SYSTEM FOR POLYCYSTIC OVARY SYNDROME (PCOS) MANAGEMENT

Abstract

Abstract The present disclosure provides a system for polycystic ovary syndrome (PCOS) management, comprising a user interface for receiving and displaying data related to health metrics, symptoms, and menstrual cycle history; a data processing unit adapted to process user-provided health data, menstrual cycle data, and symptom tracking data; a personalization module operatively connected to said data processing unit, wherein said personalization module provides tailored health insights and recommendations based on said user-provided health data; an artificial intelligence-driven prediction unit configured to generate menstrual cycle predictions, detect cycle anomalies, and identify patterns indicative of potential health concerns; a nutritional analysis module adapted to analyze food products and suggest dietary plans suitable for PCOS management; a fitness guidance module configured to adjust workout recommendations based on user performance data and hormonal patterns; a hormonal monitoring unit adapted to analyze uploaded laboratory results and interpret hormonal levels in simplified terms; a fertility advisor module configured to provide guidance for conception support based on said menstrual cycle data and fertility markers; a continuous learning unit configured to adjust said recommendations in response to user feedback and historical data; and a virtual support module providing access to an artificial intelligence chatbot for symptom tracking guidance and virtual consultations. Fig. 1

Specification

Description:SYSTEM FOR POLYCYSTIC OVARY SYNDROME (PCOS) MANAGEMENT
Field of the Invention
[0001] The present disclosure generally relates to health management systems. Further, the present disclosure particularly relates to a system for polycystic ovary syndrome (PCOS) management.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Polycystic ovary syndrome (PCOS) is one of the most prevalent endocrine disorders observed among women of reproductive age. Characterized by hormonal imbalances, PCOS often leads to symptoms such as irregular menstrual cycles, excessive hair growth, acne, and infertility. Studies estimate that PCOS affects approximately 8-13% of women in reproductive age, with up to 70% of cases remaining undiagnosed globally. PCOS frequently emerges post-puberty and is generally diagnosed in women in their twenties and thirties. Various systems and techniques for managing PCOS are currently available.
[0004] A well-known approach for PCOS management involves general health tracking applications, which provide users with tools to log data regarding menstrual cycles, symptoms, and lifestyle habits. Such applications, however, are generally limited in their ability to address specific health complexities associated with PCOS. These systems lack personalized tracking features adapted for hormonal irregularities and often fail to analyze symptom patterns unique to PCOS sufferers. As a result, such systems are often ineffective in providing meaningful health insights or detecting early warning signs, thereby limiting their utility for comprehensive PCOS management.
[0005] Another commonly used method for PCOS management includes manual or semi-automated dietary and fitness tracking applications that help users monitor nutritional intake and exercise routines. Although these applications provide general health and wellness guidance, they often overlook specific dietary restrictions or exercise recommendations suited for individuals with PCOS. Furthermore, such systems do not include an adaptive feedback mechanism to adjust recommendations based on real-time health data, thereby lacking the capability to provide relevant, individualized insights. Consequently, users with PCOS may experience limited effectiveness when using these systems due to the absence of tailored diet or exercise plans that address their unique health needs.
[0006] Other health management systems are also available, which often incorporate limited artificial intelligence (AI)-based functionalities. While these systems may offer predictive insights or pattern detection based on user data, they lack the capability to manage the hormonal, nutritional, and fitness-specific requirements necessary for comprehensive PCOS management. Moreover, such systems frequently lack a continuous learning framework, thereby restricting the improvement of prediction accuracy or user insights based on historical data and outcomes. Additionally, existing AI-powered systems rarely provide a virtual support structure that allows users to engage in real-time symptom tracking guidance or receive consultation recommendations.
[0007] Further limitations exist in conventional PCOS management systems, which generally fail to integrate various health components-such as hormonal monitoring, cycle tracking, and fertility guidance-into a unified system. As a result, users are often required to use multiple platforms to access different health-related insights, resulting in fragmented health management. Such fragmentation restricts the ability of users to gain a holistic understanding of their health status and limits the effectiveness of PCOS management.
[0008] In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for PCOS management.
Summary
[0009] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00010] The following paragraphs provide additional support for the claims of the subject application.
[00011] The present disclosure provides a system for polycystic ovary syndrome (PCOS) management. The system includes a user interface for receiving and displaying data related to health metrics, symptoms, and menstrual cycle history. A data processing unit processes user-provided health data, menstrual cycle data, and symptom tracking data. A personalization module operatively connected to said data processing unit provides tailored health insights and recommendations based on said user-provided health data. An artificial intelligence-driven prediction unit generates menstrual cycle predictions, detects cycle anomalies, and identifies patterns indicative of potential health concerns. A nutritional analysis unit analyzes food products and suggests dietary plans suitable for PCOS management. A fitness guidance unit adjusts workout recommendations based on user performance data and hormonal patterns. A hormonal monitoring unit analyzes uploaded laboratory results and interprets hormonal levels in simplified terms. A fertility advisor unit provides guidance for conception support based on said menstrual cycle data and fertility markers. A continuous learning unit adjusts said recommendations in response to user feedback and historical data. A virtual support unit provides access to an artificial intelligence chatbot for symptom tracking guidance and virtual consultations.
