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 .
Status of the Claims
The status of the claims as of the response filed 1/22/2026 is as follows: Claims 2, 11-12, and 18-28 remain cancelled. Claim 1 is currently amended. Claims 3-10, 13-17, and 29 are as previously presented. Claims 1, 3-10, 13-17, and 29 are currently pending in the application and have been considered below.
Response to Amendment
Rejection Under 35 USC 112(b)
Claim 1 has been amended to sufficiently clarify the indefinite language such that the corresponding 35 USC 112(b) rejections are withdrawn.
Response to Arguments
Rejection Under 35 USC 101
On page 7 of the response filed 1/22/2026 Applicant argues that the independent claims do not recite an abstract idea at least because they recite “specific elements that are not practical to be performed in the mind, including: (1) continuously obtaining heart rate data and blood glucose levels, (2) performing machine learning to create a glucose metabolization model, (3) automatically generating and transmitting notification messages over a network, (4) prompting a subject via a user interface and receiving user input via the user interface, (5) generating a first and second visualizations of blood glucose levels over a duration of time, and (6) outputting, via the user interface, an overlay combining the first and second visualizations.” Applicant’s arguments are fully considered, but are not persuasive. As an initial matter, Examiner notes that the claims have not been characterized as reciting a mental process, and are rather considered to recite a certain method of organizing human activity, e.g. managing personal behavior, relationships, or interactions between people (see paras. 20-21 of the non-final rejection mailed 7/24/2025). Examiner maintains that many aspects of challenged elements (1)-(6) still fit into the “certain methods of organizing human activity” grouping. For example, a clinician could receive and track heart rate, glucose level, and food consumption data from a patient, and use these types of data to fit/create a glucose metabolization model that can predict a patient’s blood glucose levels based on heart rate, blood glucose levels, and food consumption data. The clinician could then communicate with a patient to monitor heart rate data and blood glucose levels in an ongoing manner and make determinations about whether the measured blood glucose levels deviate from a target response. If the measured glucose levels deviate from the target response, the clinician could attempt to search through the food consumption logs to find a corresponding food that explains the deviation, and in the case that no corresponding food is present the clinician could generate a message indicating a possible missed food log event and communicate the message to all relevant users (e.g. the patient, a family member of the patient, other caregivers of the patient, etc.) so that the users may be informed of possible missed food log events. In addition, the clinician could directly prompt the patient to input the missing food event, and receive input from the patient corresponding to the missing food that was consumed by the patient. The clinician could utilize the fitted glucose metabolization model to predict a patient’s future glucose response given current conditions and plot a graph to visually depict the predicted response, along with plotting a graph of actual measured glucose response overlaid on the predicted graph as the actual data becomes available, and communicate the visual graph to the patient. Thus, these functions are found to be part of the abstract idea, but for the recitation of the additional elements of physiological sensors, machine learning, a network, and a user interface, which are evaluated in Steps 2A – Prong 2 and 2B.
In the instant case, the additional computing elements (i.e. the computer memory, electronic display, computer processors, high-level machine learning, computer network, and user interface) amount to instructions to “apply” the exception with a computer because steps that could otherwise be performed as a certain method of organizing human activity (as explained above) are merely being implemented digitally/electronically using computing components recited at a high level of generality (see MPEP 2106.05(f)). That is, otherwise-abstract functions like storing data, creating a prediction model, determining blood glucose deviations, searching food logs, generating and transmitting a message, prompting a user for input and receiving the input, generating a medical recommendation, plotting/ graphing glucose levels, outputting information, etc. are merely being automated in an electronic environment by using high-level computing components like processor and memory, unspecified “machine learning,” a user interface, etc. as tools with which to implement the functions. The use of a heart rate monitor and continuous glucose monitor to track and continuously obtain/receive physiological data amounts to insignificant extra-solution activity in the form of mere data gathering, because these elements act as necessary means of gathering the data needed for the main analysis steps (see MPEP 2106.05(g)). Accordingly, these elements do not provide integration into a practical application or “significantly more” than the abstract idea itself, as explained in more detail below.
On pages 8-9 of the response Applicant argues that claim 1 “provides improvements in glucose monitoring technology” by “allowing users to receive up-to-date tracking of possible missed food log events, enter user input of food consumption data into the blood glucose monitoring device, and view an overlay combining visualizations of predicted and actual blood glucose levels for a test subject.” Applicant points to several examples in the specification that outline the features for displaying data to a user, notifying a user of events, and interactively prompting a user for input. Applicant further analogizes the instant claims to those found eligible in CardioNet directed to an improvement in cardiac monitoring technology, and Example 42 of the 2019 Revised Patent Subject Matter Eligibility Guidance.
