Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a health monitor and particularly to a device for non-invasive personalized health monitoring.
Description of Related Art
[002] The growing demand for proactive healthcare arises from the rise in chronic diseases, the aging population, and the need for early detection of health risks. Conventional health monitoring often occurs only in clinical settings or after symptoms appear, that delays preventive measures and reduces effectiveness.
[003] Existing solutions include commercial wearables such as smartwatches that provide heart rate or activity data, continuous glucose monitors designed for diabetic patients, and ECG patches that focus on cardiac health. These solutions deliver useful data streams but address only specific conditions or limited biometrics. These solutions serve as supplementary tools rather than comprehensive health management systems and are primarily reactive in their application.
[004] Despite their usefulness, current solutions fail to provide deep biomarker analysis, integrated multi-parameter monitoring, and predictive insights. They often function as isolated devices, without seamless integration into broader healthcare systems. Their inability to generate real-time alerts based on personalized trends restricts their capacity to prevent complications.
[005] There is thus a need for an improved and advanced device for non-invasive personalized health monitoring that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a device for non-invasive personalized health monitoring. The device comprising a skin-compatible patch adapted to attach to a body surface of a user. The skin-compatible patch comprise an integrated biosensor adapted to detect physiological parameters of the user. The device further comprising a processing unit communicatively connected to the skin-compatible patch and to the integrated biosensor. The processing unit is configured to receive the detected physiological parameters of the user; analyze the received physiological parameters of the user using an artificial intelligence algorithm to identify patterns and health trends; and generate a personalized health metric.
[007] Embodiments in accordance with the present invention further provide a method for non-invasive personalized health monitoring. The method comprising steps of receiving detect physiological parameters of a user; analyzing the received physiological parameters of the user using an artificial intelligence algorithm to identify patterns and health trends; generating a personalized health metric; comparing the personalized health metric against baseline values specific to the user; transmitting alerts to a computing unit when the personalized health metric deviates from baseline values specific to the user.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a device for non-invasive personalized health monitoring.
[009] Next, embodiments of the present application may provide a device that analyzes real-time data to forecast potential health risks before symptoms appear, enabling preventive care rather than reactive treatment.
[0010] These and other advantages will be apparent from the present application of the embodiments described herein.
[0011] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0013] FIG. 1 illustrates a block diagram of a device for non-invasive personalized health monitoring, according to an embodiment of the present invention;
[0014] FIG. 2 illustrates a connectivity of the device for non-invasive personalized health monitoring with a computing unit, according to an embodiment of the present invention;
[0015] FIG. 3 illustrates a structure diagram indicating shortcomings of present solutions, according to an embodiment of the present invention; and
[0016] FIG. 4 depicts a flowchart of a method for non-invasive personalized health monitoring, according to an embodiment of the present invention.
[0017] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0018] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0019] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0020] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0021] As used herein, the term “user” refers to any individual or subject on whom the device is applied for health monitoring. The user may include, but is not limited to, a patient under medical supervision, a healthy individual engaged in wellness tracking, or any person requiring continuous or periodic monitoring of physiological parameters. The term “user” is not limited by age, gender, or health condition and encompasses human subjects in both clinical and non-clinical environments.
[0022] As used herein, the term “medical professional” refers to any qualified individual engaged in the assessment, diagnosis, treatment, or management of health conditions. The medical professional may include, but is not limited to, physicians, surgeons, nurses, dieticians, physiotherapists, or other licensed healthcare practitioners. The term further encompasses specialists and generalists who may utilize the device outputs for preventive, diagnostic, or therapeutic decision-making.
[0023] FIG. 1 illustrates a block diagram of a device 100 for non-invasive personalized health monitoring, according to an embodiment of the present invention. In an embodiment of the present invention, the device 100 may be adapted to continuously track multiple health biomarkers. The device 100 may further be adapted to provide personalized, real-time predictions and alerts. The device 100 may be adapted to operate in a non-invasive manner. The device 100 may employ artificial intelligence to facilitate proactive healthcare management.
[0024] According to the embodiments of the present invention, the device 100 may incorporate non-limiting hardware components to enhance a processing speed and an efficiency, such as the device 100 may comprise a skin-compatible patch 102, an integrated biosensor 104, a processing unit 106, and a communication unit 108. In an embodiment of the present invention, the hardware components of the device 100 may be integrated with computer-executable instructions for overcoming challenges and limitations of the existing devices.
