronics necessarily found in typical continuous glucose monitoring systems (i.e., a sensor portion and electronics to transfer data from an implanted/invasive sensor). Thus, such components are considered parts of a well-understood, routine, and conventional element (i.e., continuous glucose monitoring systems).
As per Applicant’s Paragraph 0073, a receiving device… comprising a display, an input component, and a power supply may be “such as a mobile phone, tablet, personal computing device, or other similar computing device capable of communicating with analyte sensor 110 over a communication link” (Paragraph 0073). Thus, applicant establishes that a number of known devices are capable of fulfilling the limitations of a receiving device. Applicant’s one or more processors in communication with the receiving device… a memory storing a glucose monitoring application and a visualization application are embodied by generic processors and memory known which are constituent parts of the list of known devices Applicant provides.
Applicant’s disclosure is not particular regarding the details of the pre-trained machine learning model comprising a neural network, and recites “the models described above may be generated via any known machine learning techniques—e.g., unsupervised learning, supervised learning, semi-supervised learning, re-informed learning, clustering, classification, regression, decision tree, neural networks, anomaly detection or any others” (Paragraph 0211). No special programming or algorithms are indicated for how such algorithms operate. This lack of disclosure is acceptable under 35 U.S.C. 112(a) since this computer-implemented limitation performs non-specialized functions known by those of ordinary skill in the medical technology arts (i.e., machine learning model processing of medical data). Thus, Applicant's specification essentially admits that this computer-implemented limitation is conventional and performs well understood, routine and conventional activities in the computing or medical technology arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the pre-trained machine learning model comprising a neural network because it describes such an additional element in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding limitations that perform “well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications). Furthermore, as explained in Step 2A, Prong Two, limitations directed to the pre-trained machine learning model comprising a neural network are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
Dependent claims 5-7 recite an additional element of a trusted computer system. As per Applicant’s Paragraph 0128, a trusted computer system “may include servers, desktops, and other type of electronic device that support CGM system 800 by processing web-based traffic and HTTP request methods.” Since most generic computer processors are capable of processing web-based traffic and HTTP request methods, such an element is considered well-understood, routine, and conventional.
Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear from the claims themselves and the specification that these limitations require no improved computer resources and merely utilize already available computers with their already available basic functions to use as tools in executing the claimed process.
The recitation of the above-identified additional limitations in the claims amount to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
For at least the above reasons, the claims are directed to applying an abstract idea on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. In other words, none of the claims provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in the independent claims do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment (processing of sensor data). That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. As such, the above-identified additional elements, when viewed as whole, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, the claims merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself, or (ii) provide a technical solution to a problem in a technical field.
Therefore, none of the claims amounts to significantly more than the abstract idea itself.
Accordingly, the claims are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas implemented on a generic computer in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al.
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.
Claims 1, 6, 7, 10-12, 15, and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over:
Galley et al. (WO 2024200408 A1) (disclosed by Applicant) (hereinafter – Galley) in view of
Narayanaswami et al. (US 20220361812 A1) (hereinafter – Narayanaswami).
Re. Claims 1 and 15: A computer-readable medium comprising instructions that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:
receive glucose data collected by a sensor control device (Fig. 1: user device 1 and medical server 5 in communication with continuous glucose monitoring sensor device 4),
wherein the sensor control device comprises sensor electronics coupled to an in vivo glucose sensor comprising a portion configured to be positioned in a body of a user to collect information about glucose levels (Fig. 1: continuous glucose monitoring sensor device 4; Page 17, lines 7-8: “For example, the continuous glucose monitoring sensor device 4 can be a disposable glucose sensor that is, e.g., worn under the skin”);
receive a user selection of meal information, wherein the meal information comprises one or more foods and a portion size for each of the one or more foods (Page 7: “Determining the plurality of predicted glucose values may further be based on at least one of the following: meal event information, insulin bolus information, insulin basal amounts, physical activity event information, stress event information, illness event information. In particular, the predicted glucose values may be determined based on a recent or planned meal consumption (i.e. , carbohydrate intake)…;” Examiner notes that carbohydrate intake may be reasonably construed as a portion size; Page 17: “The (predicted) glucose level influencing events include, e.g., meal consumption… The historical data may be received and/or created and/or modified by… the user device 1 , e.g., via the input device 2;” Examiner notes that historical data comprises user entry of meal information, after which prediction time windows are determined).