Applicant’s arguments are fully considered, but are not persuasive. Many of these features are part of the abstract idea itself, such as generating visualizations representing glucose response levels, generating and sharing a message with users, and interactively prompting a user for input, because such functions can be achieved via a human actor like a clinician managing their personal behavior and/or interactions with other people (as explained above). The use of computing elements like one or more processors, a computer network, and a user interface to implement these otherwise-abstract functions does not amount to an improvement to glucose monitoring technology as Applicant asserts, and instead amount to instructions to “apply” the judicial exceptions using high-level computing components such that the otherwise-abstract functions of evaluating and sharing patient data are automated and/or digitized in an electronic environment (see MPEP 2106.05(f)).
Examiner also notes that the claimed features are not analogous to improvements in cardiac monitoring technology, and instead describe steps that, but for the recitation of generic computer components and sensor devices, a human actor such as a clinician could follow to manage their personal behavior and/or interactions with other people to monitor and analyze patient data, make medical predictions and determinations, clarify missed logging events, recommend actions to improve the patient’s wellness, and share data with the patient and other users. Further, the claimed features of automatically generating a message indicating a possible missed food log event and transmitting the message to all users of the interactive glucose monitoring device over a user network are not analogous to the technical improvement of Example 42 because these features do not provide technical features such as collecting, converting, and consolidating patient information from various physicians and healthcare providers into a standardized format; rather they describe steps that could be implemented in analog interactions between human actors but for the recitation of generic computer components, as explained in the Step 2A – Prong 1 abstract idea analysis. In Example 42, the crux of the invention was a technical improvement related to specific formatting conversions and remote accessibility; in contrast, the crux of the instant claims is analysis of patient data to identify foods associated with glucose level deviations and/or potential missed food logging events, which merely utilize computer infrastructure such as processors, a network, and a user interface as a means with which to digitize the certain method of organizing human activity.
On page 10 of the response Applicant argues that the combination of a heart rate monitor, a continuous glucose monitor, and operations (a)-(c) and (e)-(h) was not well-understood, routine, or conventional at the time of filing and thus provide significantly more than the abstract idea itself under Step 2B. Applicant’s arguments are fully considered, but are not persuasive. Many of the functions of steps (a)-(c) and (e)-(h) are considered part of the abstract idea itself, as explained above, and thus cannot be considered additional elements beyond the abstract idea. The only additional elements beyond the abstract idea itself include the following underlined elements:
a heart rate monitor configured to track and continuously obtain heart rate data of the test subject;
a continuous glucose monitor configured to track and continuously receive the blood glucose levels of the test subject;
computer memory configured to store food consumption data and glucose levels;
an electronic display comprising a user interface;
one or more computer processors operatively coupled to the heart rate monitor, the continuous glucose monitor, and the electronic display, wherein the one or more computer processors are individually or collectively programmed to perform steps (a) through (h)
use of machine learning to create a glucose metabolization model;
transmission of the message to users over a computer network; and
a user interface to output data to and receive input from a user.
As explained in more detail below, these elements amount to instructions to “apply” the judicial exception with computer components because otherwise-abstract operations like analyzing and sharing data among users are merely being digitized/automated with high-level computing components like processors, unspecified “machine learning,” a computer network, and a user interface. The heart rate monitor and CGM amount to insignificant extra-solution activity in the form of data gathering because they merely act as means for obtaining the heart rate and glucose data needed for the main data analysis steps of the invention. Additionally, Examiner notes that the combination of a heart rate monitor, continuous glucose monitor, and computing elements like processors, machine learning, and user interfaces to evaluate patient data and make health predictions and recommendations is well-understood, routine, and conventional, as evidenced by at least Fig. 5, [0065], [0071], [0076], & [0536]-[0539] of Pauley et al. (US 20210104173 A1); Fig. 1, [0030]-[0036], & [0049] of Wexler et al. (US 20200375549 A1); and abstract, Fig. 1, [0007], & [0318]-[0327] of Dalal et al. (US 20200245913 A1).
For the reasons outlined above, the 35 USC 101 rejections are upheld for claims 1, 3-10, 13-17, and 29.
