JP2024513825A - Detecting Anovulatory Cycles from Wearable-Based Physiological Data - Google Patents

Abstract

Methods, systems and devices for detecting anovulatory cycles are described. The system may be configured to receive physiological data collected over multiple days, the physiological data including at least body temperature data. Additionally, the system may be configured to determine a time series of body temperature values obtained over the multiple days, the time series including a plurality of ovulatory cycles of the user. The system may then identify a plurality of ovulatory cycles based on a positive slope in the time series and identify indicators of anovulatory cycles based on deviations in the time series. The system may generate messages indicative of the identified indicators of anovulatory cycles for display on a graphical user interface on the user device.

Description

Cross-referenced This application has the benefit of U.S. Nonprovisional Patent Application No. 17/709,736 by Thigpen et al., entitled "ANOVULATORY CYCLE DETECTION FROM WEARABLE-BASED PHYSIOLOGICAL DATA," filed March 31, 2022. , filed April 1, 2021, assigned to the applicant and expressly incorporated herein by reference, by Aschbacher et al. Claims the benefit of patent application Ser. No. 63/169,314.

TECHNICAL FIELD The following relates to wearable devices and data processing, including detection of anovulatory cycles from wearable-based physiological data.

Some wearable devices may be configured to collect data from a user related to body temperature and heart rate. For example, some wearable devices may be configured to detect cycles related to reproductive health. However, conventional cycle detection techniques implemented by wearable devices are inadequate.

FIG. 2 illustrates an example system that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure.

FIG. 1 illustrates an example of a system supporting detection of anovulatory cycles from wearable-based physiological data, in accordance with aspects of the present disclosure.

FIG. 2 illustrates an example system that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure.

FIG. 3 illustrates an example timing diagram supporting detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present invention.

FIG. 3 illustrates an example graphical user interface (GUI) that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure.

FIG. 2 is a block diagram of an apparatus that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure.

FIG. 2 is a block diagram of a wearable application that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure.

FIG. 2 is an illustration of a system that includes a device that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure.

1 is a flow chart illustrating a method for supporting detection of anovulatory cycles from wearable-based physiological data, according to aspects of the present disclosure. 3 is a flowchart illustrating a method of supporting detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure. 3 is a flowchart illustrating a method of supporting detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure.

Some wearable devices may be configured to collect physiological data from a user, including body temperature data, heart rate data, and the like. The acquired physiological data may be used to analyze the user's movements and other activities, such as sleep patterns. Many users desire more insight into their physical health, including sleep patterns, activity and overall physical health. In particular, many users may want more insight into women's health, including their menstrual cycles, ovulation and fertility patterns. However, typical cycle tracking or women's health devices and applications lack the ability to provide robust predictions and insights for several reasons.

First, typical cycle prediction applications require users to manually take their body temperature with a device at discrete times each day. This single body temperature data point may not provide enough context to accurately capture or predict true body temperature changes that are indicative of women's health cycle patterns, and the measurement device's sensitivity to user movements or actions Considering this, it may be difficult to capture accurately. Second, whether devices are wearable or measure a user's body temperature more frequently throughout the day, typical devices and applications do not combine measured body temperature with physiological contributions to a woman's cycle. It lacks the ability to collect other physiological, behavioral or contextual input from the user, which can provide a more comprehensive understanding of the complete set of information.

Aspects of the present disclosure are directed to techniques for detecting and tracking anovulatory cycles. In particular, the computing device of the present disclosure may receive physiological data, including body temperature data, from a wearable device associated with a user and determine a time series of body temperature values obtained over multiple days. For example, aspects of the present disclosure may extract one or more morphological characteristics from a graphical representation of a time series of body temperature values, such as a positive slope of the time series of body temperature values, a temperature deviation of the time series of body temperature values, or both. can be identified. Accordingly, aspects of the present disclosure identify one or more indicators of anovulatory cycles in a time series based on identifying morphological characteristics (eg, deviations). In such cases, one or more indicators of an anovulatory cycle may be associated with temperature deviations in the time series and/or one or more additional morphological characteristics in the time series of body temperature values.

In some implementations, the system may analyze historical temperature data from the user to identify an ovulatory cycle for multiple previous menstrual cycles, and may identify the user's previous menstrual cycles as well as ovulatory cycles and/or anovulation. An indicator may be generated to a user, clinician, or fertility specialist indicating a pattern associated with the cycle. The system may also analyze the temperature series data in real time and determine the next fertilization window based on identifying multiple ovulatory cycles and identifying one or more indicators of anovulatory cycles in the time series. (eg, the period during which the user will ovulate).

For purposes of this disclosure, the term "ovulatory cycle" refers to the part of a user's menstrual cycle that includes hormone production and a series of natural changes in the structure of the uterus and ovaries of the female reproductive system that enable pregnancy. Sometimes used to refer to. The ovulation cycle occurs around day 14 of a 28 day cycle. Ovulation is the release of an egg from the ovary. For example, a user may experience ovulation between days 6 and 14 of the menstrual cycle, where follicle-stimulating hormone begins to mature follicles within the ovaries.

For purposes of this disclosure, the term "anovulatory cycle" may be used to refer to a portion of a user's cycle in which ovulation, or the release of an egg from the ovaries, does not occur. For example, a user may experience an anovulatory cycle due to hormonal imbalances, which may result from hormonal birth control, environmental factors, exercise, stress, or a combination thereof. Anovulatory cycles may have insufficient levels of progesterone, which may cause heavy bleeding, which the user may mistake for menstruation. Because progesterone may increase body temperature, a wearable device that detects body temperature (e.g., a ring) may help identify cycles with low progesterone levels and detect anovulatory cycles.

Some aspects of the present disclosure are directed to identifying one or more anovulatory cycles before a user experiences the symptoms and effects of the anovulatory cycle. However, the techniques described herein may also be used to detect anovulatory cycles when the user does not exhibit symptoms or is unaware of the symptoms. In some implementations, a computing device may identify an anovulatory cycle using a temperature sensor. In such cases, the computing device may estimate retrospective dates of anovulatory cycles without the user tagging or labeling these events.

In conventional systems, retrospective events (eg, menstruation, ovulation, lack of ovulation, etc.) can be estimated using basal body temperature, which is measured once a day (eg, in the morning) with an oral thermometer. The technology described herein is based on measurements obtained from a wearable that continuously measures the user's surface temperature and signals extracted from blood flow, such as arterial blood flow, to determine whether the user's body temperature is data can be collected continuously. In some implementations, the computing device may sample the user's body temperature continuously throughout the day and night. Sampling at a sufficient rate (e.g., 1 sample per minute) throughout the day (or during certain phases of the night and/or during certain phases of the sleep cycle) is useful for the analyzes described herein. can provide sufficient body temperature data for

In some cases, continuous finger temperature measurements may capture body temperature fluctuations (eg, small or large fluctuations) that may not be evident in core body temperature. Continuous temperature measurement, for example with a finger, may not be provided by other temperature measurements elsewhere on the body, or if the user was taking the temperature manually once a day. Minute-to-minute or hour-to-hour body temperature fluctuations can be captured, providing insight into the body temperature. Accordingly, data collected by the computing device may be used to identify when a user experiences an anovulatory cycle.

The techniques described herein may notify a user, clinician, fertility specialist, caregiver, partner, or a combination thereof of one or more identified anovulatory cycles in a variety of ways. For example, the system may generate a message indicating one or more identified anovulatory cycles for display on a graphical user interface (GUI) of a user device. In such cases, the system may display a message or other notification on the user device's GUI to notify the user, clinician, etc. of the identified one or more anovulatory cycle indicators and The above ovulation cycles can be notified to the user and recommendations can be made to the user. In some implementations, the system may make tag recommendations to the user. For example, the system may recommend ovulation tags, mood tags, and symptom tags (eg, cramps, headaches) to the user at determined times in the user's cycle (eg, in a personalized manner). The system may recommend tags based on previous history of body temperature data, personalized cycling patterns and/or previous symptom history.

The system may also include graphics or text indicating data used to detect and/or predict the next anovulatory cycle. For example, the GUI may display a notification that the next anovulatory cycle is predicted based on body temperature deviation from a normal baseline. In some cases, the GUI determines whether the following is true based on heart rate deviation from normal baseline, respiratory rate deviation from normal baseline, heart rate variability (HRV) from normal baseline, or a combination thereof. A notification may be displayed that an ovulation cycle has been predicted.

Aspects of the present disclosure will first be described in the context of a system that supports the collection of physiological data from a user via a wearable device. Additional aspects of the present disclosure are described in the context of an example timing diagram and an example GUI. Aspects of the present disclosure are further illustrated by and described with reference to apparatus diagrams, system diagrams, and flowcharts related to detecting anovulatory cycles from wearable-based physiological data.

FIG. 1 illustrates an example system 100 that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure. System 100 includes a plurality of electronic devices (eg, wearable device 104, user device 106) that can be worn and/or operated by one or more users 102. System 100 further includes a network 108 and one or more servers 110.

The electronic device may include any electronic device known in the art, including a wearable device 104 (eg, a ring wearable device, a watch wearable device, etc.), a user device 106 (eg, a smartphone, laptop, tablet, etc.). The electronic device associated with each user 102 performs one or more of the following functions: 1) measuring physiological data; 2) storing the measured data; and 3) storing the measured data. 4) providing output to a user 102 (e.g., via a GUI) based on the processed data; and 5) communicating the data with each other and/or with other computing devices. may include one or more of . Different electronic devices may perform one or more of these functions.

Exemplary wearable devices 104 include ring computing devices (hereinafter referred to as "rings") configured to be worn on the fingers of user 102, wrist computing devices configured to be worn on the wrists of user 102. (e.g., a smart watch, fitness band or bracelet) and/or a head-mounted computing device (e.g., glasses/goggles). Wearable device 104 also includes bands around the head (e.g., forehead headband), bands around the arms (e.g., forearm bands and/or upper arm bands), and/or bands around the legs (e.g., thigh bands). or calf bands), bands that may be placed in other locations such as behind the ears, under the armpits, straps (eg, flexible or non-flexible bands or straps), stick-on sensors, and the like. Wearable device 104 may also be attached to or included in clothing. For example, wearable device 104 may be included in a pocket and/or pouch of clothing. As another example, wearable device 104 may be clipped and/or pinned to clothing or otherwise maintained close to user 102. Exemplary clothing items may include, but are not limited to, hats, shirts, gloves, pants, socks, outerwear (eg, jackets), and undergarments. In some implementations, wearable device 104 may be included with other types of devices, such as training/sports devices used during physical activity. For example, wearable device 104 may be attached to or included in a bicycle, skis, tennis racket, golf club, and/or training weights.