[0025] In an embodiment of the present invention, the skin-compatible patch 102 may be adapted to attach to a body surface of a user. The skin-compatible patch 102 may comprise the integrated biosensor 104 adapted to detect physiological parameters of the user. The physiological parameters are selected from glucose levels, hydration levels, heart rate, stress markers, and so forth. In certain embodiments of the present invention, the patch 102 may be flexible, biocompatible, and designed for prolonged wear without causing discomfort. The integrated biosensor 104 may further be configured to wirelessly transmit the detected physiological parameters to the processing unit 106 for real-time analysis.
[0026] In an embodiment of the present invention, the processing unit 106 may be connected to the skin-compatible patch 102 and to the integrated biosensor 104. The processing unit 106 may be configured to receive the detected physiological parameters of the user. The processing unit 106 may be configured to analyze the received physiological parameters of the user using an artificial intelligence algorithm to identify patterns and health trends. The artificial intelligence algorithm may comprise machine learning models trained to predict potential health risks. The processing unit 106 may be configured to calculate baseline values of the physiological parameters specific to the user during a calibration phase and adaptively update the baseline values over time.
[0027] In one embodiment of the present invention, when the device 100 is first applied to a user, the skin-compatible patch 102 and the integrated biosensor 104 may continuously collect physiological parameters such as glucose, hydration, heart rate, and stress markers for a defined period, for example, 5 to 7 days. The processing unit 106 may average the collected data during this phase and establish user-specific baseline values for each parameter. In certain implementations, the baseline values are further segmented according to circadian rhythms, including morning, afternoon, and night cycles.
[0028] In another embodiment of the present invention, the processing unit 106 may be configured to dynamically update baseline values of the physiological parameters over time. The artificial intelligence algorithm within the processing unit 106 applies rolling averages, variance analysis, and trend recognition to refine the baseline values. For instance, when a user improves physical fitness, resulting in a reduced resting heart rate, the baseline heart rate automatically recalibrates to reflect the new physiological condition.
[0029] In an embodiment of the present invention, the baseline values may be context-dependent rather than fixed single values. The processing unit 106 may maintain multiple baseline ranges corresponding to different user states, such as sleep, rest, or physical activity. The device 100 may determine the relevant state using contextual information obtained from motion sensors or time-of-day logs and select the appropriate baseline values accordingly.
[0030] In another embodiment of the present invention, a computing unit 200 (as shown in FIG. 2) may integrate historical medical data associated with the user. Such data may include electronic medical records or information entered manually by a medical professional. The processing unit 106 may employ pre-existing clinical ranges as initial baseline values and subsequently refine them with live sensor data from the integrated biosensor 104.
[0031] In a further embodiment of the present invention, when individual historical data may be limited or unavailable, the processing unit 106 may initialize baseline values using population benchmark data categorized by factors such as age, gender, body mass index, or lifestyle group. As the device 100 continues to collect physiological data specific to the user, the population-derived baselines progressively may shift into personalized baseline values.
[0032] The processing unit 106 may be configured to generate a personalized health metric. The personalized health metrics may comprise recommendations related to hydration, nutrition, exercise, medication management, and so forth. The processing unit 106 may be configured to compare the personalized health metric against baseline values specific to the user. The processing unit 106 may be configured to transmit alerts to a computing unit 200 and the personalized health metric when the personalized health metric deviates from baseline values specific to the user.
[0033] In an embodiment of the present invention, the communication unit 108 may be adapted to transmit the alerts and the personalized health metric to the computing unit 200. The communication unit 108 may be a Wireless Fidelity (Wi-Fi) based Internet of Things (IoT) enabled unit.
[0034] In one exemplary embodiment of the present invention, the device 100 may be applied to a user X, of 45-year-old individual with a family history of diabetes and hypertension. The user may attach the skin-compatible patch 102 to his upper arm before beginning his day. In another embodiment, the skin-compatible patch 102 may be adapted to be worn over a thin layer of clothing on the arm, while still maintaining an accuracy of measurement. In further embodiments of the present invention, the patch 102 may also be positioned on alternative body surfaces such as the wrist, abdomen, or chest, depending on user preference or medical requirement. The skin-compatible patch 102 may comprise the integrated biosensor 104 that may begin to measure glucose level, hydration level, heart rate, and stress markers continuously throughout the day.
[0035] The integrated biosensor 104 may transmit detected physiological parameters to the processing unit 106. The processing unit 106 may analyze the parameters using an artificial intelligence algorithm trained on large sets of medical data. In the morning, the device 100 may observe that the hydration level of the user is below baseline. The processing unit 106 may generate a personalized health metric recommending fluid intake and transmit this recommendation through the communication unit 108 to the computing unit 200, that may be a smartphone carried by the user. The smartphone may display a notification reminding the user to drink water.