Rejection Under 35 USC 103
On pages 10-11 of the response Applicant argues that Pauley and Hayter fail to disclose or suggest aspects of claim 1 related to creating a glucose metabolization model through machine learning and using the glucose metabolization model to generate a first visualization representing projected blood glucose levels that is overlaid with a second visualization representing actual glucose response over a duration of time. Applicant specifically asserts that “Pauley merely describes a categorical classification of blood glucose variability” and thus “merely provides predictions of discrete class labels, and cannot distinguish between two food items that may produce markedly different blood glucose responses but still fall into the same blood glucose variability category” such that the reference fails to disclose generating (i) a first visualization, using the glucose metabolization model, of projected blood glucose levels corresponding to the first glucose response over a duration of time, and (ii) a second visualization of actual blood glucose levels corresponding to the actual glucose response over the duration of time” as in claim 1.
Applicant’s arguments are fully considered, but are not persuasive. Examiner respectfully disagrees that Pauley only discloses categorical predictions of blood glucose response; para. [0122] notes that “An example prediction process can include a model for determining a user’s blood glucose concentration or a parameter related to blood glucose based on user data” (emphasis added), showing that the prediction models disclosed by the system may be used to predict not just categorical labels of glucose variability, but also glucose response concentration values. This aspect of the system is further shown in Figs. 20B-C and 23A, which depict various graphs showing measured and/or predicted glucose concentrations on the y-axis and time on the x-axis are shown.
Examiner concedes that Pauley alone does not appear to disclose overlaying a first visualization representing a projected glucose response with a second visualization representing an actual glucose response, though it does show these types of visualizations separately (see Figs. 23A and 20C, respectively). However, Hayter further teaches that two visualizations representing actual blood glucose levels and likely (i.e. projected) blood glucose levels over a period of time may be superimposed on a display for a user (Hayter Fig. 3C, [0128], noting trace 343 of the user’s current blood glucose level (i.e. actual blood glucose level as measured by a continuous analyte monitor as in [0095]) is overlaid with trace 346 of the user’s likely blood glucose level based on the user’s average prior response to similar stimuli (i.e. equivalent to projected blood glucose level)). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the overlaid projected blood glucose level visualizations and separate actual trend visualizations of Pauley such that visualizations of projected and actual blood glucose levels are overlaid in one display as in Hayter in order to provide the user with real-time visual feedback of the likely result of food consumption in the context of current actual blood glucose levels (as suggested by Hayter [0128]).
For the reasons outlined above, Examiner maintains that the combination of Pauley and Hayter renders obvious the claims as presently drafted, as explained in more detail below.
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, 3-10, 13-17, and 29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
In the instant case, claims 1, 3-10, 13-17, and 29 are directed to a device (i.e. a machine). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A – Prong 1
Independent claim 1 recites steps that, under their broadest reasonable interpretations, cover certain methods of organizing human activity, e.g. managing personal behavior, relationships, or interactions between people. Specifically, the claims recite:
a heart rate monitor configured to track heart rate data of the test subject over a duration of time;
a continuous glucose monitor configured to track blood glucose levels of the test subject over the duration of time;
computer memory configured to store food consumption data and glucose levels responsive to the food consumption data;
an electronic display comprising a user interface;
one or more computer processors operatively coupled to the heart rate monitor, the continuous glucose monitor, and the electronic display, wherein the one or more computer processors are individually or collectively programmed to:
(a) perform machine learning to create a glucose metabolization model based on the food consumption data and glucose levels, wherein the glucose metabolization model is configured to predict blood glucose levels in response to at least heart rate data, blood glucose levels, and food consumption data;
(b) continuously obtain the heart rate data of the test subject using the heart rate monitor;
(c) continuously receive the blood glucose levels of the test subject using the continuous glucose monitor;
(d) determine whether the blood glucose levels of the test subject deviate from a target response;
(e) when the blood glucose levels of the test subject deviate from the target response,
(i) determine a food consumed by the test subject that corresponds to the deviated blood glucose levels, wherein determining the food consumed by the test subject comprises:
(1) searching food log data of the test subject for a presence of food consumption corresponding to the deviated blood glucose levels, and
(2) if the searching does not indicate the presence of food consumption corresponding to the deviated blood glucose levels, then automatically generating a message indicating a possible missed food log event, transmitting the message to all users of the interactive glucose monitoring device over a computer network, so that each user can track possible missed food log events, prompting the test subject via the user interface to input the food consumed by the test subject, and receiving, via the user interface, user input of the food consumed by the test subject, and
(ii) generate at least one alternative action for the test subject, wherein the at least one alternative action generates a first blood glucose response in the test subject with a reduced deviation from the target response as compared to a second glucose response generated by the test subject consuming the food in absence of the at least one alternative action;
(f) generate (i) a first visualization, using the glucose metabolization model, of projected blood glucose levels corresponding to the first glucose response over a duration of time, and (ii) a second visualization of actual blood glucose levels corresponding to the actual glucose response over the duration of time;
(g) generate an overlay that combines the first visualization of projected blood glucose levels and the second visualization of actual blood glucose levels; and
(h) output, via the user interface, a user recommendation relating to the at least one alternative action and the overlay combining the first visualization of projected blood glucose levels and the second visualization of actual blood glucose levels.