Much of this disclosure may be described in the context of ring wearable device 104. Accordingly, the terms "ring 104," "wearable device 104," and similar terms may be used interchangeably, unless otherwise specified herein. However, it is noted herein that aspects of the present disclosure may be implemented using other wearable devices (e.g., a watch wearable device, a necklace wearable device, a bracelet wearable device, an earring wearable device, an anklet wearable device, etc.). is contemplated, the use of the term "ring 104" should not be considered limiting.

In some aspects, user device 106 may include handheld mobile computing devices such as smartphones and tablet computing devices. User devices 106 may also include personal computers such as laptops and desktop computing devices. Other example user devices 106 may include server computing devices that may communicate with other electronic devices (eg, via the Internet). In some implementations, the computing device may include a medical device, such as an external wearable computing device (eg, a Holter monitor). Medical devices may also include implantable medical devices such as pacemakers and cardioverter defibrillators. Other example user devices 106 include Internet of Things (IoT) devices (e.g., IoT devices), smart televisions, smart speakers, smart displays (e.g., video calling displays), hubs (e.g., wireless communication hubs), security systems, smart May include home computing devices such as appliances (eg, thermostats and refrigerators) and fitness equipment.

Some electronic devices (e.g., wearable device 104, user device 106) may measure physiological parameters of each user 102, such as photoplethysmography waveforms, continuous skin temperature, pulse waveforms, respiration rate, heart rate, heart rate variability (HRV), actigraphy, galvanic skin response, pulse oximetry, and/or other physiological parameters. Some electronic devices that measure physiological parameters may also perform some/all of the calculations described herein. Some electronic devices may not measure physiological parameters but may perform some/all of the calculations described herein. For example, a ring (e.g., wearable device 104), a mobile device application, or a server computing device may process received physiological data measured by other devices.

In some implementations, the user 102 may operate or be associated with multiple electronic devices, some of which may measure physiological parameters and some of which may process the measured physiological parameters. In some implementations, the user 102 may have a ring (e.g., wearable device 104) that measures physiological parameters. The user 102 may have or be associated with a user device 106 (e.g., a mobile device, smartphone), where the wearable device 104 and the user device 106 are communicatively coupled to each other. In some cases, the user device 106 may receive data from the wearable device 104 and perform some/all of the calculations described herein. In some implementations, the user device 106 may also measure physiological parameters described herein, such as movement/activity parameters.

For example, as shown in FIG. 1, a first user 102-a (user 1) has a wearable device 104-a (e.g., ring 104-a) and a user device 106-a that may operate as described herein. may operate on or be associated with a. In this example, user device 106-a associated with user 102-a may process/store physiological parameters measured by ring 104. By comparison, a second user 102-b (user 2) may be associated with a ring 104-b, a watch wearable device 104-c (eg, a watch 104-c), and a user device 106-b, where the user 102 User device 106-b associated with ring 104-b and/or watch 104-c may process/store physiological parameters measured by ring 104-b and/or watch 104-c. Additionally, an nth user 102-n (user N) may be associated with a configuration of electronic devices (eg, ring 104, user device 106-n) described herein. In some aspects, wearable devices 104 (e.g., rings 104, watches 104) and other electronic devices connect to user devices 106 of respective users 102 via Bluetooth, Wi-Fi, and other wireless protocols. may be communicatively coupled to.

In some implementations, the ring 104 (e.g., wearable device 104) of the system 100 may be configured to collect physiological data from each user 102 based on the arterial blood flow in the user's finger. In particular, the ring 104 may utilize one or more LEDs (e.g., red LEDs, green LEDs) that emit light on the palm side of the user's finger to collect physiological data based on the arterial blood flow in the user's finger. In some implementations, the ring 104 may obtain physiological data using a combination of both green and red LEDs. The physiological data may include any physiological data known in the art, including, but not limited to, body temperature data, accelerometer data (e.g., movement/exercise data), heart rate data, HRV data, blood oxygen level data, or any combination thereof.

Red LEDs and green LEDs are seen to have distinct advantages of their own when acquiring physiological data such as through different parts of the body under different conditions (e.g. bright/dark, active/inactive). As has been proposed, the use of both green and red LEDs may offer several advantages over other solutions. For example, green LEDs have been found to perform better during exercise. Additionally, using multiple LEDs (e.g., a green LED and a red LED) distributed around the ring 104 is advantageous compared to wearable devices that utilize LEDs placed in close proximity to each other, such as in a watch wearable device. , has been found to exhibit excellent performance. Additionally, blood vessels in the fingers (eg arteries, capillaries) are more accessible via the LED compared to blood vessels in the wrist. In particular, the wrist arteries are located in the lower part of the wrist (e.g., on the palm side of the wrist), compared to the upper part of the wrist (e.g., on the dorsal side of the wrist), where wearable watch devices and similar devices are typically worn. Meaning only capillaries are accessible. Thus, the use of LEDs and other sensors within the ring 104 has been shown to exhibit superior performance compared to wrist-worn wearable devices, as the ring 104 (capillary This is because it has greater access to arteries (compared to blood vessels), thereby yielding stronger signals and more valuable physiological data.

Electronic devices of system 100 (eg, user device 106, wearable device 104) may be communicatively coupled to one or more servers 110 via wired or wireless communication protocols. For example, as shown in FIG. 1, an electronic device (eg, user device 106) may be communicatively coupled to one or more servers 110 via network 108. Network 108 may implement Transmission Control Protocol and Internet Protocol (TCP/IP), such as the Internet, or may implement other network 108 protocols. The network connection between network 108 and each electronic device may facilitate the transfer of data via email, web, text message, mail, or any other suitable form of interaction within computer network 108. I can do it. For example, in some implementations, ring 104-a associated with first user 102-a may be communicatively coupled to user device 106-a, where user device 106-a is connected via network 108. and communicatively coupled to server 110. In additional or alternative cases, wearable device 104 (eg, ring 104, watch 104) may be communicatively coupled directly to network 108.

System 100 may provide on-demand database services between user device 106 and one or more servers 110. In some cases, server 110 may receive data from user device 106 via network 108 and may store and analyze the data. Similarly, server 110 may provide data to user device 106 via network 108. In some cases, server 110 may be located in one or more data centers. Server 110 may be used for data storage, management, and processing. In some implementations, server 110 may provide a web-based interface to user device 106 via a web browser.

In some aspects, system 100 may detect time periods during which user 102 is asleep and categorize the time periods during which user 102 is asleep into one or more sleep stages (eg, sleep stage classification). For example, as shown in FIG. 1, user 102-a may be associated with wearable device 104-a (eg, ring 104-a) and user device 106-a. In this example, ring 104-a may collect physiological data associated with user 102-a, including body temperature, heart rate, HRV, respiratory rate, etc. In some aspects, data collected by ring 104-a may be input to a machine learning classifier, where the machine learning classifier determines whether user 102-a is (has been) asleep. configured to determine a time period. Additionally, the machine learning classifier can differentiate time periods into different sleep stages, including wakeful sleep stages, rapid eye movement (REM) sleep stages, light sleep stages (NREM), and deep sleep stages (NREM). may be configured to classify. In some aspects, the classified sleep stages may be displayed to the user 102-a via the GUI of the user device 106-a. Sleep stage classification may be used to provide feedback to the user 102-a regarding the user's sleep patterns, such as recommended bedtimes, recommended wake-up times, etc. Additionally, in some implementations, the sleep stage classification techniques described herein are used to calculate scores for each user, such as Sleep Scores, Readiness Scores, etc. may be used for

In some aspects, system 100 may utilize circadian rhythm-derived characteristics to further improve physiological data collection, data processing procedures, and other techniques described herein. The term circadian rhythm may refer to the natural internal process that regulates an individual's sleep-wake cycle, repeating approximately every 24 hours. In this regard, the techniques described herein may utilize circadian rhythm regulation models to improve physiological data collection, analysis, and data processing. For example, the circadian rhythm adjustment model may be input into a machine learning classifier along with physiological data collected from the user 102 via the wearable device 104-a. In this example, the circadian rhythm adjustment model may be configured to "weight" or adjust physiological data collected over the user's natural, approximately 24-hour circadian rhythm. In some implementations, the system may initially start with a "baseline" circadian rhythm regulation model, and use physiological data collected from each user 102 to modify the baseline model to A tailored and individualized circadian rhythm adjustment model unique to each 102 may be generated.

In some aspects, system 100 may utilize other biological rhythms to further improve physiological data collection, analysis, and processing through the phases of these other rhythms. For example, if a weekly rhythm is detected within an individual's baseline data, the model may be configured to adjust the "weight" of the data by day of the week. Biological rhythms that may require adjustments to the model with this method are: 1) ultradian (faster than daily rhythms, in the sleep cycle during the sleep state and in the measured physiological variables during the wake state); 2) circadian rhythms; and 3) non-endogenous daily rhythms that are shown to be imposed on top of circadian rhythms, such as work schedules. 4) weekly rhythms, or other exogenously imposed artificial time cycles (e.g. in virtual cultures with 12-day "weeks", a 12-day rhythm could be used); 5) in women These include multi-day ovarian rhythms, in men spermatogenic rhythms; 6) lunar rhythms (associated with people living with little or no artificial light); and 7) seasonal rhythms.

Biological rhythms are not necessarily stationary rhythms. For example, many women experience variability in ovarian cycle length over multiple cycles, and ultradian rhythms are not expected to occur at exactly the same time or periodicity across days, even within a user. Thus, signal processing techniques sufficient to quantify the frequency composition while preserving the temporal resolution of these rhythms in the physiological data can be used to improve detection of these rhythms and assign a phase of each rhythm to each instant in time measured, thereby modifying adjustment models and comparisons of time intervals. Biological rhythm-adjustment models and parameters can be added in linear or nonlinear combinations as needed to more accurately capture the dynamic physiological baseline of an individual or group of individuals.