[0036] Later in the afternoon, the processing unit 106 may identify a rising glucose trend compared with stored baseline values. The algorithm forecasts a potential hyperglycemic condition if the trend continues. The processing unit 106, therefore, may generate a personalized alert and transmit the generated personalized alert via the communication unit 108 to the computing unit 200. The user may immediately receive a warning, and/or exercise and dietary recommendations on a smartphone of the user X.
[0037] The personalized health metrics and the alerts may further be accessible to the medical professional through the same computing unit 200. This may enable proactive adjustment of a treatment plan without waiting for symptoms to appear.
[0038] FIG. 2 illustrates a connectivity of the device 100 for non-invasive personalized health monitoring with the computing unit 200, according to an embodiment of the present invention. The computing unit 200 may be an electronic device 100 adapted to be used by the user and/or a medical professional. The computing unit 200 may enable the user to receive the alerts and the personalized health metric.
[0039] FIG. 3 illustrates a structure diagram 300 indicating shortcomings of present solutions, according to an embodiment of the present invention. In an embodiment of the present invention, conventional manual health monitoring systems, such as smartwatches and single-biomarker sensors are restricted to monitoring a limited number of biomarkers, which frequently results in missed detection of critical biomarker variations. These limitations consequently lead to missed opportunities for timely medical intervention.
[0040] Further, conventional systems operate primarily on reactive alerts without offering predictive insights. As a result, users receive notifications only after abnormal conditions occur, thereby eliminating the possibility of early warnings. The absence of integrated predictive analytics restricts proactive healthcare management and fails to identify risks in advance. The combination of these deficiencies, limited biomarker coverage, lack of predictive insights, and reliance on reactive alerts, ultimately contributes to suboptimal patient outcomes.
[0041] FIG. 4 depicts a flowchart of a method 400 for non-invasive personalized health monitoring, according to an embodiment of the present invention.
[0042] At step 402, the device 100 may receive the detected physiological parameters of the user.
[0043] At step 404, the device 100 may analyze the received physiological parameters of the user using the artificial intelligence algorithm to identify patterns and health trends.
[0044] At step 406, the device 100 may generate the personalized health metric.
[0045] At step 408, the device 100 may compare the personalized health metric against baseline values specific to the user. Upon comparison, if the personalized health metric deviates from the baseline values specific to the user, then the method 400 may proceed to a step 410. Else, the method 400 may revert to the step 402.
[0046] At step 410, the device 100 may transmit the alerts and the personalized health metric to the computing unit 200.
[0047] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0048] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A device (100) for non-invasive personalized health monitoring, the device (100) comprising:
a skin-compatible patch (102) adapted to attach to a body surface of a user, wherein the skin-compatible patch (102) comprise an integrated biosensor (104) adapted to detect physiological parameters of the user; and
a processing unit (106) communicatively connected to the skin-compatible patch (102) and to the integrated biosensor (104), characterized in that the processing unit (106) is configured to:
receive the detected physiological parameters of the user;
analyze the received physiological parameters of the user using an artificial intelligence algorithm to identify patterns and health trends;
calculate baseline values of the physiological parameters specific to the user during a calibration phase and adaptively update the baseline values over time; and
generate a personalized health metric.
2. The device (100) as claimed in claim 1, wherein the processing unit (106) is configured to transmit the personalized health metric to a computing unit (200) via a communication unit (108).
3. The device (100) as claimed in claim 1, wherein the processing unit (106) is configured to compare the personalized health metric against baseline values specific to the user.
4. The device (100) as claimed in claim 1, wherein the processing unit (106) is configured to transmit alerts to a computing unit (200) when the personalized health metric deviates from baseline values specific to the user.
5. The device (100) as claimed in claim 1, wherein the physiological parameters are selected from glucose levels, hydration levels, heart rate, stress markers, or a combination thereof.
6. The device (100) as claimed in claim 1, wherein the artificial intelligence algorithm comprises machine learning models trained to predict potential health risks.
7. The device (100) as claimed in claim 1, wherein the personalized health metrics comprise recommendations related to hydration, nutrition, exercise, medication management, or a combination thereof.
8. A method (400) for non-invasive personalized health monitoring, the method (400) is characterized by steps of:
receiving detected physiological parameters of a user;
analyzing the received physiological parameters of the user using an artificial intelligence algorithm to identify patterns and health trends;
generating a personalized health metric;
comparing the personalized health metric against baseline values specific to the user; and
transmitting alerts to a computing unit (200) when the personalized health metric deviates from baseline values specific to the user.
9. The method (400) as claimed in claim 8, comprising a step of transmitting the personalized health metric to the computing unit (200) via a communication unit (108).
10. The method (400) as claimed in claim 8, wherein the personalized health metrics comprise recommendations related to hydration, nutrition, exercise, medication management, or a combination thereof.
Date: October 06, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant