Methods and systems for improved blood glucose level monitoring and control

Abstract

A method (100) for adjusting a first biomarker measurement, comprising: receiving (120) a first biomarker measurement from a subject; contemporaneously receiving (130) a time-dependent biomarker measurement from the subject; comparing (140) the time-dependent biomarker measurement to a subject-determined baseline; determining (150), based on the comparison, a time-warp of a baseline first biomarker measurement; and adjusting (160) the baseline first biomarker measurement by the determined amount of the time-warp to generate an adjusted first biomarker measurement.

Description

METHODS AND SYSTEMS FOR IMPROVED BLOOD GLUCOSE LEVEL MONITORING AND CONTROLField of the Disclosure

[0001] The present disclosure is directed generally to methods and systems for adjusting a blood glucose level measurement, and more specifically to improved monitoring and control of blood glucose levels.Background

[0002] Diabetes is a chronic medical condition characterized by elevated levels of glucose in the blood, resulting from the body’s inability to properly produce or use insulin. The prevalence of diabetes, particularly type 2 diabetes, has grown at an alarming rate globally, driven by factors such as sedentary lifestyles, poor dietary habits, and increasing obesity rates. According to recent estimates by the World Health Organization (WHO), over 422 million people worldwide are living with diabetes, with projections showing a continued upward trend. Diabetes is a significant public health concern as it poses a high risk for developing severe complications such as cardiovascular disease, kidney failure, nerve damage, and retinopathy, which may result in blindness. Without effective management, diabetes can lead to a diminished quality of life and premature death.

[0003] Managing diabetes, particularly type 2 diabetes, requires a comprehensive approach involving lifestyle modifications, glucose monitoring, and in many cases, pharmacological interventions. The core of diabetes management is to maintain blood glucose levels within a target range, as defined by healthcare professionals, to prevent both short-term complications like hypoglycemia and hyperglycemia, and long-term health risks. Effective management strategies may include regular blood glucose testing, dietary adjustments focusing on carbohydrate regulation, physical activity, and the administration of medications such as insulin or oral hypoglycemics. Even people without diabetes may have an interest in monitoring their glucose levels. Emerging technologies, such as continuous glucose monitors (CGMs) and insulin pumps, have enhanced the ability of individuals to monitor and control blood sugar levels with greater precision, allowing for personalized treatment regimens that respond dynamically to fluctuations in glucose levels throughout the day.Philips Docket: 2024PF00310

[0004] While measuring blood glucose levels is beneficial to retain control in a reactive manner, it does not directly help the user understand the cause of fluctuations when they occur. Such understanding could help to take not only corrective but also preventive actions to keep blood glucose levels within a desired range.

[0005] One way to enhance insights into blood glucose levels is to compare a blood glucose level measurement to a user-specific baseline that has been established on historical data, and to alert user of blood glucose level deviations with regard to that baseline. Such deviations give insights and could indicate a need for corrective short-term or long-term actions, or could be confirming and rewarding, such as when due to beneficial diet or lifestyle changes. However, comparing to a fixed baseline is sensitive to just small time shifts. Thus, there remains a need for improved systems and methods that simplify the management of diabetes and optimize glucose control for better health outcomes.Summary of the Disclosure

[0006] Accordingly, there is a continued need for methods and systems for improved monitoring and control of blood glucose levels. Various embodiments and implementations herein are directed to a blood glucose monitoring system configured for monitoring a subject’s blood glucose levels. The blood glucose monitoring system receives a BGL measurement from a subject and a time-dependent biomarker measurement from the subject. The system compares the timedependent biomarker measurement to a subject-determined baseline of BGL measurements and time-dependent biomarker measurements, and determines a time-warp of a baseline BGL measurement based on the baselines, where the time-warp aligns the received time-dependent biomarker measurement with an existing time-dependent biomarker measurement pattern identified in the baseline. The baseline BGL measurement is adjusted by the time warp, and the system reports one or more of the BGL measurement, the time-dependent biomarker measurement from the subject, and the adjusted BGL measurement.

[0007] Generally, in one aspect, a method for adjusting a first biomarker measurement is provided. The method includes: (i) receiving a first biomarker measurement from a subject; (ii) receiving a time-dependent biomarker measurement from the subject, wherein the first biomarker is different than the time-dependent biomarker; (iii) comparing the time-dependent biomarkerPhilips Docket: 2024PF00310 measurement to a subject-determined baseline of first biomarker measurements and timedependent biomarker measurements; (iv) determining, based on the comparison, a time-warp of a baseline first biomarker measurement, wherein an amount of the time-warp is determined to align the baseline first biomarker measurement to an existing pattern identified in the subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements; (v) adjusting the baseline first biomarker measurement by the determined amount of the time-warp to generate an adjusted first biomarker measurement; and (vi) reporting one or more of the first biomarker measurement, the time-dependent biomarker measurement from the subject, and the adjusted first biomarker measurement.

[0008] According to an embodiment, the time-dependent biomarker measurement is measured contemporaneously with the first biomarker measurement.

[0009] According to an embodiment, the time-dependent biomarker measurement is one or more of the subject’s heart rate, the subject’s heart rate variability, the subject’s respiration rate, and the subject’s activity.

[0010] According to an embodiment, the determined amount of the time-warp is a forward shift in time.

[0011] According to an embodiment, the determined amount of the time-warp is a backward shift in time.

[0012] According to an embodiment, the method further includes generating the subject- determined baseline of first biomarker measurements and time-dependent biomarker measurements by: (i) obtaining, for a first period of time, a plurality of contemporaneous first biomarker measurements and time-dependent biomarker measurements; and (ii) determining, from the plurality of contemporaneous first biomarker measurements and time-dependent biomarker measurements, a subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements.

[0013] According to an embodiment, the method further includes comparing the adjusted first biomarker measurement to the subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements; and determining, based on the comparison, that the adjusted first biomarker measurement is a deviation from the subject-determined baseline of firstPhilips Docket: 2024PF00310 biomarker measurements and time-dependent biomarker measurements; wherein reporting comprises reporting, via a user interface, the determined deviation of the adjusted first biomarker measurement.

[0014] According to an embodiment, determining the time-warp of the first biomarker measurement further comprises determining the amount of the time-warp to align the received first biomarker measurement with an existing time-dependent first biomarker measurement pattern identified in the subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements.

[0015] According to an embodiment, the first biomarker is a blood glucose level (BGL), and the method further comprises adjusting, based on the reporting, a blood sugar control program. According to an embodiment, adjusting a blood sugar control program comprises adjusting one or more of dietary intake, fine-tuning an insulin or oral medication schedule, and adjusting an activity level.

[0016] According to another aspect is a method for adjusting a blood glucose level (BGL) measurement. The method includes: (i) receiving a BGL measurement from a subject; (ii) receiving a time-dependent biomarker measurement from the subject, wherein the time-dependent biomarker measurement is measured contemporaneously with the first biomarker measurement; (iii) comparing the time- dependent biomarker measurement to a subject-determined baseline of BGL measurements and time-dependent biomarker measurements; (iv) determining, based on the comparison, a time-warp of a baseline BGL measurement, wherein an amount of the time-warp is determined to align the baseline BGL measurement to an existing pattern identified in the subject- determined baseline of BGL measurements and time-dependent biomarker measurements; (v) adjusting the baseline BGL measurement by the determined amount of the time-warp to generate an adjusted BGL measurement; and (vi) reporting one or more of the BGL measurement, the timedependent biomarker measurement from the subject, and the adjusted BGL measurement.

[0017] According to another aspect is a first biomarker monitoring system. The system includes: a first biomarker monitor configured to obtain a first biomarker measurement from a subject; a time-dependent biomarker monitor configured to obtain a time-dependent biomarker measurement from a subject; a processor configured to: (i) receive a first biomarker measurement and a time-dependent biomarker measurement, wherein the first biomarker is different than thePhilips Docket: 2024PF00310 time-dependent biomarker; (ii) compare the time-dependent biomarker measurement to a subject- determined baseline of first biomarker measurements and time-dependent biomarker measurements; (iii) determine, based on the comparison, a time-warp of a baseline first biomarker measurement, wherein an amount of the time-warp is determined to align the baseline first biomarker measurement to an existing pattern identified in the subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements; and (iv) adjust the baseline first biomarker measurement by the determined amount of the time-warp to generate an adjusted first biomarker measurement; and a user interface configured to report one or more of the first biomarker measurement, the time-dependent biomarker measurement from the subject, and the adjusted first biomarker measurement.

[0018] According to an embodiment, the time-dependent biomarker measurement is measured contemporaneously with the first biomarker measurement.

[0019] According to an embodiment, the first biomarker is a blood glucose level (BGL).

[0020] According to an embodiment, the time-dependent biomarker measurement is one or more of the subject’s heart rate, the subject’s heart rate variability, the subject’s respiration rate, and the subject’s activity.

[0021] According to an embodiment, wherein the processor is further configured to generate the subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements by: (1) obtaining, for a first period of time, a plurality of contemporaneous first biomarker measurements and time-dependent biomarker measurements; and (2) determining, from the plurality of contemporaneous first biomarker measurements and time-dependent biomarker measurements, a subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements.