In some aspects, each device of system 100 may support technology for anovulatory cycle detection based on data collected by wearable device 104. In particular, the system 100 shown in FIG. 1 supports techniques for identifying one or more anovulatory cycles of a user 102 and causing the user device 106 to display an indicator of the one or more identified anovulatory cycles. It is possible. For example, as shown in FIG. 1, User 1 (user 102-a) may be associated with wearable device 104-a (eg, ring 104-a) and user device 106-a. In this example, ring 104-a may collect data associated with user 102-a, including body temperature, heart rate, respiratory rate, HRV data, and the like. In some aspects, data collected by ring 104-a may be used to identify one or more ovulatory cycles during which user 1 experiences ovulation or release of an egg from the ovary. In some examples, data collected by ring 104-a may be used to identify one or more anovulatory cycles in which user 1 does not experience ovulation or release of an egg from the ovary.

Identifying one or more anovulatory cycles may be performed by any of the components of system 100, including ring 104-a, user device 106-a associated with user 1, one or more servers 110, or any combination thereof. It can be performed by either. Upon identifying one or more anovulatory cycles, system 100 may selectively cause the GUI of user device 106 to display an indicator of the identified one or more anovulatory cycles. In such cases, user device 106 may be associated with User 1, User 2, User N, or a combination thereof, where User 2 and User N are associated with User 1's associated clinician, fertility specialist, It may be an example of a user or a combination thereof.

In some implementations, upon receiving physiological data (e.g., including body temperature data), the system 100 may determine a time series of body temperature values obtained over multiple days. The system 100 may identify one or more ovulatory cycles in the time series based on one or more positive slopes of the time series of body temperature values. In such cases, the system 100 may identify one or more ovulatory cycles, where each ovulatory cycle of the one or more ovulatory cycles is associated with a positive slope in the time series. The system 100 may identify one or more anovulatory cycles in the time series based on deviations of the body temperature values from the identified ovulatory cycles. In such cases, the system 100 may identify a decrease in body temperature values, an absence of body temperature values, or both.

In some implementations, system 100 sends alerts, messages, or educational content (e.g., ring 104-a) for User 1, User 2, and/or User N based on the identified anovulatory cycles. , user device 106-a, or both), where the message may provide insight regarding the identified one or more ovulatory cycles and/or one or more anovulatory cycles. In some cases, the message includes symptoms associated with the identified one or more ovulatory cycles and/or one or more anovulatory cycles, the identified one or more ovulatory cycles, and/or the one or more anovulatory cycles. one or more medical conditions associated with an anovulatory cycle, one or more ovulatory cycles identified and/or educational videos and/or text (e.g. content) associated with one or more anovulatory cycles, or menstruation. It may provide insight into those combinations associated with any phase of the cycle.

It should be understood by those skilled in the art that one or more aspects of the present disclosure may be implemented in system 100 to additionally or alternatively solve other problems than those described above. Additionally, aspects of the present disclosure may provide technical improvements to the "traditional" systems or processes described herein. However, the specification and accompanying drawings include only exemplary technical improvements that result from implementing aspects of the disclosure, and therefore all technical improvements that are provided within the scope of the claims. It does not represent.

FIG. 2 illustrates an example system 200 that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure. System 200 may implement system 100 or may be implemented by system 100. In particular, system 200 illustrates an example of ring 104 (eg, wearable device 104), user device 106, and server 110, as described with reference to FIG.

In some aspects, ring 104 may be configured to be worn around a user's finger and may determine one or more physiological parameters of the user when worn around the user's finger. . Exemplary measurements and determinations may include, but are not limited to, the user's skin temperature, pulse waveform, breathing rate, heart rate, HRV, blood oxygen level, and the like.

System 200 further includes a user device 106 (eg, a smartphone) in communication with ring 104. For example, ring 104 may be in wireless and/or wired communication with user device 106 . In some implementations, ring 104 transmits measured and processed data (e.g., body temperature data, photoplethysmogram (PPG) data, motion/accelerometer data, ring input data, etc.) to user device 106. obtain. User device 106 may also send data to ring 104, such as ring 104 firmware/configuration updates. User device 106 may process data. In some implementations, user device 106 may transmit data to server 110 for processing and/or storage.

Ring 104 may include a housing 205 that may include an inner housing 205-a and an outer housing 205-b. In some aspects, the housing 205 of the ring 104 includes, but is not limited to, device electronics, a power source (e.g., a battery 210 and/or a capacitor), one or more substrates (e.g., Various components of the ring may be stored or otherwise included, including printed circuit boards) and the like. Device electronics may include device modules (eg, hardware/software) such as processing module 230-a, memory 215, communication module 220-a, power module 225, etc. The device electronics may also include one or more sensors. Exemplary sensors may include one or more temperature sensors 240, a PPG sensor assembly (eg, PPG system 235), and one or more motion sensors 245.

The sensors may include associated modules (not shown) configured to communicate with respective components/modules of ring 104 and generate signals associated with the respective sensors. In some aspects, each of the components/modules of ring 104 may be communicatively coupled to each other via wired or wireless connections. Additionally, ring 104 may include additional and/or alternative sensors or other components configured to collect physiological data from the user, including optical sensors (eg, LEDs), oximeters, and the like.

The ring 104 illustrated and described with reference to FIG. 2 is provided for illustrative purposes only. As such, ring 104 may include additional or alternative components such as those shown in FIG. Other rings 104 may be manufactured that provide the functionality described herein. For example, a ring 104 may be manufactured with fewer components (eg, sensors). In a particular example, a ring 104 having a single temperature sensor 240 (or other sensor), a power source, and device electronics configured to read the single temperature sensor 240 (or other sensor) is manufactured. may be done. In another example, temperature sensor 240 (or other sensor) may be attached to a user's finger (eg, using a clamp, spring-loaded clamp, etc.). In this case, the sensor may be wired to another computing device, such as a wrist-worn computing device, that reads temperature sensor 240 (or other sensor). In other examples, rings 104 may be manufactured that include additional sensors and processing functionality.

Housing 205 may include one or more housing 205 components. Housing 205 may include an outer housing 205-b component (eg, a shell) and an inner housing 205-a component (eg, a molding). Housing 205 may include additional components (eg, additional layers) not explicitly shown in FIG. 2. For example, in some implementations, ring 104 includes one or more insulators that electrically isolate device electronics and other conductive materials (e.g., electrical traces) from outer housing 205 (e.g., metallic outer housing 205-b). It may include layers. Housing 205 may provide structural support for device electronics, battery 210, substrates, and other components. For example, the housing 205 may protect the device electronics, battery 210, and substrate from mechanical forces such as pressure and shock. Housing 205 may also protect the device electronics, battery 210, and substrate from water and/or other chemicals.

Outer housing 205-b may be manufactured from one or more materials. In some implementations, the outer housing 205-b may include a metal, such as titanium, which may be relatively lightweight and provide strength and wear resistance. Outer housing 205-b may also be manufactured from other materials such as polymers. In some implementations, outer housing 205-b may be decorative as well as protective.

Inner housing 205-a may be configured to interface with a user's finger. Inner housing 205-a may be formed from a polymer (eg, medical grade polymer) or other material. In some implementations, inner housing 205-a may be transparent. For example, the inner housing 205-a may be transparent to light emitted by a PPG light emitting diode (LED). In some implementations, inner housing 205-a may be molded over outer housing 205-b. For example, the inner housing 205-a may include a polymer that is molded (eg, injection molded) to fit the outer housing 205 metal shell.

Ring 104 may include one or more substrates (not shown). Device electronics and battery 210 may be included on one or more substrates. For example, device electronics and battery 210 may be mounted on one or more substrates. Exemplary substrates may include one or more printed circuit boards (PCBs), such as flexible PCBs (eg, polyimide). In some implementations, electronics/battery 210 may include a surface-mounted device (eg, a surface-mount technology (SMT) device) on a flexible PCB. In some implementations, one or more substrates (eg, one or more flexible PCBs) may include electrical traces that provide electrical communication between device electronics. Electrical traces may also connect battery 210 to device electronics.

The device electronics, battery 210, and substrate may be arranged within the ring 104 in a variety of ways. In some implementations, one substrate containing the device electronics may be mounted along the bottom (e.g., bottom half) of the ring 104 such that the sensors (e.g., PPG system 235, temperature sensor 240, motion sensor 245, and other sensors) interface with the underside of the user's finger. In these implementations, the battery 210 may be included (e.g., on a separate substrate) along the top of the ring 104.

The various components/modules of ring 104 represent functionality (eg, circuits and other components) that may be included in ring 104. A module may include any discrete and/or integrated electronic circuit components implementing analog and/or digital circuitry capable of producing the functionality attributed to the module herein. For example, a module may include analog circuitry (eg, amplifier circuitry, filtering circuitry, analog-to-digital conversion circuitry, and/or other signal conditioning circuitry). The module may also include digital circuits (eg, combinatorial or sequential logic circuits, memory circuits, etc.).

The memory 215 (memory module) of the ring 104 can be random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any It may include any volatile, non-volatile, magnetic or electrical medium, such as other memory devices. Memory 215 may store any of the data described herein. For example, memory 215 may be configured to store data collected by the respective sensors and PPG system 235 (eg, exercise data, body temperature data, PPG data). Additionally, memory 215 may include instructions that, when executed by one or more processing circuits, cause the modules to perform various functions attributed to the modules herein. The device electronics of ring 104 described herein is only one example of device electronics. Accordingly, the types of electronic components used to implement device electronics may vary based on design considerations.

The functionality attributed to the modules of ring 104 described herein may be implemented as one or more processors, hardware, firmware, software, or any combination thereof. The depiction of different features as modules is intended to emphasize different functional aspects and is not necessarily to imply that such modules must be implemented by separate hardware/software components. do not have. Rather, the functionality associated with one or more modules may be performed by separate hardware/software components or may be integrated within a common hardware/software component.