[00012] Further, an adaptive learning algorithm in the personalization module refines health insights and recommendations based on longitudinal data trends from the user. A cycle anomaly alert component in the prediction unit generates notifications upon detection of irregular patterns in menstrual cycles or extended cycles. The nutritional analysis unit further comprises an image recognition sub-unit configured to scan ingredient lists of food products and evaluate compatibility with PCOS dietary guidelines. The fitness guidance unit includes a hormonal response tracker adapted to adjust exercise intensity and frequency based on real-time hormonal fluctuations tracked through user inputs or third-party integrations. Additionally, a predictive analytics component in the hormonal monitoring unit calculates potential risks of secondary health conditions associated with PCOS, such as insulin resistance and thyroid disorders, based on hormonal data trends.
[00013] An ovulation predictor within the fertility advisor unit utilizes hormonal and cycle data to calculate optimal conception periods specific to the user's physiological patterns. A reinforcement feedback loop in the continuous learning unit revises recommendations based on outcome data received from user follow-up entries or consultation results. The virtual support unit includes a telehealth integration component adapted to provide virtual access to healthcare professionals for live consultation based on severity indicators analyzed by the prediction unit. Additionally, the data processing unit aggregates data from multiple users, generating anonymized trend analyses to improve predictive and personalization accuracy for different demographics.
[00014] .
Brief Description of the Drawings
[00015] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00016] FIG. 1 illustrates an architectural diagram of a system for polycystic ovary syndrome (PCOS) management, in accordance with the embodiments of the present disclosure.
[00017] FIG. 2 illustrates a flow diagram for a system designed for polycystic ovary syndrome (PCOS) management, in accordance with the embodiments of the present disclosure.
Detailed Description
[00018] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00019] The use of the terms "a" and "an" and "the" and "at least one" and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term "at least one" followed by a list of one or more items (for example, "at least one of A and B") is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms "comprising," "having," "including," and "containing" are to be construed as open-ended terms (i.e., meaning "including, but not limited to,") unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00020] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00021] As used herein, the term "user interface" refers to any graphical or interactive display through which a user may enter, receive, or view information regarding health metrics, symptoms, and menstrual cycle history relevant to polycystic ovary syndrome (PCOS) management. This may include interactive displays on mobile devices, web-based applications, or integrated screens that enable users to input data on metrics such as mood, physical symptoms, and cycle irregularities. Additionally, the user interface as used herein includes elements that allow for personalized data views, providing an accessible means for tracking daily health-related information specific to PCOS.
[00022] As used herein, the term "data processing unit" refers to any computational unit or processor designed to receive, aggregate, and analyze user-provided health data, menstrual cycle information, and symptom tracking data for PCOS management purposes. This may include hardware or software configurations capable of handling complex datasets and integrating various data inputs to develop cohesive health insights. Additionally, the data processing unit as used herein includes functionality to format, sort, and manage said data to facilitate personalized analysis and effective symptom management in PCOS-related conditions.
[00023] As used herein, the term "personalization module" refers to a component configured to process health data in order to provide tailored insights and recommendations based on specific user profiles. This may include analytical tools, algorithms, or machine learning components operatively connected to a data processing unit, which enable the creation of personalized recommendations for diet, exercise, or other lifestyle adjustments. Additionally, the personalization module as used herein considers longitudinal health data to deliver insights that align with the user's evolving health needs specific to PCOS management.
[00024] As used herein, the term "artificial intelligence-driven prediction unit" refers to a computational module utilizing machine learning and artificial intelligence for generating predictions related to menstrual cycle patterns, detecting anomalies, and identifying potential health concerns. This includes units capable of processing historical cycle data to identify irregularities or patterns indicative of PCOS-associated risks. Additionally, the prediction unit as used herein provides alerts on health trends, assisting users in recognizing cycle variations that may require lifestyle adjustments or professional consultation.
[00025] As used herein, the term "nutritional analysis module" refers to any component adapted to evaluate food products and suggest dietary plans suitable for managing PCOS. This includes tools capable of scanning ingredient lists, assessing nutritional values, and identifying dietary options that align with PCOS-friendly guidelines. Additionally, the nutritional analysis module as used herein evaluates food choices based on factors such as insulin sensitivity and hormonal balance, providing diet recommendations that address specific dietary needs associated with PCOS.
[00026] As used herein, the term "fitness guidance module" refers to any system component providing exercise recommendations that are adjusted based on user performance data and hormonal patterns. This includes modules capable of analyzing user feedback on workout routines and tailoring exercise plans that align with the user's physical capacity and hormonal variations typical of PCOS. Additionally, the fitness guidance module as used herein incorporates exercise options that aim to improve physical resilience, hormonal balance, and overall wellness in individuals managing PCOS.
[00027] As used herein, the term "hormonal monitoring unit" refers to a component adapted to analyze laboratory results, including data on hormonal levels, to interpret them in accessible terms. This includes systems that integrate hormonal readings to detect trends, variations, or abnormalities in hormonal profiles related to PCOS. Additionally, the hormonal monitoring unit as used herein provides insights into hormonal fluctuations, facilitating users' understanding of their condition and supporting informed decisions on PCOS management.