[0022] According to an embodiment, the processor is further configured to: compare the adjusted first biomarker measurement to the subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements; and determine, based on the comparison, that the adjusted first biomarker measurement is a deviation from the subject- determined baseline of first biomarker measurements and time-dependent biomarker measurements; wherein reporting comprises reporting, via a user interface, the determined deviation of the adjusted first biomarker measurement.Philips Docket: 2024PF00310

[0023] It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein. It should also be appreciated that terminology explicitly employed herein that also may appear in any disclosure incorporated by reference should be accorded a meaning most consistent with the particular concepts disclosed herein.

[0024] These and other aspects of the various embodiments will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.Brief Description of the Drawings

[0025] In the drawings, like reference characters generally refer to the same parts throughout the different views. The figures showing features and ways of implementing various embodiments and are not to be construed as being limiting to other possible embodiments falling within the scope of the attached claims. Also, the drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the various embodiments.

[0026] FIG. 1 is a flowchart of a method for blood glucose level monitoring, in accordance with an embodiment.

[0027] FIG. 2 is a schematic representation of a blood glucose level monitoring system, in accordance with an embodiment.

[0028] FIG. 3 is a flowchart of a method for blood glucose level monitoring, in accordance with an embodiment.

[0029] FIG. 4 is a graph of a subject-specific baseline generated by a blood glucose level monitoring system for a subject, in accordance with an embodiment.

[0030] FIG. 5 is a graph of a subject-specific biomarker baseline generated by the blood glucose monitoring system for a subject, with BGL measurements and time-dependent biomarker measurements, in accordance with an embodiment.Philips Docket: 2024PF00310

[0031] FIG. 5A depicts line graph representation 500A as one example of a relationship between ascending slopes for a digestion BGL(t) signal, a longer duration stress BGL(t) signal, and an acute stress BGL(t) signal, according to an embodiment of the present invention;

[0032] FIG. 5B depicts line graph representation 500B of an example of a time-shifted relationship between descending slopes for a digestion BGL(t) signal, a longer duration stress BGL(t) signal, and an acute stress BGL(t) signal, according to an embodiment of the present invention;

[0033] FIG. 6 is an illustrative embodiment depicting a digestion BGL(t) signal in BGL(t) line graph 600 including a first ‘must pass through’ zone for the ascending flank, a second ‘must pass through’ zone for the descending flank, and an exclusion zone, according to an embodiment of the present invention;

[0034] FIG. 7 is an illustrative embodiment depicting a longer duration stress BGL(t) signal in BGL(t) line graph 700 including an ascending flank, a descending flank, and an exclusion zone, according to an embodiment of the present invention;

[0035] FIG. 8A depicts BGL(t) line graph 800 A, which includes a digestion BGL(t) signal followed by a longer duration stress BGL(t) signal over consecutive timespans, according to an embodiment of the present invention;

[0036] FIG. 8B depicts comparison graph 800B in which heart rate variability is considered when analyzing the cause of blood glucose level variability, according to an embodiment of the present invention;

[0037] FIG. 8C depicts comparison graph 800C in which heart rate variability is considered when analyzing the cause of blood glucose level variability, according to an embodiment of the present invention;

[0038] FIG. 9 A depicts a block diagram of components of server computer 308 within the distributed data processing environment 300A of FIG. 3 A, in accordance with one embodiment of the present invention; andPhilips Docket: 2024PF00310

[0039] FIG. 9B depicts a block diagram of components of client computing device 304B within the data processing environment 300B of FIG. 3B, in accordance with one embodiment of the present invention.Detailed Description of Embodiments

[0040] The present disclosure describes various embodiments of a system and method configured for adjusting blood glucose level measurements using a time-warp determination. More generally, Applicant has recognized and appreciated that it would be beneficial to provide methods and systems for improved monitoring and control of blood glucose levels. Accordingly, a blood glucose measurement system receives a BGL measurement from a subject and a time-dependent biomarker measurement from the subject. The system compares the time-dependent biomarker measurement to a subject-determined baseline of BGL measurements and time-dependent biomarker measurements, and determines a time-warp of a baseline BGL measurement based on the baselines, where the time-warp aligns the received time-dependent biomarker measurement with an existing time-dependent biomarker measurement pattern identified in the baseline. The baseline BGL measurement is adjusted by the time warp, and the system reports one or more of the BGL measurement, the time-dependent biomarker measurement from the subject, and the adjusted BGL measurement.

[0041] According to an embodiment, the systems and methods described or otherwise envisioned herein can, in some non-limiting embodiments, be implemented as a component of or an extension of a blood glucose monitoring or control system, as an element of a commercial product for blood glucose monitoring or control, or any suitable system. However, the disclosure is not limited to these devices or systems, and thus disclosure and embodiments disclosed herein can encompass any system that may utilize or benefit from the analysis described or otherwise envisioned herein. The present invention may cover the use of any detectable biomarkers using invasive and / or non-invasive techniques. For example, biomarkers may include glucose, lactate, cortisol, melatonin, and any other biomarkers.

[0042] Referring to FIG. 1, in one embodiment is a flowchart of a method 100 for adjusting a blood glucose level (BGL) measurement using a blood glucose monitoring system. The methods described in connection with the figures are provided as examples only, and shall be understood to not limit the scope of the disclosure. The blood glucose monitoring system can be any of thePhilips Docket: 2024PF00310 systems described or otherwise envisioned herein. The blood glucose monitoring system can be a single system or multiple different systems.

[0043] At step 110 of the method, a blood glucose monitoring system 200 is provided. Referring to an embodiment of a blood glucose monitoring system 200 as depicted in FIG. 2, for example, the system comprises one or more of a processor 220, memory 230, user interface 240, communications interface 250, and storage 260, interconnected via one or more system buses 212. It will be understood that FIG. 2 constitutes, in some respects, an abstraction and that the actual organization of the components of the system 200 may be different and more complex than illustrated. Additionally, blood glucose monitoring system 200 can be any of the systems described or otherwise envisioned herein. Other elements and components of blood glucose monitoring system 200 are disclosed and / or envisioned elsewhere herein.

[0044] According to an embodiment, the blood glucose monitoring system 200 is or comprises one or more blood glucose monitors 270, as described or otherwise envisioned herein. According to an embodiment, the one or more blood glucose monitors 270 can comprise a fingerstick glucose monitor (i.e., a traditional blood glucose meter), a continuous glucose monitor (CGM), a flash glucose monitor, an implantable glucose monitor, a non-invasive glucose monitor, and / or any other system or method for measuring blood glucose levels.

[0045] According to an embodiment, the blood glucose monitoring system 200 is or comprises one or more time-dependent biomarker sensors or monitors 280, as described or otherwise envisioned herein. According to an embodiment, the time-dependent biomarker can be, for example, one or more of a subject’s heart rate, subject’s heart rate variability, subject’s respiration rate, subjects blood pressure, and subject’s activity level or motion / movement, among many other possible time-dependent biomarkers. Thus, the time- dependent biomarker sensor or monitor 280 can be a heart rate sensor or monitor, a respiration sensor or monitor, a blood pressure sensor or monitor, and / or a motion or movement sensor or monitor, among many other types of timedependent biomarker sensors or monitors 280.

[0046] At step 112 of the method, the blood glucose monitoring system generates a subject- determined baseline of blood glucose level (BGL) measurements and time-dependent biomarker measurements, which will be utilized by the system for time warping new BGL measurements. According to an embodiment, a BGL measurement is any measurement of a subject’s bloodPhilips Docket: 2024PF00310 glucose level, either an actual measurement or an estimate. According to an embodiment, a BGL measurement is obtained using any of the methods or systems described or otherwise envisioned herein. According to an embodiment, a time-dependent biomarker measurement is any measurement of any subject biomarker that may or can vary over time. Examples of timedependent biomarkers include a subject’s heart rate, subject’s heart rate variability, subject’s respiration rate, subjects blood pressure, and subject’s activity level or motion / movement, among many other possible time-dependent biomarkers.

[0047] To generate a baseline of BGL measurements and time-dependent biomarker measurements, the system obtains a plurality of BGL measurements and time-dependent biomarker measurements over a first time period. The number of measurements comprising the plurality can depend on a variety of factors, including but not limited to the subject, the desired baseline, the availability of measurements, and many other measurements. Lor example, the measurements can be obtained throughout the course of the first time period, at intervals. The intervals can be minutes or hours, and can be regular or irregular. The first time period can be any period including days, weeks, or months. The first time period can be any period of time required or sufficient to generate a suitable baseline, where a suitable baseline is a baseline that is capable of being utilized for comparison time warping, as described or otherwise envisioned herein. Once obtained, the plurality of measurements may be utilized immediately, or may be stored in local or remote storage for use in further steps of the method.

[0048] To further generate a baseline of BGL measurements and time-dependent biomarker measurements, the system determines a subject-determined baseline of BGL measurements and time-dependent biomarker measurements. The baseline may be generated from the measurements by averaging or percentile filtering, or according to known methods for generating a baseline of subject measurements over time. Once generated, the baseline may be utilized immediately, or may be stored in local or remote storage for use in further steps of the method.

[0049] At step 120 of the method, the blood glucose monitoring system receives a BGL measurement from the subject, and importantly this subject is the same subject for which the baseline was generated. According to an embodiment, the received BGL measurement is any measurement of a subject’s blood glucose level, either an actual measurement or an estimate. According to an embodiment, a BGL measurement is obtained using any of the methods or systemsPhilips Docket: 2024PF00310 described or otherwise envisioned herein. Once received, the blood glucose level measurement may be utilized immediately, or may be stored in local or remote storage for use in further steps of the method.