But for the recitation of generic computer components like computer memory, processors, an electronic display comprising a user interface, etc., each of the italicized functions, when considered as a whole, describe a diabetes monitoring/management interaction that could be achieved by a clinician or other medical professional managing their personal behavior and/or interactions with others (e.g. a patient). For example, a clinician could receive and track heart rate, glucose level, and food consumption data from a patient, and use these types of data to fit/create a glucose metabolization model that can predict a patient’s blood glucose levels based on heart rate, blood glucose levels, and food consumption data. The clinician could then communicate with a patient to monitor heart rate data and blood glucose levels in an ongoing manner and make determinations about whether the measured blood glucose levels deviate from a target response. If the measured glucose levels deviate from the target response, the clinician could attempt to search through the food consumption logs to find a corresponding food that explains the deviation, and in the case that no corresponding food is present the clinician could generate a message indicating a possible missed food log event and communicate the message to all relevant users (e.g. the patient, a family member of the patient, other caregivers of the patient, etc.) so that the users may be informed of possible missed food log events. In addition, the clinician could directly prompt the patient to input the missing food event, and receive input from the patient corresponding to the missing food that was consumed by the patient. The clinician could further use their medical expertise to think of an alternative food the patient could have eaten instead or another corrective action to remedy the deviated glucose level (e.g. performing an exercise, administering insulin, etc.) that is appropriate for the patient. The clinician could utilize the fitted glucose metabolization model to predict a patient’s future glucose response given current conditions and plot a graph to visually depict the predicted response, along with plotting a graph of actual measured glucose response overlaid on the predicted graph as the actual data becomes available. The clinician could finally communicate the visual graph showing both predicted and actual glucose responses in addition to the determined recommendation to the patient. Thus, the steps recited in claim 1 describe various interactions between a patient and one or more medical professionals, and accordingly recite an abstract idea in the form of a certain method of organizing human activity.
Dependent claims 3-10, 13-17, and 29 inherit the limitations that recite an abstract idea from their dependence on claim 1, and thus these claims also recite an abstract idea under the Step 2A – Prong 1 analysis. In addition, claims 3-10, 13, 15-16, and 29 recite further limitations that, under their broadest reasonable interpretations, merely further describe the abstract idea identified above.
Specifically, claims 3 and 6-10 specify types of alternative actions, each of which are types of action recommendations that a clinician would be capable of thinking of and communicating to a patient.
Claim 4 specifies further types of inputs considered when generating the alternative action, each of which are types of data that a clinician would be capable of observing and considering in such a determination.
Claim 5 recites generating and displaying a third visualization of blood glucose levels of the subject over a duration of time. A clinician would be capable of drawing or plotting a patient’s blood glucose over time to convey this information to a patient, as well as including predicted responses for an alternative action on the visualization to succinctly communicate the projected impact of an action for the patient.
Claim 13 recites generating the alternative action by accessing logs of the subject to determine at least one previous action performed by the subject that reduced an elevation in blood glucose levels. A clinician could achieve this step by speaking with a patient about their history of mitigating actions in response to high blood glucose.
Claim 15 recites displaying a plurality of alternative actions to the subject, receiving an alternative action selected by the subject from among the plurality of alternative actions, and displaying a predicted blood glucose response for the subject corresponding to the selected alternative action. These steps could be achieved during a diabetes management interaction by a clinician visually presenting multiple action options to a patient, speaking with the patient to obtain their preferred action selection, then drawing or plotting a predicted blood glucose response for the selected action to visually convey this information to the patient.
Claim 16 recites outputting a plurality of actions according to predicted blood glucose level response for the subject, which a clinician could achieve by writing down recommended actions for the patient according to the clinician’s predictions of their effect on patient blood glucose levels.
Claim 29 recites determining and outputting time-in-range data for the test subject based at least in part on the first or second glucose response over the duration of time, which a clinician could achieve by using their medical expertise to make determinations about the amount of time that the patient’s blood glucose levels remain within specified acceptable glucose response ranges or zones and communicating the duration to the patient.
However, recitation of an abstract idea is not the end of the analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A – Prong 2
The judicial exception is not integrated into a practical application. In particular, independent claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes the following additional elements (underlined):
a heart rate monitor configured to track and continuously obtain heart rate data of the test subject;
a continuous glucose monitor configured to track and continuously receive the blood glucose levels of the test subject;
computer memory configured to store food consumption data and glucose levels;
an electronic display comprising a user interface;
one or more computer processors operatively coupled to the heart rate monitor, the continuous glucose monitor, and the electronic display, wherein the one or more computer processors are individually or collectively programmed to perform steps (a) through (h)
use of machine learning to create a glucose metabolization model;
transmission of the message to users over a computer network; and
a user interface to output data to and receive input from a user.
The additional computing elements (i.e. the computer memory, electronic display, computer processors, high-level machine learning, computer network, and user interface) amount to instructions to “apply” the exception with a computer because steps that could otherwise be performed as a certain method of organizing human activity (as explained above) are merely being implemented digitally/ electronically using computing components recited at a high level of generality (see MPEP 2106.05(f)). That is, otherwise-abstract functions like storing data, creating a prediction model, determining blood glucose deviations, searching food logs, generating and transmitting a message, prompting a user for input and receiving the input, generating a medical recommendation, plotting/graphing glucose levels, outputting information, etc. are merely being automated in an electronic environment by using high-level computing components like processor and memory, unspecified “machine learning,” a user interface, etc. as tools with which to implement the functions. The use of a heart rate monitor and continuous glucose monitor to track and continuously obtain/receive physiological data amounts to insignificant extra-solution activity in the form of mere data gathering, because these elements act as necessary means of gathering the data needed for the main analysis steps (see MPEP 2106.05(g)). Accordingly, claim 1 as a whole is directed to an abstract idea without integration into a practical application.
The judicial exception recited in dependent claims 3-10, 13-17, and 29 is also not integrated into a practical application. Claims 3-10, 13, 15-16, and 29 are performed with the same additional elements as the independent claims without introducing new additional elements, and accordingly also amount to instructions to “apply” the exception with a computer as explained above for claim 1.
Claim 14 recites that accessing the logs of the subject comprises communicating over a network with a server system via an application executed by an electronic device, which amounts to instructions to “apply” the exception with a computer because an otherwise-abstract step (e.g. accessing patient logs) is merely being implemented with high-level computing components in an electronic or digital environment.
Claim 17 recites generating the at least one alternative action by processing data for the subject by a recommender system on at least one server system, which amounts to instructions to “apply” the exception with a computer because an otherwise-abstract step (e.g. generating a recommended patient action) is merely being digitized and/or automated with high-level computing components.
Accordingly, the additional elements of claims 1, 3-10, 13-17, and 29 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1, 3-10, 13-17, and 29 are directed to an abstract idea.
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 elements of a computer memory, electronic display comprising a user interface, computer processors, high-level machine learning, and computer network to perform the storing, creating, determining, searching, generating, transmitting, prompting, receiving, outputting, etc. steps of the invention amount to mere instructions to apply the exception using generic computer components. As evidence of the generic nature of the above recited additional elements, Examiner notes at least paras. [00186], [00189]-[00196], [00203]-[00212] of Applicant’s specification, where the invention is described as being implemented with various “suitable” computing devices and components communicating over known computer networks, including various examples of generic computer elements like processors, memory, electronic displays, and user interfaces. Paras. [00113], [00147], [00203] further describe the models as being based on “machine learning” but provide no specific examples of machine learning techniques, leaving one of ordinary skill in the art to understand that any known type of machine learning may be utilized to create the glucose metabolization model. These disclosures do not indicate that the elements of the invention are particular machines, and instead provide generic examples of computer hardware such that one of ordinary skill in the art would understand that any suitable generic electronic device could be used to implement the invention.
Regarding the heart rate monitor and continuous glucose monitor, these elements amount to insignificant extra-solution activity in the form of means of necessary data gathering, as explained above. Further, Examiner notes that it is well-understood, routine, and conventional to obtain heart rate data from a heart rate monitor and glucose levels from a continuous glucose monitor, as evidenced by at least [0071] & [0099]-[0100] of Pauley et al. (US 20210104173 A1); [0032]-[0034] of Wexler et l. (US 20200375549 A1); and [0007] of Dalal et al. (US 20200245913 A1).
The additional elements introduced in the dependent claims are also not sufficient to amount to significantly more than the judicial exception. As explained above, the accessing of logs from a server system by communicating over a network as in claim 14, as well as the use of a recommender system on at least one server system as in claim 17 each amount to instructions to “apply” the exception. Examiner points to the same portions of Applicant’s specification cited above as evidence of the generic nature of the network and server system.