Galley discloses an invention which can predict, using a pre-trained machine learning model (Page 13), a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information entered by the user (Figs. 3a, 3b: visualizations including predictions of glucose trends provided by statistical model). Galley is deficient in reciting that the statistical model comprises a neural network.
Narayanaswami teaches analogous art in the technology of predicting blood glucose levels after a meal (Paragraph 0016) particularly utilizing a neural network (Paragraph 0040; Fig. 2). Narayanaswami further teaches the invention configured to predict, using a pre-trained machine learning model comprising a neural network, a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information (Paragraph 0016: “In a first aspect of the invention, a machine-learning model that is trained for each individual patient is provided that predicts the post-prandial blood glucose trace of the patient based on the macronutrient profile of the ingested meal and given a specific proposed bolus dose of insulin;” Paragraph 0040: “In preferred embodiments of the invention, the model may comprise a convolutional neural network 214 and a post-prandial blood glucose prediction model 216, which may be, for example, a recurrent neural network. Convolution neural network 214 may output a dilated convolution of the input data 202-210 in the form of features extracted for the input data that can be used by model 216 to generate the post-prandial blood glucose prediction 220;” Paragraph 0044: various interfaces of Figs. 3A-3C discussed for entering macronutrient profile information 210 via a graphical user interface);
It would have been obvious to one having skill in the art before the effective filing date to have modified Galley to include the use of a neural network and associated input and interfaces therefor, the motivation being that such input features allow a user to enter more granular information to a neural network, whereby such a neural network trained on fats, proteins, and carbohydrates (as opposed to carbohydrate intake alone in Galley) allows for consideration of how the overall macronutrient profile affects the amount of glucose which enters the blood stream, as well as the rate and timing of absorption (Paragraph 0003).
Galley as modified by Narayanaswami further teaches:
output, to a display of the receiving device, a visualization of the glucose data,
the visualization comprising:
a line graph of glucose levels based on the glucose data collected by the sensor control device over time (Figs. 3a, 3b);
an indication of a most recent glucose level received from the sensor control device (Figs. 3a, 3b: see vertical line labeled “now” on x-axis); and
the range of future glucose levels, wherein the range of future glucose levels is bounded by predicted minimum values and predicted maximum values over time (Figs. 3a, 3b: see vertical bars 33 for predicted glucose levels 32),
wherein the range of future glucose levels is determined based on a confidence metric associated with each of the future glucose levels (Page 18, lines 19-20: “Confidence intervals for the predicted glucose values 32 are represented by bars 33”), and
wherein the range of future glucose levels is visually distinguishable from the line graph of glucose levels received from the sensor control device (Figs. 3a, 3b: : dots indicating received glucose levels do not have confidence intervals and appear before vertical line marked “now”).
Narayanaswami, in teaching further detail regarding the incorporated input features necessary for the neural network, further teaches the invention configured to:
output an interactive visual object for receiving an adjustment to the meal information (Figs. 3A-3C; Paragraph 0027: “FIGS. 3A-3C show several embodiments of user interfaces enabling the user to enter the macronutrient profile of an ingested meal”); and
update, by the pre-trained machine learning model, the predicted range of future glucose levels based on receipt of the adjustment of the meal information (Claim 18: “The method of claim 5 wherein the machine-learning model is initially trained on a wide population of users or a cluster of users similar to the user and further wherein the machine-learning model is updated based on subsequent meals entered by the user and the resulting post-prandial blood glucose traces;” Paragraph 0036: “The bolus dosing application 156 may track and correlate the macronutrient profiles of meals eaten by the user with blood glucose traces of the user for a predetermined period of time (hereinafter referred to as the “post-prandial window”) after the user has ingested the meal…;” Paragraph 0049: “Based on the inputs 202-210, including the initial bolus dose 402 and bolus split 404, model 216 produces a prediction 220 of the post-prandial blood glucose trace as previously described;” Fig. 2: macronutrient profile 210 entered into CNN to produce post-prandial blood glucose prediction).
Claim 15 recites limitations of claim 1 mutatis mutandis embodied in a method; thus, the rejection of claim 1 encompasses each limitation required by claim 15.