Processing modules 230-a of ring 104 may include one or more processors (eg, processing units), microcontrollers, digital signal processors, systems on a chip (SoC), and/or other processing devices. Processing module 230-a communicates with modules included in ring 104. For example, processing module 230-a may transmit data to/receive data from modules and other components of ring 104, such as sensors. As described herein, modules may be implemented by various circuit components. Accordingly, modules may also be referred to as circuits (eg, communication circuits and power circuits).

Processing module 230-a may communicate with memory 215. Memory 215 may include computer readable instructions that, when executed by processing module 230-a, cause processing module 230-a to perform various functions attributable to processing module 230-a. In some implementations, processing module 230-a (e.g., a microcontroller) is provided by communication module 220-a (e.g., an integrated Bluetooth Low Energy transceiver) and/or additional onboard memory 215. It may also include additional features associated with other modules, such as communication functionality.

Communication module 220-a may include circuitry to provide wireless and/or wired communication with user device 106 (eg, communication module 220-b of user device 106). In some implementations, communication modules 220-a, 220-b may include wireless communication circuitry, such as Bluetooth circuitry and/or Wi-Fi circuitry. In some implementations, communication modules 220-a, 220-b may include wired communication circuitry, such as universal serial bus (USB) communication circuitry. Using communication module 220-a, ring 104 and user device 106 may be configured to communicate with each other. The processing module 230-a of the ring may be configured to send data to/receive data from the user device 106 via the communication module 220-a. Exemplary data may include exercise data, body temperature data, pulse waveforms, heart rate data, HRV data, PPG data, and status updates (e.g., charging status, battery charge level, and/or ring 104 configuration settings). Not limited. The ring's processing module 230-a may also be configured to receive updates (eg, software/firmware updates) and data from the user device 106.

Ring 104 may include a battery 210 (eg, rechargeable battery 210). Exemplary batteries 210 may include lithium ion or lithium polymer type batteries 210, although a variety of battery 210 options are possible. Battery 210 may be charged wirelessly. In some implementations, ring 104 may include a power source other than battery 210, such as a capacitor. The power source (eg, battery 210 or capacitor) may have a curved shape that matches the curve of ring 104. In some aspects, the charger or other power source may include additional sensors that may be used to collect data in addition to, or supplement the data collected by the ring 104 itself. . Additionally, a charger or other power source for ring 104 may function as a user device 106 , in which case the charger or other power source for ring 104 receives data from ring 104 and receives data from ring 104 . may be configured to store and/or process data and communicate data between ring 104 and server 110.

In some aspects, ring 104 includes a power module 225 that may control charging of battery 210. For example, power module 225 may interface with an external wireless charger that charges battery 210 when interfacing with ring 104. The charger may include a data structure that mates with a datum structure of ring 104 to create a particular orientation with ring 104 during charging of 104. Power module 225 may also regulate the voltage of the device electronics, regulate power output to the device electronics, and monitor the state of charge of battery 210. In some implementations, battery 210 may include a protection circuit module (PCM) that protects battery 210 from high current discharge, overvoltage during charging 104, and undervoltage during discharging 104. Power module 225 may also include electrostatic discharge (ESD) protection.

One or more temperature sensors 240 may be electrically coupled to processing module 230-a. Temperature sensor 240 may be configured to generate a body temperature signal (eg, body temperature data) indicative of body temperature read or sensed by temperature sensor 240 . Processing module 230-a may determine the user's body temperature at the location of temperature sensor 240. For example, in ring 104, body temperature data generated by temperature sensor 240 may indicate the user's body temperature (eg, skin temperature) at the user's finger. In some implementations, temperature sensor 240 may contact the user's skin. In other implementations, a portion of housing 205 (eg, inner housing 205-a) may form a barrier (eg, a thin thermally conductive barrier) between temperature sensor 240 and the user's skin. In some implementations, the portion of ring 104 that is configured to contact the user's finger may have a thermally conductive portion and an insulating portion. The thermally conductive portion may conduct heat from the user's finger to temperature sensor 240. The insulating portion may insulate portions of ring 104 (eg, temperature sensor 240) from ambient temperature.

In some implementations, temperature sensor 240 may generate a digital signal (eg, body temperature data) that processing module 230-a may use to determine body temperature. As another example, if temperature sensor 240 includes a passive sensor, processing module 230-a (or temperature sensor 240 module) measures the current/voltage generated by temperature sensor 240 and applies the measured current/voltage to Body temperature can be determined based on. Exemplary temperature sensor 240 includes a thermistor, such as a negative temperature coefficient (NTC) thermistor, or other types of sensors including resistors, transistors, diodes, and/or other electrical/electronic components. may be included.

Processing module 230-a may sample the user's body temperature over time. For example, processing module 230-a may sample the user's body temperature according to a sampling rate. Although exemplary sampling rates may include one sample per second, processing module 230-a may be configured to sample the body temperature signal at other sampling rates higher or lower than one sample per second. Good too. In some implementations, processing module 230-a may sample the user's body temperature continuously throughout the day and night. Sampling at a sufficient rate throughout the day (eg, one sample per second, one sample per minute, etc.) may provide sufficient body temperature data for the analyzes described herein.

The processing module 230-a may store the sampled body temperature data in the memory 215. In some implementations, the processing module 230-a may process the sampled body temperature data. For example, the processing module 230-a may determine an average body temperature value for a period of time. In one example, the processing module 230-a may determine an average body temperature value per minute by summing all body temperature values collected in one minute and dividing by the number of samples in one minute. In a particular example where the body temperature is sampled at one sample per second, the average body temperature may be the sum of all sampled body temperatures for one minute divided by 60 seconds. The memory 215 may store the average body temperature value over time. In some implementations, the memory 215 may store the average body temperature (e.g., one per minute) instead of the sampled body temperatures to conserve memory 215.

The sampling rate that may be stored in memory 215 may be configurable. In some implementations, the sampling rate may be the same throughout the day and night. In other implementations, the sampling rate may be changed throughout the day/night. In some implementations, ring 104 may filter/reject body temperature readings, such as large spikes in body temperature that do not indicate a physiological change (eg, a temperature spike from a hot shower). In some implementations, ring 104 filters temperature readings that may be unreliable due to other factors, such as excessive exercise during exercise of 104 (e.g., as indicated by motion sensor 245). / You may refuse.

Ring 104 (eg, a communication module) may transmit the sampled body temperature data and/or average body temperature data to user device 106 for storage and/or further processing. User device 106 may forward the sampled body temperature data and/or average body temperature data to server 110 for storage and/or further processing.

Although ring 104 is shown to include a single temperature sensor 240, ring 104 may include one or more temperature sensors 240 located along inner housing 205-a near a user's finger. A plurality of temperature sensors 240 may be included. In some implementations, temperature sensor 240 may be a standalone temperature sensor 240. Additionally or alternatively, one or more temperature sensors 240 may be included (eg, packaged with other components), such as an accelerometer and/or a processor.

Processing module 230-a may obtain and process data from multiple temperature sensors 240 in a manner similar to that described with respect to a single temperature sensor 240. For example, processing module 230 may individually sample, average, and store body temperature data from each of multiple temperature sensors 240. In other examples, processing module 230-a may sample the sensors at different rates and average/store different values for different sensors. In some implementations, processing module 230-a is configured to determine a single body temperature based on an average of two or more body temperatures determined by two or more temperature sensors 240 at different locations on the finger. may be configured.

A temperature sensor 240 on ring 104 may capture a distal temperature at a user's finger (eg, any finger). For example, one or more temperature sensors 240 on ring 104 may capture the user's temperature from the underside of the finger or at different locations on the finger. In some implementations, ring 104 may continuously acquire distal body temperature (eg, at a sampling rate). Although distal temperature is described herein as measured by a ring 104 on the finger, other devices may measure body temperature at the same/different location. In some cases, the distal temperature measured at the user's finger may be different than the temperature measured at the user's wrist or other external body location. Additionally, the distal body temperature (eg, "shell" body temperature) measured on the user's finger may be different from the user's core body temperature. In this manner, ring 104 may provide useful body temperature signals that may not be obtained at other internal/external locations of the body. In some cases, continuous finger temperature measurements may capture body temperature fluctuations (eg, small or large fluctuations) that may not be evident in core body temperature. Continuous temperature measurements, for example with a finger, may capture minute-to-minute or hour-to-hour body temperature fluctuations that provide additional insight that may not be provided by other temperature measurements elsewhere on the body.

Ring 104 may include a PPG system 235. PPG system 235 may include one or more optical transmitters that transmit light. PPG system 235 may also include one or more optical receivers that receive light transmitted by one or more optical transmitters. An optical receiver may generate a signal (hereinafter referred to as a "PPG" signal) indicative of the amount of light received by the optical receiver. The light transmitter may illuminate the area of the user's finger. The PPG signal generated by PPG system 235 may indicate blood perfusion in the illuminated area. For example, the PPG signal may indicate changes in blood volume in the illuminated area caused by the user's pulse pressure. Processing module 230-a may sample the PPG signal and determine the user's pulse waveform based on the PPG signal. Processing module 230-a may determine various physiological parameters of the user, such as breathing rate, heart rate, HRV, oxygen saturation, and other circulatory parameters, based on the user's pulse waveform.

In some implementations, PPG system 235 may be configured as a reflective PPG system 235, in which the optical receiver receives transmitted light that is reflected through the user's finger area. In some implementations, the PPG system 235 is a transmissive system in which the optical transmitter and optical receiver are placed opposite each other such that the light is transmitted through a portion of the user's finger directly to the optical receiver. The system may be configured as a type PPG system 235.

The number and ratio of transmitters and receivers included in PPG system 235 may vary. Exemplary optical transmitters may include light emitting diodes (LEDs). Optical transmitters may transmit light in the infrared spectrum and/or other spectra. Exemplary optical receivers may include, but are not limited to, photosensors, phototransistors, and photodiodes. The optical receiver may be configured to generate a PPG signal in response to the wavelength received from the optical transmitter. The locations of the transmitter and receiver may vary. Additionally, a single device may include reflective and/or transmissive PPG systems 235.

The PPG system 235 shown in FIG. 2 may include a reflective PPG system 235 in some implementations. In these implementations, PPG system 235 may include a centrally located optical receiver (eg, at the bottom of ring 104) and two optical transmitters located on either side of the optical receiver. In this implementation, PPG system 235 (eg, an optical receiver) may generate a PPG signal based on light received from one or both of the optical transmitters. In other implementations, other arrangements, combinations and/or configurations of one or more optical transmitters and/or optical receivers are contemplated.

Processing module 230-a may control one or both of the optical transmitters to transmit light while sampling the PPG signal generated by the optical receiver. In some implementations, processing module 230-a may cause the optical transmitter with the stronger received signal to transmit light while sampling the PPG signal generated by the optical receiver. For example, a selected optical transmitter may continuously emit light while the PPG signal is sampled at a sampling rate (eg, 250 Hz).

By sampling the PPG signal generated by PPG system 235, a pulse waveform that may be referred to as a "PPG" may be obtained. The pulse waveform may indicate blood pressure versus time for multiple cardiac cycles. The pulse waveform may include peaks indicative of cardiac cycles. Additionally, the pulse waveform may include respiratory induced variation that may be used to determine respiratory rate. In some implementations, processing module 230-a may store the pulse waveform in memory 215. Processing module 230-a may process the pulse waveforms as they are generated and/or from memory 215 to determine the user's physiological parameters as described herein.

Processing module 230-a may determine the user's heart rate based on the pulse waveform. For example, processing module 230-a may determine heart rate (eg, beats per minute) based on the time between peaks of the pulse waveform. The time between peaks is sometimes called the interbeat interval (IBI). Processing module 230-a may store the determined heart rate value and IBI value in memory 215.

Processing module 230-a may determine HRV over time. For example, processing module 230-a may determine HRV based on variations in IBl. Processing module 230-a may store HRV values over time in memory 215. Additionally, processing module 230-a may determine the user's breathing rate over time. For example, processing module 230-a may determine the respiration rate based on frequency modulation, amplitude modulation, or baseline modulation of the user's IBI value over a period of time. The respiratory rate may be calculated in breaths per minute or as another respiratory rate (eg, breaths per 30 seconds). Processing module 230-a may store values of the user's breathing rate over time in memory 215.

Ring 104 may include one or more motion sensors 245, such as one or more accelerometers (eg, 6-D accelerometers) and/or one or more gyroscopes (gyros). Motion sensor 245 may generate a motion signal indicative of sensor motion. For example, ring 104 may include one or more accelerometers that generate acceleration signals indicative of accelerometer acceleration. As another example, ring 104 may include one or more gyro sensors that generate gyro signals indicative of changes in angular movement (eg, angular velocity) and/or orientation. Motion sensor 245 may be included in one or more sensor packages. An exemplary accelerometer/gyro sensor is a Bosch® BMI160 inertial micro electro-mechanical system (MEMS) sensor that can measure angular velocity and acceleration in three vertical axes.

Processing module 230-a may sample the motion signal at a sampling rate (eg, 50 Hz) and determine motion of ring 104 based on the sampled motion signal. For example, processing module 230-a may sample the acceleration signal to determine the acceleration of ring 104. As another example, processing module 230-a may sample the gyro signal to determine angular motion. In some implementations, processing module 230-a may store athletic data in memory 215. The motion data may include sampled motion data as well as motion data calculated based on the sampled motion signals (eg, acceleration and angle values).

Ring 104 may store various data described herein. For example, ring 104 may store temperature data such as raw sampled temperature data and calculated temperature data (eg, average temperature). As another example, ring 104 stores PPG signal data such as pulse waveforms and data calculated based on the pulse waveforms (e.g., heart rate values, IBI values, HRV values, and respiratory rate values). good. Ring 104 may also store motion data, such as sampled motion data indicative of linear and angular motion.

Ring 104 or other computing device may calculate and store additional values based on the sampled/calculated physiological data. For example, processing module 230 may calculate and store various metrics, such as sleep metrics (eg, sleep scores), activity metrics, and readiness metrics. In some implementations, additional values/metrics may be referred to as "derived values." Ring 104 or other computing/wearable device may calculate various values/metrics regarding exercise. Exemplary derived values for movement data may include, but are not limited to, movement counts, regularity values, intensity values, metabolic equivalence of task values (METs), and direction values. Not done. Movement counts, regularity values, intensity values, and METs may indicate the amount of movement (eg, velocity/acceleration) of the user over time. The orientation value may indicate how ring 104 is oriented relative to the user's finger and whether ring 104 is worn on the left or right hand.

In some implementations, motion counts and regularity values may be determined by counting the number of acceleration peaks within one or more time periods (eg, one or more 30 second to 1 minute time periods). The intensity value may indicate the number of movements and the associated intensity of the movements (eg, an acceleration value). Intensity values may be classified as low, medium and high depending on the associated threshold acceleration value. METs may be determined based on the intensity of movement, regularity/irregularity of movement, and number of movements associated with different intensities over a period of time (eg, 30 seconds).

In some implementations, processing module 230-a may compress data stored in memory 215. For example, processing module 230-a may delete the sampled data after performing calculations based on the sampled data. As another example, processing module 230-a may average data over a longer period of time to reduce the number of values stored. In a specific example, if the user's average body temperature over one minute is stored in memory 215, processing module 230-a calculates the average body temperature for five minutes for storage, and then calculates the average body temperature for that one minute. You may also delete body temperature data. Processing module 230-a may process data based on various factors, such as the total amount of used/available memory 215 and/or the amount of time elapsed since ring 104 last sent data to user device 106. may be compressed.

Although the user's physiological parameters may be measured by sensors included in ring 104, other devices may measure the user's physiological parameters. For example, the user's body temperature may be measured by temperature sensor 240 included in ring 104, although other devices may measure the user's body temperature. In some examples, other wearable devices (eg, wrist devices) may include sensors that measure physiological parameters of the user. Additionally, medical devices, such as external medical devices (eg, wearable medical devices) and/or implantable medical devices, may measure physiological parameters of the user. One or more sensors on any type of computing device may be used to implement the techniques described herein.

Physiological measurements may be taken continuously throughout the day and/or night. In some implementations, physiological measurements may be taken during the day portion and/or night portion of 104. In some implementations, physiological measurements may be taken in response to a user determining that they are in a particular state, such as an active state, a resting state, and/or a sleeping state. For example, ring 104 can perform physiological measurements in a resting/sleeping state to obtain a cleaner physiological signal. In one example, the ring 104 or other device/system may detect when the user is at rest and/or sleeping and obtain physiological parameters (eg, body temperature) of the detected condition. The device/system may use rest/sleep physiological data and/or other data when the user is in other states to implement the techniques of this disclosure.

In some implementations, ring 104 may be configured to collect, store, and/or process data, as previously described herein, and may include any of the data described herein. The information may be transferred to user device 106 for storage and/or processing. In some aspects, user device 106 includes a wearable application 250, an operating system (OS), a web browser application (eg, web browser 280), one or more additional applications, and a GUI 275. User device 106 may further include other modules and components, including sensors, audio devices, haptic feedback devices, and the like. Wearable applications 250 may include examples of applications (eg, “apps”) that may be installed on user device 106. Wearable application 250 may be configured to obtain data from ring 104, store the obtained data, and process the obtained data as described herein. For example, the wearable application 250 includes a user interface (UI) module 255, an acquisition module 260, a processing module 230-b, a communication module 220-b, and a storage module configured to store application data (e.g., a database 265). ) may be included.

The various data processing operations described herein may be performed by ring 104, user device 106, server 110, or any combination thereof. For example, in some cases, data collected by ring 104 may be pre-processed and sent to user device 106. In this example, user device 106 may perform some data processing operations on the received data, may transmit the data to server 110 for data processing, or both. For example, in some cases, user device 106 may perform processing operations that require relatively low processing power and/or operations that require relatively low latency, whereas user device 106 may perform processing operations that require relatively low processing power and/or operations that require relatively low latency; Data may be sent to server 110 for processing operations that require processing power and/or that may tolerate relatively high latency.

In some aspects, ring 104, user device 106, and server 110 of system 200 may be configured to assess a user's sleep pattern. In particular, each component of system 200 collects data from a user via ring 104 and generates one or more scores (e.g., sleep score, readiness score) for the user based on the collected data. can be used for For example, as previously described herein, ring 104 of system 200 may be worn by a user to collect data from the user including body temperature, heart rate, HRV, etc. The data collected by ring 104 may be used to determine when the user is asleep in order to evaluate the user's sleep for a given "sleep day." In some aspects, a score may be calculated for each sleep day for the user such that a first sleep day is associated with a first set of scores and a second sleep day is associated with a second set of scores. A score may be calculated for each sleep day based on data collected by ring 104 during each sleep day. Scores include, but are not limited to, sleep scores, readiness scores, etc.

In some cases, "sleep days" may align with traditional calendar days such that a given sleep day lasts from midnight to midnight of the respective calendar day. In other cases, sleep days may be offset relative to calendar days. For example, a sleep day may last from 6:00 PM (18:00) on one calendar day to 6:00 PM (18:00) on the next calendar day. In this example, 6:00 p.m. may serve as a "cutoff time," where data collected from the user before 6:00 p.m. is counted for the current sleep day, and after 6:00 p.m. Data collected from the user is then counted for the next sleep day. Due to the fact that most individuals sleep the most at night, by offsetting the sleep days relative to the calendar day, the system 200 assesses the user's sleep pattern in a manner that is consistent with the user's sleep schedule. be able to. In some cases, users can selectively adjust the timing of their sleep days relative to the calendar day (e.g., via a GUI) to align the sleep days with the length of time each user typically sleeps. can do.

In some implementations, each daily user's overall score (e.g., sleep score, readiness score) is determined by one or more "contributors," "factors," or "contributing factors." )” may be determined/calculated based on For example, a user's overall sleep score may be calculated based on a set of contributing factors including total sleep, efficiency, rest, REM sleep, deep sleep, latency, timing, or any combination thereof. A sleep score may include any amount of contributing factors. The "total sleep" contribution factor may refer to the sum of all sleep periods on a sleep day. The "efficiency" contributing factor may reflect the percentage of time spent sleeping compared to the time spent while awake while in bed, and includes longer sleep periods during the sleep day (e.g. primary sleep periods). period)), weighted by the duration of each sleep period. The "rest" contribution factor may indicate how restful a user's sleep is and may be calculated using the average of all sleep periods on a sleep day, weighted by the duration of each period. Rest contributing factors include "wake-up count" (e.g. the sum of all wake-ups detected during different sleep periods (when the user wakes up)), excessive movement, and "got-up count" (e.g. the sum of all wake-ups detected during different sleep periods), excessive movement ``got up count'' (e.g. the sum of all got ups (when the user gets out of bed) detected during different sleep periods).

The "REM sleep" contribution factor may refer to the sum of REM sleep duration over all sleep periods of the sleep day, including REM sleep. Similarly, a "deep sleep" contribution factor may refer to the sum of deep sleep durations across all sleep periods on sleep days that include deep sleep. "Latency" contributing factor may mean the time (e.g., average, median, longest) it takes for a user to enter sleep, using an average of long sleep periods throughout the sleep day to determine the duration and duration of each period. It may be calculated weighted by the number of such periods (e.g. the integration of a given sleep stage may be its own contributing factor or may weight other contributing factors). Finally, the "timing" contributing factor may refer to the relative timing of sleep periods within a sleep day and/or calendar day, weighted by the duration of each period using the average of all sleep periods within a sleep day. can be calculated as follows.

As another example, a user's overall readiness score is based on a set of contributing factors including sleep, sleep balance, heart rate, HRV balance, recovery index, body temperature, activity, activity balance, or any combination thereof. It can be calculated by A readiness score may include any amount of contributing factors. The "sleep" contribution factor may refer to the combined sleep score of all sleep periods within a sleep day. A "sleep balance" contributing factor may refer to the cumulative duration of all sleep periods within a sleep day. In particular, sleep balance can indicate to a user whether the sleep that the user has been getting over a period of time (eg, the past two weeks) is balanced with the user's needs. Typically, adults need 7 to 9 hours of sleep per night to stay healthy, alert, and perform at their best mentally and physically. However, it is normal to have a bad night's sleep from time to time, so the sleep balance contributor factor takes into account long-term sleep patterns to determine whether each user's sleep needs are being met. The "resting heart rate" contributing factor may indicate the lowest heart rate from the longest sleep period (eg, main sleep period) of the sleep day and/or the lowest heart rate from a nap that occurs after the main sleep period.

Continuing with the "contributing factors" (eg, factors, contributing factors) of the readiness score, the "HRV Balance" contributing factor may indicate the highest HRV average from the main sleep period and naps that occur after the main sleep period. The HRV Balance Contributor allows a user to track their recovery status by comparing their HRV trends over a first period (e.g., two weeks) to their average HRV over some longer second period (e.g., three months). can help. A "recovery index" contributing factor may be calculated based on the longest sleep period. Recovery Index measures the time it takes for a user's resting heart rate to stabilize during the night. A sign of very good recovery is that the user's resting heart rate stabilizes during the first half of the night, at least six hours before the user wakes up, leaving the body time to recover for the next day. The "body temperature" contributing factor is based on the longest sleep period (e.g. main sleep period) or the maximum sleep period if the user's maximum body temperature during the nap is at least 0.5°C higher than the maximum body temperature during the longest period. It can be calculated based on subsequent naps. In some aspects, the ring may measure the user's temperature while the user is sleeping, and the system 200 may display the user's average body temperature relative to the user's baseline temperature. If the user's body temperature is outside the normal range (e.g., clearly above or below 0.0), the temperature contributing factor may be highlighted (e.g., placed in a "Pay attention" state) or other Alerts may be generated to the user in a manner.

In some aspects, system 200 may support techniques for anovulatory cycle detection. In particular, each component of system 200 may be used to identify one or more anovulatory cycles in a time series representing a user's body temperature over time. A user's anovulatory cycles may be predicted by utilizing a temperature sensor on ring 104 of system 200. In some cases, the anovulatory cycle identifies one or more morphological characteristics, such as a temperature deviation in a time series representing the user's body temperature over time, and one or more morphological features, each corresponding to a temperature deviation in the time series. It can be estimated by identifying anovulatory cycles.

For example, as previously described herein, ring 104 of system 200 may be worn by a user to collect data from the user including temperature, heart rate, HRV, respiratory data, and the like. Ring 104 of system 200 may collect physiological data from the user based on temperature sensors and measurements extracted from arterial blood flow (eg, using PPG signals). Physiological data may be collected continuously. In some implementations, processing module 230-a may sample the user's body temperature continuously throughout the day and night. Sampling at a sufficient rate (eg, one sample per minute) throughout the day and/or night may provide sufficient body temperature data for the analyzes described herein. In some implementations, ring 104 may continuously acquire body temperature data (eg, at a sampling rate). In some examples, even if the temperature is continuously collected, the system 200 may collect other information about the user (e.g., sleep stage, activity level, illness) that the system 200 collects or otherwise derives. (e.g., the onset of cancer) can be used to select a representative body temperature for a particular day that is an accurate representation of the underlying physiological phenomenon.

In contrast, systems that require the user to manually measure body temperature every day, and/or systems that measure body temperature continuously but lack other contextual information about the user, are not suitable for ovulation tracking. Inaccurate or inconsistent body temperature values may be selected, leading to inaccurate predictions and a degraded user experience. In contrast, data collected by ring 104 may be used to accurately determine when a user is ovulating and when the user is experiencing an anovulatory cycle. The identified anovulatory cycles and related techniques are further illustrated and described in connection with FIGS. 3 and 4.

FIG. 3 illustrates an example system 300 that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure. System 300 may implement or be implemented by system 100, system 200, or both. In particular, system 300 illustrates an example of ring 104 (eg, wearable device 104), user device 106, and server 110, as described with reference to FIG.

Ring 305 may acquire temperature data 320, heart rate data 325, respiration rate data 330, and HRV data 335, among other forms of physiological data as described herein. In such a case, ring 305 may transmit temperature data 320, heart rate data 325, respiration rate data 330, and HRV data 335 to user device 310. Body temperature data 320 may include continuous nighttime temperature data, continuous daytime temperature data, or both. In some cases, multiple devices may acquire physiological data. For example, a first computing device (e.g., user device 310) and a second computing device (e.g., ring 305) may obtain body temperature data 320, heart rate data 325, respiration rate data 330, HRV data 335, or a combination thereof. Good too. Breathing rate data 330 is an example of the breathing rate during sleep.

For example, the ring 305 may record information such as the user's body temperature data 320, respiration rate data 330, heart rate data 325, HRV data 335, galvanic skin response, blood oxygen saturation, actigraphy and/or other user physiological data. Physiological data of the user may be obtained. For example, ring 305 may capture raw data and transform the raw data into daily granularity features. In some implementations, different granularity of input data may be used. Ring 305 may transmit the data to another computing device, such as a mobile device (eg, user device 310), for further processing.

For example, user device 310 may determine menstrual/ovarian cycle tracking data (eg, ovulatory cycles and anovulatory cycles) based on the received data. In some cases, the system 300 determines the ovarian cycle based on body temperature data 320, respiration rate data 330, heart rate data 325, HRV data 335, galvanic skin response, blood oxygen saturation, activity, sleep architecture, or a combination thereof. Tracking data may be determined. In some cases, system 300 may determine which features are useful predictors of anovulatory cycles. Although the system may be implemented by ring 305 and user device 310, any combination of computing devices described herein may implement features ascribed to system 300.

User device 310-a may include a ring application 350. Ring application 350 may include at least module 340 and application data 345. In some cases, application data 345 may include the user's historical temperature patterns and other data. Other data may include body temperature data 320, heart rate data 325, respiration rate data 330, HRV data 335, or combinations thereof.

Ring application 350 may present the identified ovulatory and anovulatory cycles to the user. Ring application 350 may include application data processing modules that may perform data processing. For example, application data processing modules may include a module 340 that provides functionality ascribed to system 300. Exemplary modules 340 may include a daily temperature determination module, a time series processing module, an ovulation identification module, and an anovulation identification module.

The daily body temperature determination module may determine daily body temperature values (eg, by selecting a representative body temperature value for the day from a series of body temperature values collected continuously throughout the day and/or night). A time series processing module may process the time series data to identify ovulatory and anovulatory cycles. Ovulation identification may identify ovulation cycles based on processed time series data. Anovulation identification may identify anovulatory cycles based on processed time series data. In such a case, system 300 may receive the user's physiological data (eg, from ring 305) and output a daily classification of whether the current cycle is ovulatory or anovulatory. Ring application 350 may store application data 345 such as captured body temperature data, other physiological data, and ovulation tracking data (eg, event data).

In some cases, system 300 may generate ovulation tracking data (eg, ovulatory cycle data and anovulatory cycle data) based on the user's physiological data (eg, body temperature data 320 and/or exercise data). Ovulation tracking data may include one or more ovulation cycles of a user, where the one or more ovulation cycles include user temperature data (e.g., daily) acquired over an analysis period (e.g., a period of weeks/months). body temperature data 320). For example, system 300 may receive physiological data associated with a user from a wearable device (eg, ring 305). The physiological data may include at least body temperature data 320, heart rate data 325, respiration rate data 330, HRV data 335, or a combination thereof. For example, system 300 acquires a user's physiological data over an analysis period (eg, multiple days). In such a case, system 300 may acquire and process the user's physiological data over the analysis period and generate one or more time series of the user's physiological data.

In some cases, system 300 may obtain daily user body temperature data 320 over an analysis period. For example, system 300 may calculate a single body temperature value each day. System 300 may acquire multiple body temperature values during the day and/or night and process the acquired body temperature values to determine a single daily body temperature value. In some implementations, system 300 may determine a time series of multiple body temperature values obtained over multiple days based on received body temperature data 320.

System 300 may identify ovulatory cycles in a time series of body temperature values based on one or more positive slopes of the time series of body temperature values, as described with reference to FIG. 4 . The system 300 may identify one or more indicators of anovulatory cycles in a time series of temperature values based on the deviation of the temperature values from the identified ovulatory cycles, as described with reference to FIG. .

In some implementations, the system 300 may determine that the received heart rate data 325 exceeds the user's threshold heart rate for at least a portion of multiple days. In such a case, the system 300 may identify one or more indicators of an anovulatory cycle in the time series based on determining that the received heart rate data 325 exceeds the user's threshold heart rate. In some examples, system 300 may determine that the received respiration rate data 330 exceeds the user's threshold respiration rate for at least a portion of multiple days. In such cases, system 300 may identify one or more indicators of an anovulatory cycle in the time series based on determining that the received respiration rate data 330 exceeds the user's threshold respiration rate.

In some implementations, the system 300 may determine that the received HRV data 335 is below the user's threshold HRV for at least a portion of multiple days. In such cases, the system 300 may identify one or more indicators of anovulatory cycles in the time series based on determining that the received HRV data 335 is below the user's threshold HRV. In such cases, thresholds (eg, body temperature, heart rate, respiratory rate, HRV) may be adjusted specific to the user based on historical data 355 obtained by system 300.

System 300 may cause the GUI of user devices 310-a, 310-b to display the identified ovulatory and anovulatory cycles. In some cases, the system 300 may display a time series on the GUI. System 300 may generate ovulation tracking data output. For example, system 300 generates a tracking GUI that includes physiological data (e.g., at least body temperature data 320), tagged events, and/or other GUI elements described herein with reference to FIG. Good too. In such cases, system 300 may render ovulatory and anovulatory cycles on the ovulation tracking GUI.

The system 300 may generate a message 365 indicating the identified indicators of anovulatory cycles for display on a GUI on the user device 310-a or 310-b. For example, the system 300 (e.g., the user device 310-a or the server 315) may send a message 365 indicating the identified indicators of one or more anovulatory cycles to the user device 310-b. In such a case, the user device 310-b may be associated with a clinician, fertility specialist, caregiver, partner, or a combination thereof. Detection of a possible anovulatory cycle may trigger a personalized message 365 to the user highlighting the detected pattern in the temperature data and providing educational links regarding anovulatory cycles.

In some implementations, the ring application 350 may notify the user of identified ovulation and anovulation cycles and/or prompt the user to perform various tasks in the activity GUI. The notifications and prompts may include text, graphics, and/or other user interface elements. Notifications and prompts may be included in the ring application 350, such as when there is just an identified or predicted ovulation and/or anovulation cycle, and the ring application 350 may display the notifications and prompts. The user device 310 may display the notifications and prompts in a separate window on the home screen and/or overlay on other screens (e.g., on top of the home screen). In some cases, the user device 310 may display the notifications and prompts on the mobile device, the user's watch device, or both.

In some implementations, user device 310 may store historical user data. In some cases, historical user data may include historical data 355. Historical data 355 may include a user's historical body temperature pattern, a user's historical heart rate pattern, a user's historical breathing rate pattern, a user's historical HRV pattern, a user's historical menstrual cycle start event (e.g., cycle length, cycle start date, etc.), or may include a combination of Historical data 355 may be selected from recent months. Historical data 355 may be used to determine the user's threshold, determine the user's body temperature value, identify the user's ovulatory cycle, identify the user's anovulatory cycle, or a combination thereof ( eg, by user device 310 or server 315). Using historical data 355, user device 310 and/or server 315 can personalize the GUI by considering the user's historical data 355.

In such a case, the user device 310 may transmit the history data 355 to the server 315. In some cases, the transmitted history data 355 may be the same as the history data stored in the ring application 350. In other examples, the history data 355 may be different than the history data stored in the ring application 350. The server 315 may receive the history data 355. The server 315 may store the history data 355 in server data 360.

In some implementations, user device 310 and/or server 315 may also store other data that may be examples of user information. User information may include, but is not limited to, the user's age, weight, height, and gender. In some implementations, user information may be used as a characteristic to identify ovulatory and anovulatory cycles. Server data 360 may also include other data such as user information.

In some implementations, the system 300 may include one or more user devices 310 for different users. For example, the system 300 may include a user device 310-a for a primary user and a user device 310-b for a secondary user 302 (e.g., a partner) associated with the primary user. The user devices 310 may measure physiological parameters of the different users, provide GUIs for the different users, and receive user input from the different users. In some implementations, the different user devices 310 may obtain physiological information and provide output related to female health, such as menstrual cycles, ovarian cycles, diseases, fertility, and/or pregnancy. In some implementations, the user device 310-b may obtain physiological information related to the secondary user 302, such as male diseases and fertility.

In some implementations, system 300 may provide a GUI that informs second user 302 of relevant information. For example, a first user and a second user 302 may share their information with each other via one or more user devices 310, such as via a server device, mobile device, or other device. In some implementations, the second user 302 may share one or more of the users' accounts (eg, usernames, login information, etc.) and/or related data with each other (eg, with the first user). By sharing information between users, system 300 may assist second user 302 in making health decisions related to fertility and ovulation. In some implementations, a user may be prompted (eg, in a GUI) to share certain information. For example, a user may choose to share her ovulation information with the second user 302 using the GUI. In such a case, the user and the second user 302 may receive notification of the ovulation window on their respective user devices 310. In other examples, the second user 302 may make its information (eg, disease, fertility data, etc.) available to users via a notification or other sharing arrangement. In such a case, the second user 302 may be an example of a clinician, a fertility specialist, a caregiver, a partner, or a combination thereof.

FIG. 4 illustrates an example timing diagram 400 that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure. Timing diagram 400 may implement aspects of or be implemented by system 100, system 200, system 300, or a combination thereof. For example, in some implementations, timing diagram 400 may be displayed to the user via GUI 275 of user device 106, as shown in FIG.

As described in further detail herein, the system may be configured to track ovulation cycles and identify anovulation cycles. In some cases, the user's temperature pattern throughout the day and night may be an indicator that may characterize ovulation cycles. For example, daytime and nighttime skin temperatures may identify ovulation and anovulation cycles. Thus, timing diagram 400-a illustrates the relationship between the user's temperature data and time (e.g., over multiple days and/or months). In this regard, the vertical bars shown in timing diagram 400-a may be understood to refer to "body temperature value 405-a." The dark vertical bars shown in timing diagram 400-a may be understood to refer to "ovulation 410." Ovulation 410 may indicate an ovulation cycle. The dashed curve shown in timing diagram 400-a may be understood to refer to "function 420." The user's body temperature value 405 may be relative to a baseline body temperature.

In some cases, the system (eg, ring 104, user device 106, server 110) may receive physiological data associated with the user from the wearable device. Physiological data may include at least body temperature data. The system may determine a time series of multiple body temperature values 405-a obtained over multiple days based on the received body temperature data. Referring to timing diagram 400-a, multiple days may be an example of three months. The system may process the original time series body temperature data (eg, body temperature values 405-a) to identify one or more indicators of anovulatory cycles 415. In some cases, the time series may include one or more ovulatory cycles of the user, including ovulation 410, one or more anovulatory cycles 415 of the user, or both.

Body temperature values 405-a may be continuously collected by the wearable device. Physiological measurements may be taken continuously throughout the day and/or night. For example, in some implementations, the ring continuously monitors physiological data (e.g., body temperature data, sleep data, heart rate, HRV data, respiratory rate data, MET data) according to one or more measurement cycles throughout each day/sleep day. etc.). In other words, the ring may continuously acquire physiological data from the user regardless of the "trigger conditions" for making such measurements. In some cases, continuous finger temperature measurements may capture body temperature fluctuations (eg, small or large fluctuations) that may not be evident in core body temperature. For example, continuous temperature measurement with a finger may not be provided by other temperature measurements elsewhere on the body or if the user was taking the temperature manually once a day. Minute-to-minute or hourly body temperature fluctuations may be captured, providing additional insight.

In some implementations, the system determines the approximate date of ovulation 410 by observing the user's relative body temperature over a number of days and marking increases in body temperature that may indicate ovulation 410 (e.g., an ovulatory cycle). Can be detected. The ovulation event (eg, ovulation 410) may be used to estimate the user's fertility window (eg, about 5 days before ovulation 410). Distinguishing the phases of the menstrual cycle using individualized continuous physiology may provide accurate fertilization window estimates. In such cases, the system may identify the fertilization window based on identifying one or more ovulatory cycles.

The system may identify one or more ovulatory cycles in the time series of body temperature values 405-a based on the one or more positive slopes of the time series of body temperature values 405-a. For example, after determining the time series, the system may identify one or more positive slopes of the time series of multiple body temperature values 405-a. The system may identify one or more ovulatory cycles in the time series of body temperature values 405-a in response to identifying one or more positive slopes in the time series.

The system may fit the received body temperature data (eg, body temperature value 405-a) to a trigonometric or polynomial function 420. For example, function 420 may include one or more positive slopes of the time series. Each ovulation 410 is associated with a positive slope in the time series of body temperature values 405-a. For example, ovulation 410 may occur at the beginning of a positive slope. In such cases, a positive slope may indicate that ovulation 410 has occurred (eg, that the user is experiencing an ovulatory cycle).

In some cases, the system may determine or estimate the maximum and/or minimum temperature of the user after determining the time series of the user's body temperature values 405-a collected via the ring. The system may identify one or more positive slopes of the time series of multiple body temperature values 405-a based on determining the maximum and/or minimum values. In some cases, a positive slope may be determined by calculating the difference between the maximum and minimum values. In other examples, identifying one or more positive slopes of a time series of temperature values 405-a is responsive to calculating a derivative of the original time series temperature data (e.g., body temperature values 405-a). It is possible.

In some cases, the system may identify one or a positive slope of spectral power for an ultradian frequency range based on receiving physiological data. The system may identify one or more ovulatory cycles (eg, ovulation 410) in the time series of body temperature values 405 in response to identifying one or more positive slopes of spectral power. Each ovulation 410 is associated with a positive slope of spectral power. For example, ovulation 410 may occur at the beginning of a positive slope. In such cases, a positive slope may indicate that ovulation has occurred.