[0050] According to an embodiment, the blood glucose monitoring system receives a timedependent biomarker measurement from the subject, where this subject is the same subject for which the baseline was generated and from which the BGL measurement was obtained. According to an embodiment, the time-dependent biomarker measurement is obtained and / or received contemporaneously with the BGL measurement. Contemporaneously can mean that the measurements are obtained at exactly the same time, or approximately the same time. Thus, the measurements can be obtained at the same moment, or within several moments or minutes of each other, possibly more than a few minutes depending on the needs or design of the system or method. Once received, the time-dependent biomarker measurement may be utilized immediately, or may be stored in local or remote storage for use in further steps of the method.

[0051] At step 140 of the method, the blood glucose monitoring system 200 compares the timedependent biomarker measurement to the subject-determined baseline of BGL measurements and time-dependent biomarker measurements. According to an embodiment, comparing the timedependent biomarker measurement to the subject-determined baseline of BGL measurements and time-dependent biomarker measurements comprises comparing the time- dependent biomarker measurement taken at a certain timepoint to time-dependent biomarker measurements in the subject- determined baseline at a same or similar timepoint.

[0052] At step 150 of the method, the blood glucose monitoring system 200 determines, based on the comparison in step 140, a time-warp of a baseline BGL measurement is necessary. According to an embodiment, an amount of the time-warp is determined in order to align the baseline BGL measurement to an existing pattern identified in the subject-determined baseline of BGL measurements and time-dependent biomarker measurements. In other words, a determination that a time-warp of the baseline BGL measurement is appropriate or necessary is based on the comparison of the time-dependent biomarker measurement taken at a certain timepoint to one or more time-dependent biomarker measurements in the subject-determined baseline at a same or similar timepoint. The amount of the time-warp of the baseline BGL measurement is an amountPhilips Docket: 2024PF00310 by which the baseline time-dependent biomarker value must be to be shifted in time (ahead or backward) to optimally time-align to the received time-dependent biomarker measurement.

[0053] According to an embodiment, the amount of the time-warp of the baseline BGL measurement is an amount by which the baseline BGL measurement must be shifted in in time (ahead or backward) to optimally time-align. According to another embodiment, the amount of the time-warp of the baseline BGL measurement is an amount by which both the baseline timedependent biomarker value must be to be shifted in time (ahead or backward) to optimally time- align to the received time-dependent biomarker measurement, and / or a baseline BGL measurement must be shifted in in time (ahead or backward) to optimally time-align to the received BGL measurement.

[0054] At step 160 of the method, the blood glucose monitoring system 200 adjusts the baseline BGL measurement by the determined amount of the time-warp, thereby generating an adjusted BGL measurement. Once adjusted, the adjusted BGL measurement may be utilized immediately, or may be stored in local or remote storage for use in further steps of the method.

[0055] At step 170 of the method, the blood glucose monitoring system 200 reports one or more of the received BGL measurement, the received time- dependent biomarker measurement from the subject, and the adjusted BGL measurement to a medical professional, the subject, and / or another person via a user interface of the system. The information may be provided to a user via any mechanism for display, visualization, or otherwise providing information via a user interface. According to an embodiment, the information may be communicated by wired and / or wireless communication to a user interface and / or to another device. For example, the system may communicate the information to a mobile phone, computer, laptop, wearable device, and / or any other device configured to allow display and / or other communication of the report. The user interface can be any device or system that allows information to be conveyed and / or received, and may include a display, a mouse, and / or a keyboard for receiving user commands. As just one nonlimiting example, the user interface may be a component of a glucose blood level monitoring system, or any other system.

[0056] At step 180 of the method, the blood glucose monitoring system 200 compares the adjusted BGL measurement to the subject-determined baseline of BGL measurements and timedependent biomarker measurements. According to an embodiment, the comparison comprisesPhilips Docket: 2024PF00310 comparing the adjusted BGL measurement to BGL measurement(s) in the subject-determined baseline at a timepoint similar to or the same as the adjusted timepoint of the adjusted BGL measurement.

[0057] At step 182 of the method, the blood glucose monitoring system 200 determines, based on the comparison from step 180 of the method, that the adjusted BGL measurement is a deviation from the subject-determined baseline of BGL measurements and time-dependent biomarker measurements. In other words, the comparison reveals that after the adjustment, the adjusted BGL measurement is not as expected for the timepoint based on the subject-determined baseline of BGL measurements. This is a significant improvement compared to prior art methods of blood glucose level monitoring.

[0058] Returning to step 170 of the method, the blood glucose monitoring system 200 reports the determined deviation of the adjusted BGL measurement to a medical professional, the subject, and / or another person via a user interface of the system. The information may be provided to a user via any mechanism for display, visualization, or otherwise providing information via a user interface. According to an embodiment, the information may be communicated by wired and / or wireless communication to a user interface and / or to another device. For example, the system may communicate the information to a mobile phone, computer, laptop, wearable device, and / or any other device configured to allow display and / or other communication of the report. The user interface can be any device or system that allows information to be conveyed and / or received, and may include a display, a mouse, and / or a keyboard for receiving user commands.

[0059] At step 190 of the method, a blood sugar control program being implemented by the subject is adjusted based on the information provided in the report via the user interface, including one or more of the received BGL measurement, the received time- dependent biomarker measurement from the subject, the adjusted BGL measurement, and / or the determined deviation. For example, the determined deviation may identify that an adjustment of the subject’s blood sugar control program is necessary. The adjustment can be a change in one or more factors related to the subject’s blood sugar control program, including but not limited to adjusting one or more of dietary intake, fine-tuning an insulin or oral medication schedule, and adjusting an activity level. For example, the adjustment may comprise raising or lowering an amount of insulin to be taken by the subject, based on the determined deviation.Philips Docket: 2024PF00310

[0060] Referring to FIG. 3, in one embodiment, is a flowchart of a method 300 for adjusting a blood glucose level (BGL) measurement using a blood glucose monitoring system. The methods described in connection with the figures are provided as examples only, and shall be understood to not limit the scope of the disclosure. The blood glucose monitoring system can be any of the systems described or otherwise envisioned herein. The blood glucose monitoring system can be a single system or multiple different systems.

[0061] At step 310 of the method, a blood glucose monitoring system 200 obtains one or more time-dependent biomarker measurements from the subject, and one or more BGL measurements from the subject. According to an embodiment, the received BGL measurement is any measurement of a subject’s blood glucose level, either an actual measurement or an estimate. According to an embodiment, a BGL measurement is obtained using any of the methods or systems described or otherwise envisioned herein. Once received, the blood glucose level measurement may be utilized immediately, or may be stored in local or remote storage for use in further steps of the method. According to an embodiment, the time-dependent biomarker measurement is obtained and / or received contemporaneously with the BGL measurement. Contemporaneously can mean that the measurements are obtained at exactly the same time, or approximately the same time. Thus, the measurements can be obtained at the same moment, or within several moments or minutes of each other, possibly more than a few minutes depending on the needs or design of the system or method. Once received, the time-dependent biomarker measurement may be utilized immediately, or may be stored in local or remote storage for use in further steps of the method.

[0062] At step 320 of the method, which can occur before or after step 310, the blood glucose monitoring system 200 determines a subject-specific baseline for time-dependent biomarker measurements from the subject, and BGL measurements from the subject. The subject-specific baseline can be determined by, for example, averaging or percentile filtering, among other methods, and can be determined over multiple periods of a certain recurring nature (e.g., day, week, year, etc.).

[0063] At step 330 of the method, the system compares the one or more time-dependent biomarker measurements obtained from the subject (and optionally the one or more BGL measurements obtained from the subject) to the determined subject-specific baseline, thusPhilips Docket: 2024PF00310 determining a time-warp necessary for the one or more BGL measurements obtained from the subject.

[0064] According to an embodiment, any of the one or more time-dependent biomarker measurements are compared to the subject-specific baseline, and time- warping is determined, being the amount by which a certain baseline biomarker value needs to be shifted in time (ahead or backward) to optimally time-align to current biomarker value(s). According to an embodiment, the interpretation of the warp amount is that it shows how much a current measurement or pattern is off with respect to “normal” or baseline measurements or patterns, and can thus optionally be targeting a best fit after warping. According to an embodiment, by repeating this for all biomarkers and combining (e.g., averaging) the various warp amounts, one single warp amount is output. According to an embodiment, , the warp amount can be represented as a vector with each entry corresponding to a moment in time (e.g., one entry = one value per clock minute) and containing a positive or negative time shift value to apply on a BGL baseline value.

[0065] According to an embodiment, the time-dependent biomarker measurements - just like BGL measurements, contain information about life patterns. For example, periods of sleep, resting, eating, and / or sporting, among other things, may be recognizable in the data of the various biomarkers, and multiple biomarkers together may paint a more accurate timeline picture than any one by itself. According to an embodiment, the warping amount can be bound by some criteria - for example it should not exceed some smaller portion of the recurring period of interest (e.g., a few hours in case a daily baseline is used), among other possible criteria and adjustment.