[00028] As used herein, the term "fertility advisor module" refers to a component providing guidance for conception support tailored to users' menstrual and fertility data. This includes tools utilizing cycle tracking, hormonal data, and fertility markers to identify optimal conception periods for individuals with PCOS. Additionally, the fertility advisor module as used herein may include alerts and insights specific to fertility patterns, aiding users in identifying potential challenges and supporting conception planning in individuals with PCOS.
[00029] As used herein, the term "continuous learning unit" refers to a system component that adjusts recommendations over time in response to user feedback and historical data. This includes units employing machine learning algorithms that refine insights based on the user's evolving health profile, ensuring the relevancy and accuracy of lifestyle and health recommendations. Additionally, the continuous learning unit as used herein includes mechanisms for adapting to emerging data trends, improving the efficacy of long-term PCOS management strategies.
[00030] As used herein, the term "virtual support module" refers to a component providing access to an artificial intelligence-powered chatbot designed to assist with PCOS-related symptom tracking and general inquiries. This includes virtual support features enabling 24/7 access to health guidance, as well as tools offering recommendation suggestions and consultations based on the user's symptom data. Additionally, the virtual support module as used herein includes telehealth functionality that facilitates real-time support and guidance in PCOS management, enhancing accessibility to health resources for users.
[00031] FIG. 1 illustrates an architectural diagram of a system for polycystic ovary syndrome (PCOS) management, in accordance with the embodiments of the present disclosure. The system includes a user interface designed to enable users to enter, review, and interact with health-related data. This user interface is configured to receive and display information related to a variety of health metrics, symptoms, and menstrual cycle history pertinent to PCOS management. Such an interface may include graphical elements on a digital screen, interactive menus, and fields designed for user input, facilitating the logging of symptoms such as fatigue, mood changes, and other PCOS-related health indicators. Additionally, the user interface is adapted to present historical data in accessible formats, such as charts or lists, to allow users to track trends over time. In certain embodiments, the user interface is also equipped with alerting capabilities to notify users of potential health concerns identified through data analysis. These alerts can be based on abnormal patterns or significant deviations in recorded health metrics. Furthermore, the user interface may be adapted for customization, enabling the user to select relevant health metrics and set notification preferences based on individual needs. Such customization further enhances user engagement and facilitates a tailored health management experience for users with PCOS. In particular, the interface enables ongoing tracking and monitoring that addresses the unique variability and long-term nature of PCOS, supporting both immediate and long-term health insights.
[00032] The data processing unit in the PCOS management system functions as a core component that collects, organizes, and interprets the health data entered through the user interface. This processing unit is configured to handle complex data inputs, including the integration of user-provided health data, menstrual cycle information, and symptom tracking data. Such a unit employs algorithms capable of parsing and structuring data from multiple sources, thereby allowing the system to generate cohesive insights that form the basis of personalized recommendations. The data processing unit may also integrate data from external sources, such as wearable devices or third-party health applications, to provide a comprehensive view of the user's health metrics. Moreover, the data processing unit is adapted to filter and prioritize data based on relevance to PCOS, reducing noise and enhancing the accuracy of its outputs. In one aspect, the data processing unit supports real-time processing, ensuring that recent entries are quickly analyzed and reflected in health insights and alerts generated by the system. Additionally, the unit may include error-checking mechanisms to ensure that the health data is accurate and internally consistent, further supporting reliable health management. Overall, the data processing unit is essential for enabling the system to provide relevant and personalized health recommendations based on dynamically changing data inputs, particularly as it relates to the health complexities associated with PCOS.
[00033] The personalization module, operatively connected to the data processing unit, delivers customized health insights and recommendations tailored to the unique health profile of each user. This module analyzes the processed data, including individual health metrics, menstrual cycle information, and symptom patterns, to produce recommendations that address the specific needs and lifestyle preferences of users managing PCOS. The personalization module may employ machine learning algorithms and decision-support logic to determine appropriate guidance for diet, exercise, and other lifestyle factors. This component is specifically configured to consider the user's history of symptoms, providing insights that evolve based on longitudinal data trends. For example, the personalization module may identify patterns suggesting increased susceptibility to flare-ups or adverse health events, adjusting recommendations accordingly. Additionally, the module provides the ability to refine these insights continuously, based on user feedback or new health data. In certain embodiments, the personalization module includes an adaptive learning capability, which allows the system to learn from prior interactions and improve the specificity of recommendations. This component, therefore, enables a user-centered approach to PCOS management, offering individualized guidance that reflects each user's changing health status and personal goals.
[00034] The artificial intelligence-driven prediction unit within the PCOS management system is responsible for generating predictive insights related to menstrual cycle patterns and potential health anomalies. This unit employs artificial intelligence and machine learning techniques to analyze historical and current data, thereby identifying patterns and trends that may suggest deviations in the menstrual cycle. This capability is particularly beneficial in managing PCOS, as it allows users to receive timely warnings and insights on irregular cycles, missed periods, or other anomalies. The prediction unit may operate on historical data collected through the user interface and processed by the data processing unit, enabling it to detect cyclical patterns that are not immediately apparent. Additionally, the unit may incorporate a model trained on typical PCOS-related cycle irregularities, enhancing its accuracy in identifying unusual patterns that may warrant medical attention or lifestyle changes. In some instances, the prediction unit also generates personalized predictions for upcoming cycles based on learned patterns, providing users with anticipatory guidance. The inclusion of such predictive capabilities allows the system to proactively support users in managing PCOS-related challenges, such as cycle irregularities and health complications associated with hormone fluctuations.