Re. Claim 6: Galley as modified by Narayanaswami teaches the invention according to claim 5. Galley further teaches the invention wherein the receiving device receives data from trusted computer system to generate the visualization of the glucose data (Page 2: “… transmitting, from the user device to a medical server, continuous glucose monitoring data indicative of a glucose level in a bodily fluid; receiving, in the user device from the medical server, a plurality of predicted glucose values for a prediction time window…;” see visualizations of prediction time windows generated at Figs. 3a, 3b).
Re. Claim 7: Galley as modified by Narayanaswami teaches the invention according to claim 1. Galley further teaches the invention wherein the trusted computer system is configured to generate the visualization of the glucose data and to transmit the visualization of the glucose data to the receiving device for display (see rejection of claim 6).
Re. Claim 10: Galley as modified by Narayanaswami teaches the invention according to claim 1. Galley further teaches the invention wherein the predicted range of future glucose levels is updated in real-time (Page 15; “Continuous glucose monitoring may be implemented as a nearly real-time… monitoring procedure frequently or automatically providing/updating analyte values without user interaction;” Examiner notes that the statistical model is responsive to such data in providing visualizations shown in Figs. 3a, 3b).
Re. Claims 11 and 19: Galley as modified by Narayanaswami teaches the invention according to claims 1 and 15. Galley further teaches the invention wherein the confidence metric is based in part on a time since the glucose data was received (Figs. 3a, 3b: see confidence intervals increasing as they move further from “now” indicator; furthermore, see differing prediction time window lengths 30 which necessarily affect confidence interval generation; Page 9: “The shortened display time interval may for example exclude predicted glucose values subsequent to a time distance after the expected time of the at least one predicted glucose level influencing event occurring, in particular subsequent to a time distance after a confidence (time) interval around the expected time. The confidence interval may be a p confidence interval with p being at least 70 %, preferably at least 85 %, more preferably at least 95 %. The shortened time interval may thus exclude, e.g., predicted glucose values impacted by meal consumption;” Pages 18-19: discussion of time window lengths dependent on received plurality of predicted glucose values and glucose influencing event data;).
Re. Claims 12 and 20: Galley as modified by Narayanaswami teaches the invention according to claim 1. Galley further teaches the invention wherein each future glucose level of the range of future glucose levels comprises a predicted maximum value and a predicted minimum value (Figs. 3a, 3b: upper and lower bounds of confidence intervals; A confidence interval is a range of values that is likely to contain a true value).
Re. Claims 21 and 22: Galley as modified by Narayanaswami teaches the invention according to claims 1 and 15. Narayanaswami, in teaching further detail regarding the incorporated input features necessary for the neural network, further teaches the invention wherein the meal information further comprises an amount of carbohydrates (see rejection of claim 1 – the modification suggested by Narayanaswami includes entering an amount of carbohydrates).
Claims 2-4, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over:
Galley et al. (WO 2024200408 A1) (disclosed by Applicant) (hereinafter – Galley) in view of
Narayanaswami et al. (US 20220361812 A1) (hereinafter – Narayanaswami) in further view of
Mazlish et al. (US 20190274624 A1) (hereinafter – Mazlish).
Re. Claims 2-4 and 16: Galley as modified by Narayanaswami teaches the invention according to claims 1 and 15. Galley teaches a software implemented solution which processes and analyzes glucose data (Galley, Page 2), thus implicitly teaching a glucose monitoring application, and also teaches particular display of data (Galley, Figs. 3a, 3b), thus teaching what can be considered an application which performs visualization of data, i.e., a “visualization application” as claimed. Galley does not disclose whether the visualization application is visually separate from a user interface of the glucose monitoring application.
Mazlish teaches analogous art in the technology of glucose monitoring (Abstract). Mazlish further teaches the invention wherein the visualization application is visually separate from a user interface of the glucose monitoring application (Figs. 5A-14B: visualization is visually separate from alarm messages and indicator screen providing data regarding glucose monitoring analysis).
It would have been obvious to one having skill in the art before the effective filing date to have modified Galley as modified by Narayanaswami to include the application user interfaces as taught by Mazlish, the motivation being that doing so allows a user to view glucose trend data as well as pertinent alarm messages and visual indicators as to when to take an interventive action (Paragraphs 0094, 0095).
Regarding claim 3, the incorporated application user interfaces of Mazlish also teaches a visualization application being a sub-module of the glucose monitoring application (a distinct sub-module of visualization program code is required to provide the visualization of blood glucose data and trend line 310).
Regarding claim 4, the incorporated application user interfaces of Mazlish also teaches wherein the visualization application is visually embedded in a user interface of the glucose monitoring application (Fig. 5B, 5C: trend line embedded within a user interface of an application which provides glucose monitoring capabilities and analysis).
Claim 16 recites limitations of claim 2 mutatis mutandis embodied in a method; thus, the rejection of claim 2 encompasses each limitation required by claim 16.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over:
Galley et al. (WO 2024200408 A1) (disclosed by Applicant) (hereinafter – Galley) in view of
Narayanaswami et al. (US 20220361812 A1) (hereinafter – Narayanaswami) in further view of
MyFitnessPal – Wayback Machine capture – February 28, 2023 (hereinafter – MyFitnessPal).
Re. Claim 23: Galley as modified by Narayanaswami teaches the invention according to claim 15, but does not teach wherein the meal information is based on information obtained via a barcode or QR code corresponding to a food.
MyFitnessPal is a meal logging application with the capability of logging foods via barcode scanning (Pages 3-4: “Logging Simplified. Scan barcodes, save meals and recipes, and use QuickTools for fast and easy food tracking”).
It would have been obvious to one having skill in the art before the effective filing date to have modified Galley as modified by Narayanaswami to utilize scanning barcode labels as taught by MyFitnessPal, the motivation being that doing so enables fast and easy food tracking (Page 4).
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over:
Galley et al. (WO 2024200408 A1) (disclosed by Applicant) (hereinafter – Galley) in view of
Narayanaswami et al. (US 20220361812 A1) (hereinafter – Narayanaswami) in further view of
Sheriff, Dani. “MyFitnessPal Tutorial: Logging Meals” workingagainstgravity, 20 January 2017, https://www.workingagainstgravity.com/articles/myfitnesspal-tutorial-logging-meals (hereinafter -- Sheriff).
Re. Claim 24: Galley as modified by Narayanaswami teaches the invention according to claim 15, but does not teach wherein the meal information is received based on a selection of a food from a list of foods in a partner system in communication with the one or more processors.
Sheriff teaches a tutorial on how to use the popular meal tracking app, MyFitnessPal. Sheriff further teaches wherein the meal information is received based on a selection of a food from a list of foods in a partner system in communication with the one or more processors (Pages 4, 5: list of foods that may be entered from MyFitnessPal database; additionally, see MyFitnessPal reference used in the rejection of claim 23: page 1: “Log from over 14 million foods”).
It would have been obvious to one having skill in the art before the effective filing date to have modified Galley as modified by Narayanaswami to utilize selection of foods from a list of foods in a partner system (e.g., a database of foods as taught by MyFitnessPal), the motivation being that doing so enables a user to identify an item that matches a particular meal (Sheriff, Page 5: “Sift through these to find the item that best matches”) which bypasses the need to manually enter each macronutrient.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure, and provides similar disclosure to elements found in Narayanaswami:
Roy et al. (US 20180174675 A1) – Figs. 14-17; Paragraph 0100;
Liu et al. (US 12011262 B2) – Claim 19;
Pavan et al. (US 20250032710 A1) – Paragraph 0134: “In some examples, meal information may be received via a convenient user interface provided by application 106;” The devices, structures, systems, computer program products, and methods of certain embodiments may utilize aspects disclosed in the following publications and which are hereby incorporated by reference herein in their entirety… Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information… Convolutional recurrent neural networks for glucose prediction…”
Nakashima et al. (US 20240233908 A1) -- Paragraph 0059: “The learning unit 201 updates parameters such as weights of the neural network based on the ground truth label. That is, the learning unit 201 calculates parameters that minimize the error function between the prediction and the ground truth label. The measurement data of the blood sugar level is used as the ground truth label. When a multidimensional vector including the exercise data and the meal data is input to the prediction model, the blood sugar level is output. Furthermore, by sequentially inputting the time-series data, the prediction model can predict the fluctuations in the blood sugar level.”
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/JUSTIN XU/Primary Examiner, Art Unit 3791
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