[0066] According to an embodiment, the timeline warp amount is determined solely on the basis of biomarkers other than BGL. In a second optional implementation, BGL and baseline BGL are also included themselves in the warp amount determination. Which of the two implementations to use might depend on accuracy and complexity considerations.

[0067] At step 340 of the method, the BGL is warped with the determined time-warp amount, thereby generating an adjusted BGL measurement. Once adjusted, the adjusted BGL measurement may be utilized immediately, or may be stored in local or remote storage for use in further steps of the method.

[0068] At step 350 of the method, the blood glucose monitoring system 200 compares the adjusted BGL measurement to the subject-determined baseline of BGL measurements and time-Philips Docket: 2024PF00310 dependent biomarker measurements. According to an embodiment, the comparison comprises comparing the adjusted BGL measurement to BGL measurement(s) in the subject-determined baseline at a timepoint similar to or the same as the adjusted timepoint of the adjusted BGL measurement. The system determines, based on the comparison, that the adjusted BGL measurement is a deviation from the subject-determined baseline of BGL measurements and timedependent biomarker measurements. In other words, the comparison reveals that after the adjustment, the adjusted BGL measurement is not as expected for the timepoint based on the subject- determined baseline of BGL measurements.

[0069] According to an embodiment, the baseline BGL measurement is adjusted to achieve the adjusted BGL measurement. According to another embodiment, a received BGL measurement is adjusted to achieve the adjusted BGL measurement.

[0070] According to an embodiment, methods 100 and 300 as described herein are utilized to adjust a BGL measurement. However, methods 100 and 300 can be utilized for any biomarker.

[0071] Referring to FIG. 2 is a schematic representation of a blood glucose monitoring system 200. System 200 may be any of the systems described or otherwise envisioned herein, and may comprise any of the components described or otherwise envisioned herein. It will be understood that FIG. 2 constitutes, in some respects, an abstraction and that the actual organization of the components of the system 200 may be different and more complex than illustrated.

[0072] According to an embodiment, system 200 comprises a processor 220 capable of executing instructions stored in memory 230 or storage 260 or otherwise processing data to, for example, perform one or more steps of the method. Processor 220 may be formed of one or multiple modules. Processor 220 may take any suitable form, including but not limited to a microprocessor, microcontroller, multiple microcontrollers, circuitry, field programmable gate array (FPGA), application-specific integrated circuit (ASIC), a single processor, or plural processors.

[0073] Memory 230 can take any suitable form, including a non-volatile memory and / or RAM. The memory 230 may include various memories such as, for example LI, L2, or L3 cache or system memory. As such, the memory 230 may include static random access memory (SRAM), dynamic RAM (DRAM), flash memory, read only memory (ROM), or other similar memory devices. The memory can store, among other things, an operating system. The RAM is used by thePhilips Docket: 2024PF00310 processor for the temporary storage of data. According to an embodiment, an operating system may contain code which, when executed by the processor, controls operation of one or more components of system 200. It will be apparent that, in embodiments where the processor implements one or more of the functions described herein in hardware, the software described as corresponding to such functionality in other embodiments may be omitted.

[0074] User interface 240 may include one or more devices for enabling communication with a user. The user interface can be any device or system that allows information to be conveyed and / or received, and may include a display, a mouse, and / or a keyboard for receiving user commands. In some embodiments, user interface 240 may include a command line interface or graphical user interface that may be presented to a remote terminal via communication interface 250. The user interface may be located with one or more other components of the system, or may located remote from the system and in communication via a wired and / or wireless communications network.

[0075] Communication interface 250 may include one or more devices for enabling communication with other hardware devices. For example, communication interface 250 may include a network interface card (NIC) configured to communicate according to the Ethernet protocol. Additionally, communication interface 250 may implement a TCP / IP stack for communication according to the TCP / IP protocols. Various alternative or additional hardware or configurations for communication interface 250 will be apparent.

[0076] Storage 260 may include one or more machine-readable storage media such as readonly memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash-memory devices, or similar storage media. In various embodiments, storage 260 may store instructions for execution by processor 220 or data upon which processor 220 may operate. For example, storage 260 may store an operating system 261 for controlling various operations of system 200.

[0077] It will be apparent that various information described as stored in storage 260 may be additionally or alternatively stored in memory 230. In this respect, memory 230 may also be considered to constitute a storage device and storage 260 may be considered a memory. Various other arrangements will be apparent. Further, memory 230 and storage 260 may both be considered to be non-transitory machine-readable media. As used herein, the term non-transitory will bePhilips Docket: 2024PF00310 understood to exclude transitory signals but to include all forms of storage, including both volatile and non-volatile memories.

[0078] While system 200 is shown as including one of each described component, the various components may be duplicated in various embodiments. For example, processor 220 may include multiple microprocessors that are configured to independently execute the methods described herein or are configured to perform steps or subroutines of the methods described herein such that the multiple processors cooperate to achieve the functionality described herein. Further, where one or more components of system 200 is implemented in a cloud computing system, the various hardware components may belong to separate physical systems. For example, processor 220 may include a first processor in a first server and a second processor in a second server. Many other variations and configurations are possible.

[0079] According to an embodiment, the system comprises one or more blood glucose monitors 270, as described or otherwise envisioned herein. According to an embodiment, the one or more blood glucose monitors 270 can comprise a fingerstick glucose monitor (i.e., a traditional blood glucose meter), a continuous glucose monitor (CGM), a flash glucose monitor, an implantable glucose monitor, a non-invasive glucose monitor, and / or any other system or method for measuring blood glucose levels.

[0080] According to an embodiment, the blood glucose monitoring system 200 is or comprises one or more time-dependent biomarker sensors or monitors 280, as described or otherwise envisioned herein. According to an embodiment, the time-dependent biomarker can be, for example, one or more of a subject’s heart rate, subject’s heart rate variability, subject’s respiration rate, subjects blood pressure, and subject’s activity level or motion / movement, among many other possible time-dependent biomarkers. Thus, the time-dependent biomarker sensor or monitor 280 can be a heart rate sensor or monitor, a respiration sensor or monitor, a blood pressure sensor or monitor, and / or a motion or movement sensor or monitor, among many other types of timedependent biomarker sensors or monitors 280.

[0081] According to an embodiment, storage 260 of system 200 may store one or more algorithms, modules, and / or instructions to carry out one or more functions or steps of the methods described or otherwise envisioned herein. For example, the system may comprise, among otherPhilips Docket: 2024PF00310 instructions or data, a subject-specific baseline 262, comparison instructions 263, adjustment instructions 264, and / or reporting instructions 265, among other possible instructions.

[0082] According to an embodiment, the subject-specific baseline 262 is a determined baseline of blood glucose level (BGL) measurements and time- dependent biomarker measurements, which will be utilized by the system for time warping new BGL measurements. To generate a baseline of BGL measurements and time-dependent biomarker measurements, the system obtains a plurality of BGL measurements and time-dependent biomarker measurements over a first time period. The number of measurements comprising the plurality can depend on a variety of factors, including but not limited to the subject, the desired baseline, the availability of measurements, and many other measurements. For example, the measurements can be obtained throughout the course of the first time period, at intervals. The intervals can be minutes or hours, and can be regular or irregular. The first time period can be any period including days, weeks, or months. The first time period can be any period of time required or sufficient to generate a suitable baseline, where a suitable baseline is a baseline that is capable of being utilized for comparison time warping, as described or otherwise envisioned herein. Once obtained, the plurality of measurements may be utilized immediately, or may be stored in local or remote storage for use in further steps of the method. Once generated, the baseline may be utilized immediately, or may be stored in local or remote storage for use in further steps of the method.

[0083] According to an embodiment, comparison instructions 263 direct the system to compare the time-dependent biomarker measurement to the subject-determined baseline of BGL measurements and time-dependent biomarker measurements. According to an embodiment, comparing the time-dependent biomarker measurement to the subject-determined baseline of BGL measurements and time-dependent biomarker measurements comprises comparing the timedependent biomarker measurement taken at a certain timepoint to time-dependent biomarker measurements in the subject- determined baseline at a same or similar timepoint. By comparing, the blood glucose monitoring system 200 determines a time-warp of a baseline BGL measurement is necessary. According to an embodiment, an amount of the time-warp is determined in order to align the baseline BGL measurement to an existing pattern identified in the subject-determined baseline of BGL measurements and time-dependent biomarker measurements. In other words, a determination that a time-warp of the baseline BGL measurement is appropriate or necessary is based on the comparison of the time-dependent biomarker measurement taken at a certainPhilips Docket: 2024PF00310 timepoint to one or more time-dependent biomarker measurements in the subject-determined baseline at a same or similar timepoint. The amount of the time-warp of the baseline BGL measurement is an amount by which the baseline time-dependent biomarker value must be to be shifted in time (ahead or backward) to optimally time-align to the received time-dependent biomarker measurement.

[0084] According to an embodiment, adjustment instructions 264 direct the system to adjust the baseline BGL measurement by the determined amount of the time-warp, thereby generating an adjusted BGL measurement. Once adjusted, the adjusted BGL measurement may be utilized immediately, or may be stored in local or remote storage for use in further steps of the method.