[00035] The nutritional analysis module in the PCOS management system provides dietary recommendations specifically suited to the nutritional needs of individuals managing PCOS. This module is configured to analyze food products and dietary habits to suggest meal plans that align with PCOS-friendly guidelines, including considerations for insulin resistance, weight management, and hormone balance. The module may utilize data on food ingredients and nutritional profiles, assisting users in making informed choices regarding their diet. Additionally, this component may be equipped with an image recognition tool or barcode scanner, allowing users to input food information directly through product packaging. The module further evaluates food items based on compatibility with dietary recommendations suitable for PCOS, identifying ingredients or nutritional components that may trigger symptoms or exacerbate PCOS-related issues. In certain embodiments, the nutritional analysis module incorporates adaptive algorithms, learning from the user's preferences and health responses to refine future dietary recommendations. This component, therefore, enables the system to deliver meal plans and dietary advice that are both scientifically informed and tailored to individual health requirements associated with PCOS.
[00036] The fitness guidance module in the PCOS management system is designed to provide exercise recommendations that account for the user's performance data and hormonal fluctuations. This module considers the impact of physical activity on PCOS and is adapted to suggest exercise routines that align with individual health needs and capabilities. The fitness guidance module may analyze input from user feedback on workouts, wearable devices, or performance data to offer an optimized fitness plan. Additionally, it may adjust exercise routines based on hormonal patterns, recognizing that certain workouts may be more suitable during particular phases of the menstrual cycle or in response to PCOS-related hormonal changes. In one embodiment, the module includes a hormonal response tracker, which further refines exercise suggestions to enhance physical resilience and support the management of PCOS symptoms. This module's inclusion enhances the system's ability to offer personalized exercise guidance that supports the physical and hormonal well-being of users, helping them achieve fitness goals that align with PCOS management objectives.
[00037] The hormonal monitoring unit in the PCOS management system is configured to analyze laboratory results, particularly focusing on hormonal data, and to provide simplified interpretations that support user understanding. This unit is capable of processing uploaded lab results, such as hormone levels associated with conditions like insulin sensitivity and androgen levels, to assess potential risk factors or deviations from healthy ranges. The hormonal monitoring unit may apply predictive analytics to evaluate long-term hormonal trends, enabling users to gain insights into the effects of PCOS on their overall hormonal health. Additionally, the unit provides alerts or recommendations if significant deviations are detected, guiding users on possible next steps, such as seeking medical consultation. In some embodiments, the hormonal monitoring unit is linked to other components in the system, allowing the integration of hormonal data with fitness and dietary recommendations for comprehensive health management. This unit enables a more informed approach to managing PCOS by providing users with accessible interpretations of complex hormonal data.
[00038] The fertility advisor module within the PCOS management system offers guidance on conception and reproductive health based on menstrual cycle data and fertility indicators. This module is configured to analyze cycle patterns, hormonal information, and other fertility markers to provide insights on optimal conception periods, specifically tailored for individuals with PCOS. The fertility advisor module employs algorithms that take into account the effects of PCOS on cycle regularity and fertility, offering personalized advice on conception timing and potential challenges associated with PCOS. This module may also provide alerts regarding irregularities or factors that could influence conception success, thereby helping users to make informed decisions regarding family planning. In certain embodiments, the fertility advisor module is linked with other system components, allowing it to offer a comprehensive reproductive health overview. This component supports users with PCOS in navigating the complexities of fertility planning, offering tailored guidance for users who may face unique reproductive challenges due to PCOS.
[00039] The continuous learning unit in the PCOS management system is responsible for refining recommendations over time by adapting to user feedback and historical data. This unit employs machine learning techniques to analyze past interactions and outcomes, enabling the system to improve its accuracy in delivering health insights. By adjusting recommendations based on individual health changes and previous user responses, the continuous learning unit enhances the relevancy of the guidance provided by the system. In one embodiment, this unit incorporates a reinforcement feedback loop, which assesses the effectiveness of prior recommendations and fine-tunes future insights accordingly. This capability allows the system to evolve in response to user-specific patterns and outcomes, fostering an adaptive approach to PCOS management. As users interact with the system and provide input over time, the continuous learning unit ensures that the health recommendations are progressively optimized to meet their ongoing needs.
[00040] The virtual support module within the PCOS management system provides users with access to an artificial intelligence-powered chatbot, which offers guidance on symptom tracking, general inquiries, and virtual consultations. This module enables a 24/7 support mechanism, allowing users to receive real-time health insights and advice tailored to their PCOS-related symptoms. Additionally, the virtual support module includes a telehealth integration component that facilitates remote access to healthcare professionals, enhancing users' ability to manage their health without the need for in-person consultations. This module's AI-driven support provides users with an accessible health management tool, offering suggestions, answering questions, and providing guidance in navigating PCOS symptoms effectively. This support component enables users to actively engage with their health management system, ensuring continuity of care and promoting informed decision-making in PCOS management.