Analyzing these additional elements as an ordered combination adds nothing that is not already present when considering the elements individually; the overall effect of the sensor devices, computing infrastructure, machine learning, and user interface in combination is to digitize and/or automate a diabetes monitoring and management operation that could otherwise be achieved as a certain method of organizing human activity. Further, Examiner notes that the combination of a heart rate monitor, continuous glucose monitor, and computing elements like processors, machine learning, and user interfaces to evaluate patient data and make health predictions and recommendations is well-understood, routine, and conventional, as evidenced by at least Fig. 5, [0065], [0071], [0076], & [0536]-[0539] of Pauley et al. (US 20210104173 A1); Fig. 1, [0030]-[0036], & [0049] of Wexler et al. (US 20200375549 A1); and abstract, Fig. 1, [0007], & [0318]-[0327] of Dalal et al. (US 20200245913 A1).
Thus, when considered as a whole and in combination, claims 1, 3-10, 13-17, and 29 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-10, 13-17, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Pauley et al. (US 20210104173 A1) in view of Hayter et al. (US 20210030323 A1).
Claim 1
Pauley teaches an interactive blood glucose monitoring device for a test subject (Pauley abstract, [0005]), comprising:
a heart rate monitor configured to track heart rate data of the test subject over a duration of time (Pauley [0071], [0100], [0332], noting heart rate data may be logged over time via a heart rate monitor);
a continuous glucose monitor configured to track blood glucose levels of the test subject over the duration of time (Pauley [0099], [0136], noting blood glucose readings may be obtained over time via CGM devices);
computer memory configured to store food consumption data and glucose levels responsive to the food consumption data (Pauley [0071]-[0072], [0539], noting the system includes computer components like memory to implement modules such as event logging module which collects and stores data, such as a food intake and blood glucose values; see also [0178], noting the system can determine when a user’s food intake history (i.e. food log) includes foods that spike the user’s glucose level, showing that the food consumption data is stored along with the glucose levels responsive to the food consumption data);
an electronic display comprising a user interface (Pauley [0065], [0243]-[0244], noting the system includes a device with a display capable of presenting user interfaces);
one or more computer processors operatively coupled to the heart rate monitor, the continuous glucose monitor, and the electronic display, wherein the one or more computer processors are individually or collectively programmed to (Pauley [0536]-[0539], noting the system may be implemented via one or more computer processors executing software modules to achieve the functions of the invention; as shown in Fig. 1, the data processing portion of the system (i.e. the processors) is operatively coupled to the data collection portion (i.e. the heart rate monitor and CGM, as indicated above) and the output portion (i.e. the electronic display, as indicated above)):
(a) perform machine learning to create a glucose metabolization model based on the food consumption data and glucose levels, wherein the glucose metabolization model is configured to predict blood glucose levels in response to at least heart rate data, blood glucose levels, and food consumption data (Pauley [0064], [0076], [0119]-[0132], noting the system may utilize models learned by a machine learning algorithm as glucose prediction models personalized to a given user’s parameters; the models can analyze input data collected by the system (e.g. including food consumption logs, heart rate data, and blood glucose levels as in [0071]) to determine relationships and correlations between the inputs and output data related to a user’s blood glucose (e.g. predicted blood glucose levels as in [0122]) such that they are considered equivalent to a glucose metabolization model);
(b) continuously obtain the heart rate data of the test subject using the heart rate monitor (Pauley [0071], [0100], [0332], noting heart rate data may be logged over time via a heart rate monitor);
(c) continuously receive the blood glucose levels of the test subject using the continuous glucose monitor (Pauley [0005], [0063], [0071]-[0072], noting a glucose value of a patient is received from a sensor, such as a CGM);
(d) determine whether the blood glucose levels of the test subject deviate from a target response (Pauley [0005], [0156], [0178], noting the system determines when the received glucose data exceeds a threshold, i.e. deviates from a target response);
(e) when the blood glucose levels of the test subject deviate from the target response (Pauley [0005], [0178], noting when a glucose value recommendation threshold is passed various actions are taken),
(i) determine a food consumed by the test subject that corresponds to the deviated blood glucose levels (Pauley [0178], [0229], noting the system can determine when a user’s food intake includes foods that spike the user’s glucose level), wherein determining the food consumed by the test subject comprises:
(1) searching food log data of the test subject for a presence of food consumption corresponding to the deviated blood glucose levels (Pauley [0178], noting the system can determine when a user’s food intake history (i.e. food log) includes foods that spike the user’s glucose level), and
(2) (Pauley [0196], noting that if the system detects a lack of food logging (i.e. a possible missed food log event), it may nudge the user to log their food. Such nudges (i.e. automatically generated messages) may be output to a user interface (i.e. via the system communicating with a user device over a computer network as in [0108]) and prompt the user to input the corresponding data type as described in [0194] & Table 3), and