[0085] According to an embodiment, reporting instructions 265 direct the system to report one or more of the received BGL measurement, the received time-dependent biomarker measurement from the subject, and the adjusted BGL measurement to a medical professional, the subject, and / or another person via a user interface of the system. The information may be provided to a user via any mechanism for display, visualization, or otherwise providing information via a user interface. According to an embodiment, the information may be communicated by wired and / or wireless communication to a user interface and / or to another device. For example, the system may communicate the information to a mobile phone, computer, laptop, wearable device, and / or any other device configured to allow display and / or other communication of the report. The user interface can be any device or system that allows information to be conveyed and / or received, and may include a display, a mouse, and / or a keyboard for receiving user commands. As just one nonlimiting example, the user interface may be a component of a glucose blood level monitoring system, or any other system.

[0086] Referring to FIG. 4, in one embodiment, is a graph of a subject-specific baseline 262 generated by the blood glucose monitoring system 200 for a subject, comprising a determined baseline of blood glucose level (BGL) measurements and time-dependent biomarker measurements. The baseline of BGL measurements and time-dependent biomarker measurements may comprise one or more components. The subject-specific biomarker baseline 262 is generated for a time period (“time (t)”) which can be any time period such as a day, a week, a month, a year, or any other time period. According to an embodiment (not shown in FIG. 4), the subject-specific biomarker baseline 262 comprises a plurality of these graphs, such as a unique or linked graph forPhilips Docket: 2024PF00310 each hour of the day, a unique or linked graph for each day of the week, a unique or linked graph for each week of the month, a unique or linked graph for each month, and more. The subjectspecific biomarker baseline 262 is generated for the time period using a plurality of subject-specific biomarker measurements received from the subject for the time period (e.g., a day, a week, a month, a year, etc.). The graph shows variation of the biomarker measurements over the time period. In this example, the graph comprises a time-dependent biomarker measurement baseline 430, which may be a median or other conglomeration of the time-dependent biomarker measurements for the time period. The graph further comprises a BGL measurement baseline 440, which may be a median or other conglomeration of the BGL measurements for the time period. The graph also comprises an upper limit 410 and a lower limit 420, although these are optional limits. The upper and lower limits may be related to the BGL measurements, the time-dependent biomarker measurements, or a combination thereof. The upper and lower limits may be determined experimentally, or may be set or chosen by a user, among other selection methods.

[0087] Referring to FIG. 5, in one embodiment, is a graph 500 of a subject-specific biomarker baseline generated by the blood glucose monitoring system 200 for a subject, with BGL measurements and time-dependent biomarker measures, including a BGL measurement baseline 440 and a time-dependent biomarker measurement baseline 430. The graph also shows a plot of received BGL measurements 520 and time-dependent biomarker measurements 510. In an embodiment, the time-dependent biomarker measurements 510 and BGL measurements 520 are received frequently or continuously, and the system graphs the measurements or otherwise compares the measurements to the subject-specific biomarker baseline. For example, a pattern 530 of the time-dependent biomarker measurements 510 is used to determine a time warp 532 with respect to a pattern 431 of the time-dependent biomarker measurement baseline 430. Pattern 431 in the time-dependent biomarker measurement baseline 430 is for instance induced by dinner. This time-warp 532 is then used to determine a suitable time-warp 533 to adjust the BGL biomarker measurement 520. This is advantageous because it allows the determination of the cause of the pattern 540 of the BGL biomarker measurement 520 by determining whether the pattern 540 of the BGL biomarker measurement 520 then aligns with the existing pattern 441 identified in the subject- determined baseline of BGL measurements 440. The system can then adjust the BGL biomarker measurement 520 by the determined amount of the time-warp to generate an adjusted time- warped BGL biomarker measurement.Philips Docket: 2024PF00310

[0088] As the pattern 431 of the time-dependent biomarker measurement baseline 430 is correlated to the pattern 441 in the subject-determined baseline of BGL measurements 440, as both for instance being caused by the person having dinner, determining the time warp from the timedependent biomarker measurements 510 and applying this time warp to the BGL biomarker measurement 520 allows determining the cause of the pattern 540 of the BGL biomarker measurement 520 to be, in this example, the person having had dinner. This match failing indicates another cause for the pattern 540 in the BGL biomarker measurement 520, for instance the pattern 540 being stress induced.

[0089] Instead of starting by determining a time warp 532 of the time-dependent biomarker measurements 510 to determine a suitable time warp 533 for the BGL biomarker measurement 520, it is also possible to start by determining the time warp 533 from the BGL biomarker measurement 520 and from this determine a suitable time warp 532 for the time-dependent biomarker measurements 510. The goal is to be able to use the correlation between the two patterns 431, 441 of the two baselines 430, 440 to establish the cause of the pattern 540 of the BGL biomarker measurement 520, respectively the pattern 530 of the time-dependent biomarker measurements 510. For instance determining that a time warp of an HRV pattern 530, the time warp being derived from the BGL biomarker measurement 520, cause an HRV pattern 530 to align with a pattern 431 of the time-dependent biomarker measurements 510 allows confirmation that the HRV pattern 530 was induced by dinner and not by stress.

[0090] Instead of applying the time warp to the time-dependent biomarker measurements 510 or the BGL biomarker measurement 520, the time warp can equally be applied to either baseline 430, 440.

[0091] By providing the novel and non-obvious blood glucose monitoring system described or otherwise envisioned herein, the system has an enormous positive effect on patient care - specifically biomarker monitoring (such as BGL monitoring) - compared to prior art systems. As just one example, by providing a system that can improve biomarker monitoring, the system can facilitate early and expeditious monitoring and correction of biomarkers in a subject, thereby leading to improved and potentially saved lives.

[0092] Figure 5A depicts line graph representation 500A as one example of a relationship between ascending slopes for digestion BGL(t) signal 502A, longer duration stress BGL(t) signal 502B, and acute stress BGL(t) signal 502C. Figure 5A also depicts threshold 504A andPhilips Docket: 2024PF00310 threshold 504B and signal ascension portion 506. In the depicted embodiment, ascending portion 506 depicts a comparison between the ascending portions of digestion BGL(t) signal 502A, longer duration stress BGL(t) signal 502B, and acute stress BGL(t) signal 502C, each of which have different characteristics. These different characteristics allow the cause of the BGL(t) excursions to be determined. For instance, acute stress causes a rapid increase of the acute stress BGL(t) signal 502C leading to a steeper ascending slope compared to the steepness of the ascending slope of the digestion BGL(t) signal 502A as it takes longer for the metabolic processes of the body to release the glucose into the blood stream. The longer duration stress BGL(t) signal 502B, as it is also caused by stress, has a similar steepness of the ascending slope as the acute stress BGL(t) signal 502C. The steepness of the ascending slope thus allows the distinction to be made between stress induced changes in the BGL(t) signal 502B, 502C and digestion induced changes in the BGL(t) signal 502A.

[0093] In the depicted embodiment, threshold 504A is associated with blood glucose levels falling within hyperglycemic territory and threshold 504B is associated with blood glucose levels falling within hypoglycemic territory.

[0094] In an alternative embodiment, threshold 504A and / or threshold 504B are not used.

[0095] In some embodiments, threshold 504A and / or threshold 504B are set at predetermined values. For example, threshold 504A can be set manually by receiving instructions via a user interface.

[0096] In another embodiment, threshold 504A and / or threshold 504B are set in a periodic manner. For example, threshold 504A and / or threshold 504B may be set every morning at 8AM. In another example, threshold 504A and / or threshold 504B may be set once every hour.

[0097] In another embodiment, threshold 504A and / or threshold 504B are set in at irregular intervals. For example, threshold 504A and / or threshold 504 may be set sporadically based on when disaggregation engine 310 can reliably determine user health parameters based on one or more vital signs sensor readings. In another example, threshold 504A and / or threshold 504B may be set periodically, but disaggregation engine 310 can skip one or more threshold setting time intervals if disaggregation engine 310 cannot reliably determine user health parameters.Philips Docket: 2024PF00310

[0098] In yet another example, threshold 504A and / or threshold 504B are set automatically by receiving program instructions and / or data from one or more hardware components.Hardware components may include, for example, sensors, processors, and memory. For example, threshold 504A may be set based on a reading from an electrochemical blood glucose sensor. However, it is contemplated that any hardware components may be used to set any one or more thresholds.

[0099] In another embodiment, threshold 504A and / or 504B are determined continuously. For example, threshold 504A can be set based on user parameters, such as one or more vital signs. In some examples, threshold 504A can be set in real-time using a continuous polling of data associated with user parameters.

[0100] In another related embodiment, threshold 504A and / or threshold 504B are based on the x-axis. In an example associated with the embodiment depicted in Figure 5A, threshold 504A and / or threshold 504B may be time-based thresholds that determine one or more lengths of time to be considered. In another example, threshold 504A and threshold 504B may be respectively associated with a y-axis-based threshold and an x-axis-based threshold.

[0101] Threshold 504A and threshold 504B are illustrative examples showing the use of one or more thresholds. Any threshold associated with any type of variable and number of variables may be set. Any number of conditions may be placed on a threshold. Any combination of different thresholds may be set using any one or more parameters.

[0102] In some embodiments, thresholds are dynamic and change based on one or more variables, such as time of day. For example, threshold 504A and / or threshold 504B may not be straight lines intersecting the x-axis in an orthogonal relationship. Instead, threshold 504A and / or threshold 504B may be an oscillating line, a stepped line, curved line, or any combination thereof.

[0103] As used herein, real-time may include any latency associated with the limitations of a system, including, but not limited to, the polling rates of one or more devices, the processing of the data, and / or a delay in setting a threshold.Philips Docket: 2024PF00310

[0104] As used herein, continuous may include any operational limitations associated with hardware and / or software in executing a continuous process, including, but not limited to, polling rate latency, CPU latency, communication latency, and / or any other type of operational step required to execute a continuous process.

[0105] As used herein in the context of dynamically determining thresholds, delays may include any delay in setting a threshold for any amount of time for any purpose. For example, disaggregation engine 310 may intentionally delay setting a threshold despite receiving user data in real-time in order to develop a historical data set associated with the vital signs of a user before setting threshold 504A and threshold 504B. In another example, disaggregation engine 310 may intentionally delay setting a threshold until user data is processed using one or more algorithms including, but not limited to, traditional algorithms and machine learning algorithms (described in further detail below). Machine learning models, such as neural networks, may also be used by the invention herein in any manner to execute any one or more operational steps.

[0106] Machine learning includes any technique allowing a computing device to learn from data without being explicitly programmed. Machine learning techniques enable the construction of algorithms to train on data and to make predictions based on that training. As such, machine learning includes non-static computational models that enable data-driven prediction and decision-making. Machine learning can be employed in computing tasks where programming using static algorithms and static computational models would be insufficient and / or impractical to implement.

[0107] Machine learning training can fall under training categories including, but not limited to, supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning. Machine learning may also include any one or more predictive analytical techniques. For example, predictive analytical techniques may include regression, classification, clustering, dimensionality reduction, and density estimation. However, predictive analytical techniques can include any predictive algorithm, set of predictive algorithms, and / or combination of one or more predictive algorithms and one or more traditional algorithms.

[0108] Figure 5B depicts line graph representation 500B of an example of the relationship between descending slopes for digestion BGL(t) signal 502A, longer duration stress BGL(t)Philips Docket: 2024PF00310 signal 502B, and acute stress BGL(t) signal 502C, in which digestion BGL(t) signal 502A, longer duration stress BGL(t) signal 502B, and acute stress BGL(t) signal 502C have been timeshifted relative to each other to align their respective peaks for clarity when comparing descending slopes. In the depicted embodiment, descending portion 508 depicts a comparison between the descending portions of digestion BGL(t) signal 502A, longer duration stress BGL(t) signal 502B, and acute stress BGL(t) signal 502C, each of which have different characteristics.

[0109] As explained for the ascending slope of the BGL(t) signal, the steepness of the descending slope provides information as to the cause of the changes in the BGL(t) signal and thus allows the distinction to be made between stress induced changes in the BGL(t) signal 502B, 502C and digestion induced changes in the BGL(t) signal 502A. A stress induced change in the BGL(t) signal 502B, 502C will exhibit a relative steep descending slope, compared to a digestion induced change in the BGL(t) signal as it takes longer for the metabolic processes of the body to absorb the glucose fromn the blood stream.

[0110] Total duration of the change in the BGL(t) signal can be helpful in determining the cause of the change in the BGL(t) signal but a long duration stress potentially exhibits a similar duration as a digestion and as such the steepness of the ascending and descending slopes of the BGL9T) signal provide more reliable information as to the cause of the change in the BGL(t) signal. Optionally, when determining the cause of the change in the BGL(t) signal weighings can be given to the steepness of the ascending slope, to the steepness of the descending slope and to the duration in order to take the various aspects of the BGL(t) signal into account.

[0111] Figure 6 is an illustrative embodiment depicting digestion BGL(t) signal 502A in BGL(t) line graph 600 including a ‘must pass through’ zone 602 for the ascending flank, a ‘must pass through’ zone 604 for the descending flank, and an exclusion zone 606.

[0112] Waveform characteristics , such as the characteristics of ascending flank 602 and descending flank 604, can be indicative of one or more sources of BGL(t) variability. Characteristics can include any definable feature of a BGL(t) signal waveform. For example, waveform characteristics can include, but are not limited to, short-term variance, long-term variance, average, slope, crest factor, kurtosis, and skewness. In some embodiments, waveform characteristics can use template matching to predict a cause of BGL(t) variability. For example,Philips Docket: 2024PF00310 waveform characteristics can be compared to a user’s historical BGL(t) signal data and associated causes. In another example, waveform characteristics can be compared to a database of historical BGL(t) signal data compiled from different sources. In yet another example, waveform characteristics can be analyzed using machine learning techniques to determine a pattern indicating a particular cause of BGL(t) variability.

[0113] In the depicted embodiment, exclusion zone 606 spans a time period where digestion BGL(t) signal 502A exceeds threshold 504A. It is contemplated that exclusion zone 606 may span any length of time that a BGL(t) signal falls outside limits set by one or more thresholds. Exclusion zone 606 is depicted herein as exceeding an upper blood glucose level threshold (e.g., threshold 504A), but exclusion zone 606 may also apply to BGL(t) signals that dip below a lower blood glucose level threshold (e.g., threshold 504B).

[0114] In some embodiments, a BGL(t) signal may have multiple exclusion zones 606. For example, digestion BGL(t) signal 502A can rise above and a first threshold (e.g., threshold 504A) over a first span of time and fall below a second threshold (e.g., threshold 504B) over a second span of time such that there are two exclusion zones 606. In another illustrative example, digestion BGL(t) signal 502A may be defined by six thresholds, such that exclusion zones 606 fall into one or more bands of exclusions.

[0115] In the depicted embodiment, removing exclusion zone 606 from digestion BGL(t) signal 502A at least partially defines the ascending flank and the descending flank. By doing so, disaggregation engine 310 focuses on the slopes of ascending flank and descending flank to predict the cause of blood glucose levels falling outside of one or more thresholds. The first ‘must pass through’ zone 602 is a zone where the ascending slope must pass through, for the cause of the change in the BGL(t) signal to be most likely a digestion. As such the first ‘must pass through’ zone 602 forms a template for the ascending slope of digestion BGL(t) signal 502 A. As the digestion BGL(t) signal is indeed caused by the digestion of food, a relatively low steepness of the ascending flank falls within the first ‘must pass through’ zone 602 of the template as shown. A second ‘must pass through’ zone 604 can be defined for digestion BGL(t) signals so as to also define a template for the descending slope. The ‘must pass through’ zone 604 is a zone where the descending slope must pass through, for the cause of the change in thePhilips Docket: 2024PF00310BGL(t) signal to be most likely a digestion. Removing exclusion zone 606 from consideration may reduce variability in collected data thereby increasing accuracy. For example, exclusion zone 606 may be highly variable depending on the individual, so removing one or more peaks and / or dips falling into exclusion zone 606 shifts focus to analyzing ascending flanks and descending flanks, which may be more consistent and reliable. As the length of the exclusion zone 606 depends on the duration of the food intake and likewise depends on the duration of the stress, as explained above for Figure 5, the exclusion zone is of limited value. Determining the exclusion zone 606 allows the proper positioning of the second ‘must pass through’ zone 604 for the descending slope, thus allowing the first ‘must pass through’ zone 602 and the second ‘must pass through’ zone 604 to form a flexible template that can be applied to BGL(t) signals of variable duration and allow the cause of the change in the BGL(t) signal to be determined.

[0116] Figure 7 is an illustrative embodiment depicting longer duration stress BGL(t) signal 502B in BGL(t) line graph 700 including ascending flank 702, descending flank 704, and exclusion zone 706.

[0117] In the depicted embodiment, exclusion zone 706 spans a time period where longer duration stress BGL(t) signal 502B exceeds threshold 504A. It is contemplated that exclusion zone 706 may span any length of time that a BGL(t) signal falls outside limits set by one or more thresholds. Exclusion zone 706 is depicted herein as exceeding an upper blood glucose level threshold (e.g., threshold 504A), but exclusion zone 706 may also apply to BGL(t) signals that dip below a lower blood glucose level threshold (e.g., threshold 504B).

[0118] In some embodiments, a BGL(t) signal may have multiple exclusion zones (e.g., exclusion zone 606 and exclusion zone 706). For example, longer duration stress BGL(t) signal 502B can rise above and a first threshold (e.g., threshold 504A) over a first span of time and fall below a second threshold (e.g., threshold 504B) over a second span of time such that there are two exclusion zones 706. In another illustrative example, longer duration stress BGL(t) signal 502B may be defined by six thresholds, such that exclusion zones are defined by one or more bands of exclusion created by the six thresholds.

[0119] In the depicted embodiment, removing exclusion zone 706 from longer duration stress BGL(t) signal 502B at least partially defines the end of the ascending flank and start of thePhilips Docket: 2024PF00310 descending flank. By doing so, disaggregation engine 310 focuses on the slopes of ascending flank and descending flank to predict the cause of blood glucose levels falling outside of one or more thresholds. Removing exclusion zone 706 from consideration may reduce variability in collected data thereby increasing accuracy. For example, exclusion zone 706 may be highly variable depending on the individual, so removing one or more peaks and / or dips falling into exclusion zone 706 shifts focus to analyzing ascending flanks and descending flanks, which may be more consistent and reliable.

[0120] Waveform characteristics of ascending flank 702 and descending flank 704 can be indicative of one or more sources of BGL(t) variability. Characteristics can include any definable feature of a BGL(t) signal waveform. For example, waveform characteristics can include, but are not limited to, short-term variance, long-term variance, average, slope, crest factor, kurtosis, and skewness. In some embodiments, waveform characteristics can use template matching to predict a cause of BGL(t) variability. For example, waveform characteristics can be compared to a user’s historical BGL(t) signal data and associated causes. In another example, waveform characteristics can be compared to a database of historical BGL(t) signal data compiled from different sources. In yet another example, waveform characteristics can be analyzed using machine learning techniques to determine a pattern indicating a particular cause of BGL(t) variability.

[0121] Waveform characteristics, as used herein, can refer to any characteristics of any type of waveform. For example, heart rate variability waveforms may be analyzed using the techniques described herein, such as by analyzing ascending flanks, descending flanks, template matching, machine learning, and any other technique used to analyze waveforms.

[0122] The first ‘must pass through’ zone 702 is a zone where the ascending slope must pass through, for the cause of the change in the BGL(t) signal to be most likely a long duration stress. As such the first ‘must pass through’ zone 702 forms a template for the ascending slope of a long duration stress BGL(t) signal 502B. As the long duration stress BGL(t) signal 502B is indeed caused by the long duration stress, a relatively high steepness of the ascending flank falls within the first ‘must pass through’ zone 702 of the template as shown. A second ‘must pass through’ zone 704 can be defined for long duration stress BGL(t) signals so as to also define a templatePhilips Docket: 2024PF00310 for the descending slope. The second ‘must pass through’ zone 704 is a zone where the descending slope must pass through, for the cause of the change in the BGL(t) signal to be most likely a long duration stress. As the length of the exclusion zone 706 depends on the duration of the stress and likewise as explained above for Figure 5, the exclusion zone 706 is of limited value. Determining the exclusion zone 706 allows the proper positioning of the second ‘must pass through’ zone 704 for the descending slope, thus allowing the first ‘must pass through’ zone 702 and the second ‘must pass through’ zone 704 to form a flexible template that can be applied to BGL(t) signals of variable duration and allow the cause of the change in the BGL(t) signal to be determined.

[0123] As is evident from Figure 6 and Figure 7, the ‘must pass through’ zones of the template differ for the various causes of changes in the BGL(t) signal. When a BGL(t) signal is to be analyzed as to the cause of the changes in the BGL(t) signal, the various templates are applied to the BGL(t) signal and the template that best matches the BGL(t) signal indicates the most likely cause for the changes in the BGL(t) signal.

[0124] Figure 8A depicts BGL(t) line graph 800A. BGL(t) line graph 800A includes digestion BGL(t) signal 502A followed by a longer duration stress BGL(t) signal 502B measured over consecutive timespans where digestion BGL(t) signal 502A spans time frame 802 and longer duration stress BGL(t) signal 502B spans time frame 804. Figure 8A depicts one illustrative example of how different causes (e.g., digestion v. longer duration stress) affect BGL(t) signals differently for the same user. As explained in Figures 5 through 7, the cause of the changes in the BGL(t) signal can be determined based on the steepness of the ascending and descending slopes of the BGL(t) signal and would lead in the case of Figure 8A to a determination of the causes to be first a long duration stress situation followed by a food intake causing digestion to cause the change in the BGL(t) signal.

[0125] Figure 8B depicts comparison graph 800B in which heart rate variability is also taken into consideration when analyzing the cause of blood glucose level variability. Comparison graph 800B shows a heart rate variability (HRV) waveform below a blood glucose level waveform spanning the same time frame.Philips Docket: 2024PF00310

[0126] Time frame 806 spans at least a portion of the waveform where a longer duration stress BGL(t) signal exceeds a blood glucose level threshold and a corresponding portion of an HRV waveform. As depicted, the blood glucose level spike associated with time frame 806 is accompanied with a decrease in heart rate variability. As such, the combination of increased blood glucose and decreased HRV may point to longer duration stress as being the cause of the blood glucose spike. When combining the result of this BGL(t) & HRVdetermination of the cause of the change in the BGL(t) signal with the determination as explained in Figures 5 through 7, based on the steepness of the ascending and / or descending slopes of the BGL(t) signal, a very reliable determination as to the cause can be made. Vice versa, having a reliable determination of the cause of the changes in the BGL(t) signal based on the steepness of the slopes, allows a better interpretation of the HRV signal, i.e. determining that a low HRV value is caused by a stress situation as in time frame 806.

[0127] Time frame 808 spans at least a portion of the waveform that a digestion stress BGL(t) signal exceeds a blood glucose level threshold and a corresponding portion of an HRV waveform. As depicted, the blood glucose level spike associated with time frame 808 is accompanied with a no meaningful change heart rate variability. As such, the combination of increased blood glucose and decreased HRV may point to digestion as being the cause of the blood glucose spike. When combining the result of this BGL(t) & HRV determination of the cause of the change in the BGL(t) signal with the determination as explained in Figures 5 through 7, based on the steepness of the ascending and / or descending slopes of the BGL(t) signal, a very reliable determination as to the cause can be made as is evident in time frame 808 where the result of the determination indicates digestion as the most likely cause of the change in the BGL(t) signal.

[0128] Figure 8C depicts comparison graph 800C in which heart rate variability is also taken into consideration when analyzing the cause of blood glucose level variability. Comparison graph 800C shows a heart rate variability (HRV) waveform below a blood glucose level waveform spanning the same time frame.

[0129] Time frame 810 spans at least a portion of the waveform where a longer duration stress BGL(t) signal exceeds a blood glucose level threshold and a corresponding portion of anPhilips Docket: 2024PF00310HRV waveform. As depicted, the blood glucose level spike associated with time frame 810 is accompanied with a decrease in heart rate variability. As such, the combination of increased blood glucose and decreased HRV may point to longer duration stress as being the cause of the blood glucose spike as explained in Figure 8B.

[0130] HRV waveforms can be analyzed in a manner similar to that of blood glucose level waveforms. For example, disaggregation engine 310 can determine ascending and descending flanks of an HRV waveform based on a heart rate variability threshold to determine the cause of a change in heart rate variability. In another example, disaggregation engine 310 can use template matching to analyze an HRV waveform to determine the cause of a change in heart rate variability. In yet another example, disaggregation engine 310 can use historical data and predictive analytical techniques to predict a cause of a change in heart rate variability.

[0131] Time frame 812 spans at least a portion of the waveform that a BGL(t) signal does not exceed a blood glucose level threshold and a corresponding portion of an HRV waveform. As depicted, the blood glucose levels associated with time frame 806 are stable, but the HRV waveform dips. As such, the combination of stable blood glucose and decreased HRV may point to dehydration being the cause of the dip in HRV rather than a stress induced decrease in HRV as a spike in the BGL(t) signal is absent. So again, the correct interpretation of the BGL(t) signal as to the cause of the spike allows the interpretation of the HRV to be improved. Currently HRV is used to determine stress levels, sleep patterns, fitness, illness, fertility, and other outcomes, often in combination with motion data, temperature data, and other parameters. Combining information derived from the BGL(t) signal with the HRV, as shown here in Figure 8, further improves the interpretation of the HRV. Similarly to blood glucose levels, HRV can be subject to any one or more thresholds, such as HRV dipping below a variability threshold.

[0132] Figure 9A depicts a block diagram of components of server computer 308 within the distributed data processing environment 300A of Figure 3 A, in accordance with one embodiment of the present invention. It should be appreciated that Figure 9A provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments can be implemented. Many modifications to the depicted environment can be made.Philips Docket: 2024PF00310

[0133] Server computer 308 can include processor(s) 904, cache 914, memory 906, persistent storage 908, communications unit 910, input / output (I / O) interface(s) 912 and communications fabric 902. Communications fabric 902 provides communications between cache 914, memory 906, persistent storage 908, communications unit 910, and input / output (I / O) interface(s) 912. Communications fabric 902 can be implemented with any architecture designed for passing data and / or control information between processors (such as microprocessors, communications, and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric 302 can be implemented with one or more buses.

[0134] Memory 906 and persistent storage 908 are computer readable storage media. In this embodiment, memory 906 includes random access memory (RAM). In general, memory 906 can include any suitable volatile or non-volatile computer readable storage media. Cache 914 is a fast memory that enhances the performance of processor(s) 904 by holding recently accessed data, and data near recently accessed data, from memory 906.

[0135] Program instructions and data used to practice embodiments of the present invention, e.g., disaggregation engine 310 and database 312, are stored in persistent storage 908 for execution and / or access by one or more of the respective processor(s) 904 of server computer 308 via cache 914. In this embodiment, persistent storage 908 includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage 908 can include a solid-state hard drive, a semiconductor storage device, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information.

[0136] The media used by persistent storage 908 may also be removable. For example, a removable hard drive may be used for persistent storage 908. Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage 908.

[0137] Communications unit 910, in these examples, provides for communications with other data processing systems or devices, including resources of client computing device 304B. In these examples, communications unit 910 includes one or more network interface cards.Philips Docket: 2024PF00310Communications unit 910 may provide communications through the use of either or both physical and wireless communications links. Disaggregation engine 310, database 312, and other programs and data used for implementation of the present invention, may be downloaded to persistent storage 908 of server computer 308 through communications unit 910.

[0138] I / O interface(s) 912 allows for input and output of data with other devices that may be connected to server computer 308. For example, I / O interface(s) 912 may provide a connection to external device(s) 916 such as a keyboard, a keypad, a touch screen, a microphone, a digital camera, and / or some other suitable input device. External device(s) 916 can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., disaggregation engine 310 and database 312 on server computer 308, can be stored on such portable computer readable storage media and can be loaded onto persistent storage 908 via I / O interface(s) 912. VO interface(s) 912 also connects to a display 918.

[0139] Display 918 provides a mechanism to display data to a user and may, for example, be a computer monitor. Display 918 can also function as a touchscreen, such as a display of a tablet computer. In another example, display 918 can be a display associated with an augmented reality and / or virtual reality environment. In yet another example, display 918 can be associated with a smartwatch. In yet another example, display 918 can be associated with a remotely tethered display.

[0140] Figure 9B depicts a block diagram of components of client computing device 304B within the data processing environment 300B of Figure 3B, in accordance with one embodiment of the present invention.

[0141] Figure 9B contemplates the components described in Figure 9A within one embodiment of a standalone device configured to execute disaggregation engine 310.

[0142] The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, andPhilips Docket: 2024PF00310 thus the invention should not be limited to use solely in any specific application identified and / or implied by such nomenclature.

[0143] The present invention may be a system, a method, and / or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

[0144] The computer readable storage medium can be any tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0145] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from thePhilips Docket: 2024PF00310 network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0146] Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

[0147] Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer readable program instructions.

[0148] These computer readable program instructions may be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing thePhilips Docket: 2024PF00310 functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function / act specified in the flowchart and / or block diagram block or blocks.

[0149] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0150] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, a segment, or a portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0151] The invention herein may also be partially or wholly executed in a cloud computing environment comprising one or more cloud computing nodes. Cloud computing nodes may communicate with each other and may be grouped in one or more networks, such as private,Philips Docket: 2024PF00310 community, public, and / or hybrid cloud networks. Cloud computing environment scan provide infrastructure, platforms, and / or software as services for which a user does not need to maintain resources on a local computing device. A cloud computing environment can communicate with any type of computerized device over any type of network and / or network addressable connection.

[0152] The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The terminology used herein was chosen to best explain the principles of the embodiment, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

[0153] The present invention may be a system, a method, and / or a computer program product. The computer program product may include a non-transitory computer readable storage medium (or media) having computer readable program instructions thereon for causing a system or processor to carry out aspects of the present invention. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the foregoing, among other possibilities. Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the internet, a local area network, and / or a wireless network. Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus, systems, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer readable program instructions.Philips Docket: 2024PF00310

[0154] All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and / or ordinary meanings of the defined terms.

[0155] The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.”

[0156] The phrase “and / or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and / or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and / or” clause, whether related or unrelated to those elements specifically identified.

[0157] As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and / or” as defined above. For example, when separating items in a list, “or” or “and / or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of’ or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.”

[0158] As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified.Philips Docket: 2024PF00310

[0159] It should also be understood that, unless clearly indicated to the contrary, in any methods claimed herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited.

[0160] In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of’ and “consisting essentially of’ shall be closed or semi-closed transitional phrases, respectively.

[0161] While several inventive embodiments have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and / or structures for performing the function and / or obtaining the results and / or one or more of the advantages described herein, and each of such variations and / or modifications is deemed to be within the scope of the inventive embodiments described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and / or configurations will depend upon the specific application or applications for which the inventive teachings is / are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific inventive embodiments described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described and claimed. Inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and / or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and / or methods, if such features, systems, articles, materials, kits, and / or methods are not mutually inconsistent, is included within the inventive scope of the present disclosure.

Claims

Philips Docket: 2024PF00310ClaimsWhat is claimed is:

1. A method (100) for adjusting a first biomarker measurement, comprising: receiving (120) a first biomarker measurement from a subject; receiving (130) a time-dependent biomarker measurement from the subject, wherein the first biomarker is different than the time-dependent biomarker; comparing (140) the time-dependent biomarker measurement to a subject- determined baseline of first biomarker measurements and time-dependent biomarker measurements; determining (150), based on the comparison, a time-warp of a baseline first biomarker measurement, wherein an amount of the time-warp is determined to align the baseline first biomarker measurement to an existing pattern identified in the subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements; adjusting (160) the baseline first biomarker measurement by the determined amount of the time-warp to generate an adjusted first biomarker measurement; and reporting (170) one or more of the first biomarker measurement, the timedependent biomarker measurement from the subject, and the adjusted first biomarker measurement.

2. The method of claim 1, wherein the time-dependent biomarker measurement is measured contemporaneously with the first biomarker measurement.

3. The method of claim 1 , wherein the time-dependent biomarker measurement is one or more of the subject’s heart rate, the subject’s heart rate variability, the subject’s respiration rate, and the subject’s activity.

4. The method of claim 1, wherein the determined amount of the time-warp is a forward shift in time.Philips Docket: 2024PF003105. The method of claim 1, wherein the determined amount of the time-warp is a backward shift in time.

6. The method of claim 1, further comprising the step of generating (112) the subject- determined baseline of first biomarker measurements and time-dependent biomarker measurements by: (i) obtaining, for a first period of time, a plurality of contemporaneous first biomarker measurements and time-dependent biomarker measurements; and (ii) determining, from the plurality of contemporaneous first biomarker measurements and time-dependent biomarker measurements, a subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements.

7. The method of claim 1, further comprising the steps of: comparing (180) the adjusted first biomarker measurement to the subject- determined baseline of first biomarker measurements and time-dependent biomarker measurements; and determining (182), based on the comparison, that the adjusted first biomarker measurement is a deviation from the subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements; wherein reporting (170) comprises reporting, via a user interface, the determined deviation of the adjusted first biomarker measurement.

8. The method of claim 1, wherein determining the time-warp of the first biomarker measurement further comprises determining the amount of the time-warp to align the received first biomarker measurement with an existing time-dependent first biomarker measurement pattern identified in the subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements.

9. The method of claim 1 , wherein the first biomarker is a blood glucose level (BGL), and the method further comprises adjusting (190), based on the reporting, a blood sugar control program, wherein adjusting a blood sugar control program comprises adjusting one or more of dietary intake, fine-tuning an insulin or oral medication schedule, and adjusting an activity level.Philips Docket: 2024PF0031010. A method (100) for adjusting a blood glucose level (BGL) measurement, comprising: receiving (120) a BGL measurement from a subject; receiving (130) a time-dependent biomarker measurement from the subject, wherein the time-dependent biomarker measurement is measured contemporaneously with the first biomarker measurement; comparing (140) the time-dependent biomarker measurement to a subject- determined baseline of BGL measurements and time-dependent biomarker measurements; determining (150), based on the comparison, a time-warp of a baseline BGL measurement, wherein an amount of the time-warp is determined to align the baseline BGL measurement to an existing pattern identified in the subject-determined baseline of BGL measurements and time-dependent biomarker measurements; adjusting (160) the baseline BGL measurement by the determined amount of the time-warp to generate an adjusted BGL measurement; and reporting (170) one or more of the BGL measurement, the time-dependent biomarker measurement from the subject, and the adjusted BGL measurement.

11. A first biomarker monitoring system (200), comprising: a first biomarker monitor (270) configured to obtain a first biomarker measurement from a subject; a time-dependent biomarker monitor (270) configured to obtain a time-dependent biomarker measurement from a subject; a processor configured to: (i) receive a first biomarker measurement and a timedependent biomarker measurement, wherein the first biomarker is different than the timedependent biomarker; (ii) compare the time-dependent biomarker measurement to a subject- determined baseline of first biomarker measurements and time-dependent biomarker measurements; (iii) determine, based on the comparison, a time-warp of a baseline first biomarker measurement, wherein an amount of the time-warp is determined to align the baseline first biomarker measurement to an existing pattern identified in the subject-determined baseline of firstPhilips Docket: 2024PF00310 biomarker measurements and time-dependent biomarker measurements; and (iv) adjust the baseline first biomarker measurement by the determined amount of the time-warp to generate an adjusted first biomarker measurement; and a user interface (240) configured to report one or more of the first biomarker measurement, the time-dependent biomarker measurement from the subject, and the adjusted first biomarker measurement.

12. The system of claim 11, wherein the time-dependent biomarker measurement is measured contemporaneously with the first biomarker measurement.

13. The system of claim 11, wherein the first biomarker is a blood glucose level (BGL), and wherein the time-dependent biomarker measurement is one or more of the subject’s heart rate, the subject’s heart rate variability, the subject’s respiration rate, and the subject’s activity.

14. The system of claim 11 , wherein the processor is further configured to generate the subject- determined baseline of first biomarker measurements and time-dependent biomarker measurements by: (1) obtaining, for a first period of time, a plurality of contemporaneous first biomarker measurements and time-dependent biomarker measurements; and (2) determining, from the plurality of contemporaneous first biomarker measurements and time-dependent biomarker measurements, a subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements.

15. The system of claim 11, wherein the processor is further configured to: compare the adjusted first biomarker measurement to the subject-determined baseline of first biomarker measurements and time-dependent biomarker measurements; and determine, based on the comparison, that the adjusted first biomarker measurement is a deviation from the subject-determined baseline of first biomarker measurements and timedependent biomarker measurements; wherein reporting comprises reporting, via a user interface, the determined deviation of the adjusted first biomarker measurement.

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