[00041] In an embodiment, the personalization module further comprises an adaptive learning algorithm that refines health insights and recommendations based on longitudinal data trends from the user. This adaptive learning algorithm analyzes trends within the user's historical data, including symptoms, cycle variations, lifestyle factors, and behavioral patterns, to improve the accuracy and relevancy of health guidance over time. For example, if data shows recurrent symptoms in correlation with specific lifestyle factors, the algorithm may prioritize recommendations to mitigate these factors, thus aligning guidance closely with the user's evolving health status. The adaptive learning algorithm enables personalized insights to remain dynamic and contextually relevant, accounting for shifts in the user's health over extended periods. Furthermore, the algorithm operates iteratively, continuously assessing and adjusting recommendations to support proactive management of PCOS symptoms and associated conditions. Such an adaptive system facilitates a highly personalized health management experience, fostering insights that evolve to meet the unique health requirements of each user as new data is collected and trends are recognized.
[00042] In an embodiment, the prediction unit further comprises a cycle anomaly alert component configured to generate notifications upon detection of irregular patterns in menstrual cycles or extended cycles. This alert component monitors the user's menstrual cycle data, using pattern recognition algorithms to detect irregularities that may indicate potential health concerns related to PCOS. For instance, if the system identifies a significant deviation from the user's established cycle patterns, such as missed or prolonged periods, the cycle anomaly alert component automatically triggers a notification to alert the user. This real-time alerting capability supports early intervention by prompting the user to seek further guidance or adjust lifestyle choices accordingly. Furthermore, the cycle anomaly alert component is designed to recognize both subtle and prominent variations in cycle data, accommodating the complex and often unpredictable nature of PCOS-related cycle irregularities. By delivering timely notifications, this component assists users in monitoring their reproductive health more effectively, facilitating informed decision-making and personalized PCOS management.
[00043] In an embodiment, the nutritional analysis module further comprises an image recognition sub-module configured to scan ingredient lists of food products, wherein said nutritional analysis module evaluates such ingredients for compatibility

foods without manual input. This feature enables users to make informed dietary decisions aligned with health goals related to managing PCOS, supporting dietary habits that are less likely to aggravate PCOS symptoms. By facilitating real-time evaluation of food ingredients, this module enhances the user's ability to adopt and maintain a PCOS-optimized diet.
[00044] In an embodiment, the fitness guidance module further comprises a hormonal response tracker adapted to adjust exercise intensity and frequency based on real-time hormonal fluctuations tracked through user inputs or third-party integrations. This hormonal response tracker analyzes data related to the user's hormonal levels, collected from wearable devices or manual input, to recommend appropriate exercise modifications that are compatible with the user's current hormonal state. For instance, if hormonal data suggests increased sensitivity or low energy levels, the module may suggest lower-intensity workouts or rest days, whereas stable hormonal levels may lead to recommendations for higher-intensity routines. Such adjustments prevent strain during sensitive periods and enhance the alignment of fitness plans with the user's hormonal cycle. This capability allows users to engage in exercise routines that are attuned to their body's hormonal shifts, potentially reducing exercise-induced stress and supporting more effective fitness outcomes. Overall, the hormonal response tracker enables a tailored fitness experience that accommodates the unique physiological fluctuations associated with PCOS.
[00045] In an embodiment, the hormonal monitoring unit further includes a predictive analytics component that calculates potential risks of secondary health conditions associated with PCOS, such as insulin resistance and thyroid disorders, based on hormonal data trends. This predictive analytics component examines hormonal data collected over time to identify patterns or anomalies that may indicate an elevated risk for secondary health complications commonly linked with PCOS. For example, the component analyzes fluctuations in insulin and androgen levels, both of which can serve as indicators of insulin resistance or hormonal imbalances that may contribute to thyroid disorders. By proactively identifying such trends, the system provides early warnings that enable users to seek preventive measures or professional guidance. This feature allows for a proactive approach to health management, as users can address potential risks before they develop into more serious conditions. The predictive analytics component, therefore, supports a comprehensive approach to managing not only PCOS but also its related health impacts.
[00046] In an embodiment, the fertility advisor module further comprises an ovulation predictor configured to utilize hormonal and cycle data to calculate optimal conception periods specific to the user's physiological patterns. This ovulation predictor analyzes menstrual cycle patterns, hormonal levels, and other fertility markers to estimate the user's most fertile days, taking into account the irregularities often present in PCOS. By considering the unique variability in each user's cycle, the ovulation predictor tailors its calculations to improve the accuracy of conception timing for individuals affected by PCOS. In certain cases, the predictor may also provide alerts if irregularities are identified that could impact fertility, thereby assisting users in addressing potential challenges proactively. The ovulation predictor functions as an integral tool for users who are attempting to conceive, offering insights that reflect both the timing and probability of conception based on the user's specific hormonal and cycle data. This targeted guidance enhances users' ability to plan for pregnancy effectively, especially in the context of PCOS-related fertility challenges.
[00047] In an embodiment, the continuous learning unit includes a reinforcement feedback loop adapted to revise recommendations based on outcome data received from user follow-up entries or consultation results. This reinforcement feedback loop continuously evaluates the effectiveness of prior recommendations by analyzing the outcomes documented by the user, thereby enabling the system to refine future guidance. For example, if a specific dietary recommendation led to positive health results, the system reinforces similar dietary advice for future instances; conversely, if a recommended exercise routine proved ineffective, the feedback loop adjusts subsequent guidance to better suit the user's needs. This dynamic learning mechanism ensures that the system adapts to the user's unique responses, making adjustments that reflect the user's evolving health profile and PCOS management progress. The continuous learning unit thus supports a personalized experience that is not static but evolves based on real-world outcomes, providing an enhanced level of responsiveness to the user's ongoing health journey.