(ii) generate at least one alternative action for the test subject, wherein the at least one alternative action generates a first blood glucose response in the test subject with a reduced deviation from the target response as compared to a second glucose response generated by the test subject consuming the food in absence of the at least one alternative action (Pauley [0005], [0148], [0178], [0187], noting when the glucose value recommendation threshold is passed, a recommendation is provided to a user; per [0007], [0148], [0177], the recommendation can include recommended food substitutions, exercise suggestions, or other actions that correct the blood glucose deviation when compared to taking no action);
(f) generate (i) a first visualization, using the glucose metabolization model, of projected blood glucose levels corresponding to the first glucose response over a duration of time, and (ii) a second visualization of (g) generate an overlay that combines the first visualization of projected blood glucose levels and the second visualization of (Pauley Figs. 20C & 23A, [0267], [0274], [0284], noting graphs of a user’s blood glucose level over time can be displayed for a user, including with predicted glucose levels associated with a suggested action like exercising as in Fig. 19E or eating a particular food as in Fig. 23A as compared with taking no action. See also [0122], noting predicted blood glucose levels may be derived by the glucose insulin meal model (i.e. the glucose metabolization model), indicating that the predicted values represented by the graphs may be generated using the glucose metabolization model); and
(h) output, via the user interface, a user recommendation relating to the at least one alternative action and the overlay combining the first visualization of projected blood glucose levels and the second visualization of (Pauley [0010], [0065], noting the recommended action is communicated to a user via an electronic display; see also Figs. 20C & 23A, [0267], [0274], [0284], noting graphs visualizing at least two blood glucose levels over time in an overlay can be displayed for a user, including with predicted glucose levels associated with a suggested action like exercising as in Fig. 19E or eating a particular food as in Fig. 23A as compared with taking no action).
In summary, Pauley teaches a method that includes prompting/nudging a user to input meal data when the system detects a potentially missed food logging event (Pauley [0194]-[0196]). However, in Pauley, the detection of a missing food event or lack of food logging does not appear to be based on searching a food log and finding no food event corresponding to a glucose event. Accordingly, Pauley fails to explicitly disclose performing the user prompting functions of step (e)(i)(2) specifically under the condition: if the searching does not indicate the presence of food consumption corresponding to the deviated blood glucose data. Further, though Pauley shows that graphs overlaying visualizations of a user’s predicted blood glucose response to certain foods or exercises may be provided (see Figs. 19E, 20C, 23A), as well as that blood glucose trends for a user may be displayed (see Fig. 20C & [0274], indicating that actual measured blood glucose values are displayed), it fails to explicitly disclose overlaying a visualization of predicted blood glucose levels over a duration with a visualization of actual blood glucose levels over the same duration as required by the instant claim.
However, Hayter teaches that a user may be prompted to input a potentially missing meal event that is detected when searching food log data of a subject does not indicate the presence of food consumption corresponding to detected deviated blood glucose data (Hayter Figs. 3A & 4A-E, [0110]-[0112], [0142]-[0144]). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the missed food log event nudging function of Pauley to occur specifically in response to a search of a food log returning a lack of a food log event corresponding to a detected glucose event as in Hayter in order to provide a more objective way to detect a potential missing meal event that is based on a user’s actual blood glucose levels as measured by a sensor (as suggested by Hayter [0110]-[0111]).
Hayter further teaches that two visualizations representing actual blood glucose levels and likely (i.e. projected) blood glucose levels over a period of time may be superimposed on a display for a user (Hayter Fig. 3C, [0128], noting trace 343 of the user’s current blood glucose level (i.e. actual blood glucose level as measured by a continuous analyte monitor as in [0095]) is overlaid with trace 346 of the user’s likely blood glucose level based on the user’s average prior response to similar stimuli (i.e. equivalent to projected blood glucose level)). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the overlaid projected blood glucose level visualizations and separate actual trend visualizations of Pauley such that visualizations of projected and actual blood glucose levels are overlaid in one display as in Hayter in order to provide the user with real-time visual feedback of the likely result of food consumption in the context of current actual blood glucose levels (as suggested by Hayter [0128]).
Claim 3
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 1, and the combination further teaches wherein the at least one alternative action comprises consuming one or more food alternatives, restricting calorie intake, or exercising (Pauley [0007], [0148], [0177], the recommendation can include recommended food substitutions and exercise suggestions).
Claim 4
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 1, and the combination further teaches wherein the at least one alternative action is generated at least in part using a continuous glucose monitor, a score corresponding to a health effect of a food, a food log, an activity log, or a meal component (Pauley [0148], [0178], [0187], noting the recommendations can be based on glucose data from a continuous glucose monitor (as in [0136]) or a rating or grade of the health effect of a food/ingredient/meal component in a food log (as in [0139] & [0148])).
Claim 5
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 1, and the combination further teaches wherein the one or more computer processors are individually or collectively programmed to generate a third visualization of blood glucose levels of the subject over the duration of time; and output, via the user interface, an overlay that combines the first visualization and the third visualization (Pauley Fig. 23A, [0267], [0274], [0280]-[0284], noting a user may be presented with multiple recommended food options and visual representations of predicted glucose responses for each food option vs. the original food type for a period of time, i.e. generating and outputting at least a third visualization for display with the first visualization).
Claim 6
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 1, and the combination further teaches wherein the at least one alternative action comprises consuming at least one alternative food that reduces an elevation in blood glucose levels in the subject upon consumption by the subject as compared to not consuming the at least one alternative food (Pauley [0187], [0204], [0268], noting the suggestion can include alternative foods that are less likely to spike a user’s blood glucose).
Claim 7
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 6, and the combination further teaches wherein the at least one alternative food is a whole wheat alternative food, a cauliflower alternative food, a baked alternative food, or a roasted alternative food (Pauley [0268], noting the food alternative can include whole wheat bread instead of white bread).
Claim 8
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 1, and the combination further teaches wherein the at least one alternative action comprises performing at least one physical activity that reduces an elevation in blood glucose levels in the subject as compared to not performing the at least one physical activity (Pauley Fig. 19F-5, [0187], [0204], noting the suggestion can include physical activity to reduce blood glucose).
Claim 9
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 8, and the combination further teaches wherein the at least one physical activity comprises exercising or fasting (Pauley Fig. 19F-5, [0187], [0204], noting the suggestion can include physical activity like exercise to reduce blood glucose).
Claim 10
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 9, and the combination further teaches wherein the exercising comprises walking or running (Pauley Fig. 19F-5, [0187], [0204], noting the suggestion can include exercising like walking to reduce blood glucose).
Claim 13
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 1, and the combination further teaches wherein generating the at least one alternative action further comprises accessing logs of the subject to determine at least one previous action performed by the subject that reduced an elevation in blood glucose levels (Pauley [0178], [0229], [0254], noting food intake history (i.e. logs of the subject) correlates past actions (i.e. eating a certain food) with patterns in the user’s blood glucose values; such logs and correlations are utilized to make and display alternative action recommendations).
Claim 14
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 13, and the combination further teaches wherein accessing the logs of the subject further comprises communicating over a network with a server system via an application executed by an electronic device (Pauley [0083], noting data in user logs may be accessed by data management engine, which may be embodied via an application running on an electronic device in network communication with a backend platform server per [0067], [0081], & [0108]).
Claim 15
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 1, and the combination further teaches wherein the one or more computer processors are individually or collectively programmed to further:
display, via the user interface, a plurality of alternative actions to the subject (Pauley [0280], noting a user may be presented with multiple recommended food options),
receive from the subject, via the user interface, a selection by the subject of an alternative action from among the plurality of alternative actions (Pauley [0281], noting a user may select at least one of the presented food options), and
display, via the user interface, a predicted blood glucose response for the subject corresponding to the selected alternative action (Pauley [0283]-[0284], noting predicted glucose response may be displayed for each food option, i.e. including the selected food option).
Claim 16
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 1, and the combination further teaches wherein the one or more computer progressors are individually or collectively programmed to further output, via the user interface, a plurality of alternative actions according to projected blood glucose level response for the subject (Pauley Fig. 23B, [0149], [0285], noting food alternative suggestions may be displayed as a ranked list based on the predicted impact on a user’s blood glucose level).
Claim 17
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 1, and the combination further teaches wherein generating the at least one alternative action comprises processing data for the subject by a recommender system on at least one server system (Pauley [0078], noting recommendations can be provided by a recommendation module, which may be embodied on a backend platform server per [0067], [0081], & [0108]).
Claim 29
Pauley in view of Hayter teaches the interactive blood glucose monitoring device of claim 1, and the combination further teaches wherein the one or more computer progressors are individually or collectively programmed to further: determine time-in-range (TIR) data for the test subject, based at least in part on the first or second glucose response over the duration of time; and output, via the user interface, the TIR data for the test subject (Pauley Table 2 on Pg 107, [0187], noting the system may provide notifications relating to time in range for CGM users, indicating that TIR data is determined based on the measured CGM data and output at the user interface as a notification).
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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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/KAREN A HRANEK/ Primary Examiner, Art Unit 3684