[00048] In an embodiment, the virtual support module further comprises a telehealth integration component adapted to provide virtual access to healthcare professionals for live consultation based on severity indicators analyzed by the prediction unit. This telehealth integration component enables users to connect with healthcare providers when certain conditions or thresholds are detected, facilitating timely medical support without the need for in-person visits. The prediction unit evaluates health data and symptom patterns, flagging cases where professional intervention may be beneficial, and the telehealth component then coordinates virtual appointments through secure video or messaging platforms. This access to live consultation supports early intervention and informed decision-making, particularly when health conditions related to PCOS escalate or new symptoms arise. The telehealth integration extends the support system of the PCOS management platform by bridging users with qualified professionals, ensuring that users receive expert guidance as part of a holistic approach to managing PCOS and its associated symptoms.
[00049] In an embodiment, the data processing unit is further adapted to aggregate data from multiple users, wherein the data processing unit generates anonymized trend analyses to improve the system's predictive and personalization accuracy for different demographics. This aggregation functionality collects anonymized data across various demographic groups, allowing the system to identify common trends and patterns that enhance the predictive capabilities and relevancy of health recommendations. For example, the system may analyze data related to common symptom triggers, cycle irregularities, or responses to dietary interventions across age groups, providing insights that enable a more accurate personalization process. By applying aggregated insights, the system can offer recommendations that are statistically validated, improving the effectiveness of individual user guidance based on larger population trends. This feature also contributes to research on PCOS by identifying trends that may inform public health initiatives or tailored interventions, further enriching the platform's capacity to deliver reliable, personalized insights in PCOS management.
[00050] In an embodiment, the user interface for receiving and displaying data on health metrics, symptoms, and menstrual cycles allows users to input and view essential data in a structured and accessible format. This improves user engagement by providing a straightforward method for tracking and reviewing health metrics. The interface's real-time display functionality enhances user awareness of health patterns, facilitating proactive management of PCOS symptoms. Through structured data entry and visualization, users experience improved accuracy and efficiency in tracking health metrics, fostering timely interventions and better-informed health decisions.
[00051] In an embodiment, the data processing unit processes user-provided health data, menstrual cycle data, and symptom tracking data to produce cohesive, integrated health insights. This unit's ability to analyze data from multiple inputs minimizes errors and redundancies while enhancing the accuracy of health insights derived from the system. By enabling data integration across multiple health metrics, the data processing unit streamlines the personalization process, resulting in highly relevant and individualized health recommendations for PCOS management. This enables users to manage their symptoms with greater precision and confidence.
[00052] In an embodiment, the personalization module, operatively connected to the data processing unit, provides tailored health insights and recommendations that align with each user's unique health profile. The personalization module's ability to analyze specific health data results in recommendations that address the user's immediate and long-term needs. This targeted guidance promotes more effective symptom management by adapting to changes in user health data over time. By personalizing recommendations, this module significantly enhances the user's ability to manage PCOS symptoms through recommendations that reflect their current health condition and lifestyle preferences.
[00053] In an embodiment, the artificial intelligence-driven prediction unit generates menstrual cycle predictions and detects anomalies. By identifying irregular patterns, this prediction unit enables users to anticipate and respond to changes in their cycle, facilitating earlier interventions and improved health outcomes. The predictive functionality provides actionable insights that allow users to proactively manage irregularities, potentially reducing the severity of PCOS-related complications. This capability enhances the user's experience by providing timely health alerts and enabling an adaptive response to health fluctuations associated with PCOS.
[00054] In an embodiment, the nutritional analysis module, configured to analyze food products and suggest dietary plans suitable for PCOS management, enables users to make informed dietary choices that support symptom reduction. By evaluating food products and analyzing ingredients, the module offers diet recommendations that are compatible with PCOS-related dietary guidelines, such as controlling insulin resistance or avoiding inflammation-triggering foods. This targeted dietary guidance helps users adopt a more effective nutrition strategy, contributing to the overall management of PCOS through a tailored diet that aligns with individual health needs.
[00055] In an embodiment, the fitness guidance module, configured to adjust workout recommendations based on user performance and hormonal data, enables the user to engage in exercise routines that support hormonal balance. By tailoring exercise intensity and frequency, this module helps reduce hormonal fluctuations associated with PCOS and supports more effective physical health management. The module's capability to align workout guidance with hormonal data also minimizes the risk of exercise-induced stress, promoting fitness routines that are safe, sustainable, and beneficial to the user's health goals specific to PCOS.
[00056] In an embodiment, the hormonal monitoring unit analyzes laboratory results and interprets hormonal levels in simplified terms, offering users an accessible understanding of their hormonal health. By providing clear interpretations and visual summaries, the monitoring unit enhances the user's ability to make informed decisions regarding hormone-related health management. Additionally, this unit's analysis of hormonal trends over time allows users to detect abnormalities early, potentially leading to faster intervention for secondary health risks associated with PCOS, such as thyroid disorders. This functionality fosters an informed approach to managing hormonal health with greater ease and confidence.
[00057] In an embodiment, the fertility advisor module, which includes an ovulation predictor based on hormonal and cycle data, provides users with tailored guidance on optimal conception periods. By analyzing hormonal and cycle variations, the fertility advisor module enhances users' understanding of their fertility windows, supporting more effective family planning. The module's ability to detect individual ovulation patterns also assists users with irregular cycles, providing them with insights that are typically challenging to identify independently. This functionality supports improved reproductive health outcomes, addressing fertility challenges commonly experienced by individuals with PCOS.
[00058] In an embodiment, the continuous learning unit, featuring a reinforcement feedback loop, refines health recommendations based on user feedback and historical data. This feedback loop adapts to user responses and health progress, creating a personalized experience that evolves as the user's health status changes. By learning from outcomes and interactions, the continuous learning unit ensures recommendations remain relevant and increasingly effective over time. This adaptive capability promotes more precise management of PCOS symptoms by continuously adjusting insights based on real-world user interactions, enhancing the overall reliability of the system's guidance.
[00059] In an embodiment, the virtual support module includes a telehealth integration component that enables virtual consultations with healthcare professionals. The telehealth component leverages severity indicators from the prediction unit, connecting users with medical assistance when needed. This functionality ensures timely access to healthcare support, especially for users experiencing heightened symptoms or complex conditions. By facilitating professional consultations directly within the system, the virtual support module provides a responsive and accessible healthcare resource, empowering users to address PCOS-related health concerns with real-time expert support as part of an integrated management approach.
[00060] In an embodiment, the data processing unit aggregates data from multiple users and generates anonymized trend analyses to improve predictive and personalization accuracy across demographics. By analyzing data trends across a large user base, the system gains insights into common health patterns, enhancing its capability to provide statistically supported recommendations. This aggregated data also contributes to refining the system's algorithms, ensuring that personalized health insights are informed by broader demographic trends. This aggregation functionality improves the accuracy of personalized health recommendations, benefiting individual users with insights derived from a larger, more comprehensive dataset.
[00061] FIG. 2 illustrates a flow diagram for a system designed for polycystic ovary syndrome (PCOS) management, in accordance with the embodiments of the present disclosure. The central element of the system is a user input module, which receives health metrics, symptoms, and menstrual history from the user. This input information is then processed through multiple functional components within the system to deliver personalized health insights and recommendations. The data processing unit leverages user health and cycle information to generate actionable health data, which is subsequently directed towards specific analytical modules. The nutritional analysis module evaluates the input data to suggest PCOS-friendly dietary recommendations, while the fitness guidance module interprets user data to tailor workout plans suited to the user's physical and hormonal conditions. Additionally, the hormonal monitoring unit interprets hormonal lab results, simplifying this data for the user's understanding. In parallel, the prediction unit analyzes menstrual cycle patterns, generating predictions and identifying cycle anomalies to aid proactive health management. The fertility advisor module provides guidance for conception support, tailored to the user's cycle data, and the virtual support module offers an AI-driven chatbot for ongoing assistance and consultation. Moreover, a continuous learning unit utilizes feedback from user interactions to refine and adjust future recommendations, ensuring evolving personalization. These combined processes ultimately lead to the generation of personalized health insights and tailored recommendations, empowering users in the management of PCOS with real-time, data-driven support.
[00062] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00063] Throughout the present disclosure, the term 'processing means' or 'microprocessor' or 'processor' or 'processors' includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00064] The term "non-transitory storage device" or "storage" or "memory," as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00065] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00066] While several implementations have been described and illustrated herein, 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 may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, 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 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 implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations 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 scope of the present disclosure.













Claims
I/We Claim:
1. A system for polycystic ovary syndrome (PCOS) management, comprising:
a user interface for receiving and displaying data related to health metrics, symptoms, and menstrual cycle history,
a data processing unit adapted to process user-provided health data, menstrual cycle data, and symptom tracking data,
a personalization module operatively connected to said data processing unit, wherein said personalization module provides tailored health insights and recommendations based on said user-provided health data,
an artificial intelligence-driven prediction unit configured to generate menstrual cycle predictions, detect cycle anomalies, and identify patterns indicative of potential health concerns,
a nutritional analysis module adapted to analyze food products and suggest dietary plans suitable for PCOS management,
a fitness guidance module configured to adjust workout recommendations based on user performance data and hormonal patterns,
a hormonal monitoring unit adapted to analyze uploaded laboratory results and interpret hormonal levels in simplified terms,
a fertility advisor module configured to provide guidance for conception support based on said menstrual cycle data and fertility markers,
a continuous learning unit configured to adjust said recommendations in response to user feedback and historical data, and
a virtual support module providing access to an artificial intelligence chatbot for symptom tracking guidance and virtual consultations.
2. The system of claim 1, wherein said personalization module further comprises an adaptive learning algorithm configured to refine health insights and recommendations based on longitudinal data trends from the user.
3. The system of claim 1, wherein said prediction unit further comprises a cycle anomaly alert component configured to generate notifications upon detection of irregular patterns in menstrual cycles or extended cycles.
4. The system of claim 1, wherein said nutritional analysis module is further configured to scan ingredient lists of food products using an image recognition sub-module, wherein said nutritional analysis module evaluates said ingredients for compatibility with PCOS dietary guidelines.
5. The system of claim 1, wherein said fitness guidance module further comprises a hormonal response tracker, wherein said hormonal response tracker is adapted to adjust exercise intensity and frequency based on real-time hormonal fluctuations tracked through user inputs or third-party integrations.
6. The system of claim 1, wherein said hormonal monitoring unit further includes a predictive analytics component that calculates potential risks of secondary health conditions associated with PCOS, such as insulin resistance and thyroid disorders, based on hormonal data trends.
7. The system of claim 1, wherein said fertility advisor module further comprises an ovulation predictor configured to utilize hormonal and cycle data to calculate optimal conception periods specific to the user's physiological patterns.
8. The system of claim 1, wherein said continuous learning unit includes a reinforcement feedback loop adapted to revise recommendations based on outcome data received from user follow-up entries or consultation results.
9. The system of claim 1, wherein said virtual support module further comprises a telehealth integration component, wherein said telehealth integration component is adapted to provide virtual access to healthcare professionals for live consultation based on severity indicators analyzed by said prediction unit.
10. The system of claim 1, wherein said data processing unit is further adapted to aggregate data from multiple users, wherein said data processing unit generates anonymized trend analyses to improve the system's predictive and personalization accuracy for different demographics.



SYSTEM FOR POLYCYSTIC OVARY SYNDROME (PCOS) MANAGEMENT
Abstract
The present disclosure provides a system for polycystic ovary syndrome (PCOS) management, comprising a user interface for receiving and displaying data related to health metrics, symptoms, and menstrual cycle history; a data processing unit adapted to process user-provided health data, menstrual cycle data, and symptom tracking data; a personalization module operatively connected to said data processing unit, wherein said personalization module provides tailored health insights and recommendations based on said user-provided health data; an artificial intelligence-driven prediction unit configured to generate menstrual cycle predictions, detect cycle anomalies, and identify patterns indicative of potential health concerns; a nutritional analysis module adapted to analyze food products and suggest dietary plans suitable for PCOS management; a fitness guidance module configured to adjust workout recommendations based on user performance data and hormonal patterns; a hormonal monitoring unit adapted to analyze uploaded laboratory results and interpret hormonal levels in simplified terms; a fertility advisor module configured to provide guidance for conception support based on said menstrual cycle data and fertility markers; a continuous learning unit configured to adjust said recommendations in response to user feedback and historical data; and a virtual support module providing access to an artificial intelligence chatbot for symptom tracking guidance and virtual consultations.
Fig. 1

, Claims:Claims
I/We Claim:
1. A system for polycystic ovary syndrome (PCOS) management, comprising:
a user interface for receiving and displaying data related to health metrics, symptoms, and menstrual cycle history,
a data processing unit adapted to process user-provided health data, menstrual cycle data, and symptom tracking data,
a personalization module operatively connected to said data processing unit, wherein said personalization module provides tailored health insights and recommendations based on said user-provided health data,
an artificial intelligence-driven prediction unit configured to generate menstrual cycle predictions, detect cycle anomalies, and identify patterns indicative of potential health concerns,
a nutritional analysis module adapted to analyze food products and suggest dietary plans suitable for PCOS management,
a fitness guidance module configured to adjust workout recommendations based on user performance data and hormonal patterns,
a hormonal monitoring unit adapted to analyze uploaded laboratory results and interpret hormonal levels in simplified terms,
a fertility advisor module configured to provide guidance for conception support based on said menstrual cycle data and fertility markers,
a continuous learning unit configured to adjust said recommendations in response to user feedback and historical data, and
a virtual support module providing access to an artificial intelligence chatbot for symptom tracking guidance and virtual consultations.
2. The system of claim 1, wherein said personalization module further comprises an adaptive learning algorithm configured to refine health insights and recommendations based on longitudinal data trends from the user.
3. The system of claim 1, wherein said prediction unit further comprises a cycle anomaly alert component configured to generate notifications upon detection of irregular patterns in menstrual cycles or extended cycles.
4. The system of claim 1, wherein said nutritional analysis module is further configured to scan ingredient lists of food products using an image recognition sub-module, wherein said nutritional analysis module evaluates said ingredients for compatibility with PCOS dietary guidelines.
5. The system of claim 1, wherein said fitness guidance module further comprises a hormonal response tracker, wherein said hormonal response tracker is adapted to adjust exercise intensity and frequency based on real-time hormonal fluctuations tracked through user inputs or third-party integrations.
6. The system of claim 1, wherein said hormonal monitoring unit further includes a predictive analytics component that calculates potential risks of secondary health conditions associated with PCOS, such as insulin resistance and thyroid disorders, based on hormonal data trends.
7. The system of claim 1, wherein said fertility advisor module further comprises an ovulation predictor configured to utilize hormonal and cycle data to calculate optimal conception periods specific to the user's physiological patterns.
8. The system of claim 1, wherein said continuous learning unit includes a reinforcement feedback loop adapted to revise recommendations based on outcome data received from user follow-up entries or consultation results.
9. The system of claim 1, wherein said virtual support module further comprises a telehealth integration component, wherein said telehealth integration component is adapted to provide virtual access to healthcare professionals for live consultation based on severity indicators analyzed by said prediction unit.
10. The system of claim 1, wherein said data processing unit is further adapted to aggregate data from multiple users, wherein said data processing unit generates anonymized trend analyses to improve the system's predictive and personalization accuracy for different demographics.

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