In some implementations, a user may indicate in an application on a user device that the user is interested in becoming pregnant. This user instruction may trigger an ovulation tracking mode, as further described with reference to FIG. In ovulation tracking mode, the system may predict the likelihood of becoming pregnant within a given time range given a set of characteristics including the user's previous cycle history. In some examples, this time range may include six months, which may be considered a threshold for infertility problems in clinical practice. The set of features includes the length, regularity, spectral power, and consistency of ultradian rhythms in body temperature, heart or HRV, and other signals derived from daytime and/or sleep measurements. , the user's age and previous cycle history. In some cases, the set of features may include health behaviors inferred from body temperature, actigraphy, and photoplethysmography data, in addition to pulse oximetry data, galvanic skin responses, and other signals. The system normalizes absolute values for body temperature, HRV, ultradian spectral power, etc., but within the user so that the values reflect relative changes for a given user and take into account their previous history. You can use metrics that are absolute values. Normalization may be performed in a variety of ways, including, but not limited to, z-score, min-max, median deltas from a defined baseline period, or after removal of seasonal trends.

Diurnal body temperature may have ultradian rhythms that are quantifiable by fast Fourier transform. In some cases, ultradian rhythms in diurnal body temperature may be relevant to fertility prediction algorithms. Troughs in a user's diurnal body temperature rhythm plotted over a 5-day period may reflect pulses of neuroendocrine and reproductive hormones that may vary in spectral power and frequency as physiological markers or mechanisms that stimulate a progesterone surge that initiates ovulation. In some cases, other signals (e.g., heart rate, HRV, respiratory rate, etc.) may also exhibit detectable ultradian rhythms.

In some implementations, the system may detect ultradian body temperature rhythms during the day. The fingers are further away from the user's core (eg, the heart) than the wrist or forehead, and this affects the physical transfer of heat from the core to the furthest points of the periphery. In such cases, the physical distance between the periphery and the core ensures that the difference between peripheral and core body temperature is greatest when detected at one of the extremities. can be shown. This difference is particularly relevant to the dynamics of daytime ultradian rhythms, when the peripheral skin vasodilates as part of an allostatic control system designed to maintain core body temperature within a biologically healthy range. Sometimes it shows. Additionally, fingers have different types of skin receptors (eg, glabrous receptors), a much higher density of nervous system input, and more small blood vessels with a higher surface area to blood volume ratio. All of these characteristics make ultradian rhythms measured by finger rings more effective and efficient at detecting and quantifying daytime ultradian body temperature rhythms than sensors placed elsewhere on the body. It can be shown that it can be obtained.

In some cases, the system obtains raw daytime temperature data (eg, minute-by-minute temperature data). In some implementations, the system may collect nighttime body temperature data and include it in the ultradian analysis. In some implementations, the system may also collect interbeat interval (IBI) data and calculate HRV and resting heart rate on a rolling basis throughout the night. In such cases, the system may apply a fast Fourier transform algorithm to generate ultradian rhythms from these features.

The system may measure spectral power using a fast Fourier transform for a sliding five-day window of daytime body temperature. The system may generate frequency-based features by measuring power within certain ultradian cycle ranges (e.g., 3.5-5 hours, 2.3-3 hours, and 1.25-2.3 hours). These ranges may be approximate, and the exact times may vary depending on the implementation. The system may post-process the features. For example, the system may normalize the features using the total spectral power over the 1-5 hour range and/or using the user's previous median basal values. The baseline may be measured as the user's median over a period of time (e.g., 14 or 28 days) or at the same time of the month in a previous cycle. In some implementations, the system may identify an increase in spectral power or an acceleration of ultradian frequency during the days to weeks preceding a potential window of ovulation. For example, the system may generate the slope of the increase or decrease in power or peak frequency over a window of several days. The slope may be considered as an additional machine learning feature input into the algorithm. In some implementations, the algorithms may be non-linear and may utilize time series methods such as ROCKET (random convolutional kernel transform), RISE (random interval spectral ensemble), inception time, or extracted spectral power features that are fed into machine learning classifier algorithms such as LightGBM (light gradient boosting machine).

The system may use both raw and/or user-normalized peak and spectral power features as input to a machine learning algorithm to predict the onset of ovulation (eg, ovulation 410). In some implementations, time series deep learning that outputs a window of possible days or days of ovulation 410 detects changes in the feature set over time, such as a decrease in peak frequency or an increase in spectral power. May include abilities. In some cases, this change may reflect changes in hypothalamic control over reproductive hormones, allowing the body to integrate environmental/seasonal inputs with endogenous rhythms and allowing users to adapt to their environment. enable you to help.

In some cases, a fast Fourier transform may be applied to the diurnal temperature data over a five-day window. Peaks in the spectral power of the ultradian rhythm for this user's five-day window may be present in the 3.5-5 hour, 2.3-3 hour periodicity ranges, with smaller peaks in the 1.25-2.3 hour range. Features for a machine learning algorithm predicting the onset of ovulation (e.g., ovulation 410) may be formed by extracting the peak frequencies in these various periodicity ranges over a five-day sliding window and extracting the total spectral power within these frequency ranges. In some implementations, the periodicity bands may be tailored to the characteristics of a particular user, rather than being determined by a global threshold. In some implementations, the spectral power features may be normalized to the total spectral power over the 1-5 hour range. In some implementations, the spectral power features may be input as absolute values for a given day. In other implementations, the same features may be expressed as the difference to the user's previous baseline median over a period of time (e.g., 14 or 28 days), or as the difference to the user's previous baseline median at the same time of the month in the previous cycle. In some implementations, the spectral power and peak frequency features may be fed into a machine learning classifier (e.g., LightGBM). In some cases, the algorithm used may utilize time series classifiers such as ROCKET, RISE, shapelets, or neural network time series classifiers such as Inception Time.

As described in further detail herein, the system may be configured to track ovulation 410. In some cases, a user's body temperature pattern throughout the day can be an indicator that can characterize ovulation 410. For example, daytime skin temperature may identify one or more ovulatory cycles, one or more anovulatory cycles 415, or both. As such, timing diagram 400-a illustrates the relationship between user body temperature data and time (eg, over multiple days). Timing diagram 400-a may depict a user having three normal cycles (eg, including ovulation 410) for each cycle. In such a case, timing diagram 400-a may show a user having three ovulation cycles.

Timing diagram 400-b shows the relationship between user body temperature data and time (eg, over multiple days and/or months). In this regard, the vertical bars shown in timing diagram 400-b may be understood to refer to "body temperature values 405-b." The dark vertical bars shown in timing diagram 400-b may be understood to refer to "ovulation 410." The dashed curve shown in timing diagram 400-b may be understood to refer to "function 420-b." The dashed circles shown in timing diagram 400-b may be understood to refer to "anovulatory cycles 415." The user's body temperature value 405-b may be relative to a baseline body temperature.

Timing diagram 400-b may show a user having two normal cycles (eg, including ovulation 410) with each cycle followed by an anovulatory cycle 415. In some implementations, the system may detect lack of ovulation based on deviations from the expected end of the follicular phase and beginning of the luteal phase. In addition to body temperature, the end of the follicular phase is accompanied by other measured physiological parameters such as a decrease in heart rate variability, an increase in respiratory rate, and an increase in body temperature that ultimately indicates ovulation and the beginning of the luteal phase. can be detected using The system may use a combination of physiological data over time (eg, days or weeks) together to classify anovulatory cycles 415.

In some implementations, the system detects an anovulatory cycle by observing the user's relative body temperature over a number of days and marking deviations from body temperature values 405-b during ovulation 410, which may indicate an anovulatory cycle 415. 415 can be detected. Anovulatory events (eg, anovulatory cycles 415) may be used to estimate a user's fertilization window. Distinguishing the phases of the menstrual cycle using individualized continuous physiology may provide accurate fertilization window estimation. In such cases, the system may identify a fertilization window based on identifying one or more anovulatory cycles 415.

The system may identify an indicator of an anovulatory cycle 415 in the time series of body temperature values 405-b based on the deviation of body temperature value 405-b from an identified ovulatory cycle (eg, ovulation 410). For example, after determining the time series, the system may identify one or more deviations in the time series of body temperature values 405-b. The system may identify one or more anovulatory cycles 415 in the time series of body temperature values 405-b in response to identifying one or more deviations in the time series. For example, the temperature value 405-b during an anovulatory cycle 415 may deviate from the function 420-b or from the predicted temperature value 405-b of the function 420-b. In such a case, based on the body temperature value 405-b during the ovulation cycle, the system may predict that the body temperature will increase after a determined time frame from the previous ovulation 410. Anovulatory cycles 415 may be identified based on the lack of increase in body temperature after a determined time frame has elapsed.

In such a case, the system may calculate the deviation in the time series of body temperature values 405-b. The deviation can include a decrease in body temperature value 405-b from an ovulatory cycle body temperature value 405-b (eg, body temperature value 405-a with reference to timing diagram 400-a). In such a case, the system may determine that body temperature value 405-b is lower than the predicted body temperature value from function 420-b. The deviation may further include a lack of body temperature value 405-b following an ovulatory cycle (eg, ovulation 410). In such a case, the system may determine that temperature value 405-b is not present where the predicted temperature value from function 420-b is. The system may identify one or more indicators of anovulatory cycles 415 based on calculating the deviation. In such a case, each anovulatory cycle 415 may include an absence of a temperature value 405-b in the time series of body temperature values 405-b, a decrease in body temperature value 405-b in the time series of body temperature values 405-b, or both. Associated.

In some implementations, the system may compare the ovulation cycle's temperature value 405-b to the current value of the temperature value 405-b. The system may determine that the dissimilarity matrix score meets a threshold based on the comparison. The system may identify an indicator of an anovulatory cycle 415 based on determining that the dissimilarity matrix score meets a threshold. For example, the system may smooth the body temperature data (eg, using a 7-day smoothing window or other window) to generate function 420. In some implementations, missing body temperature values may be filled in (eg, using the forecaster Impute method of the sktime python package). In some cases, the system may train a classifier. For example, the system uses the sktime python package (or other techniques) on a daily basis to predict whether a cycle is likely to have an ovulatory event (e.g. ovulation 410) or whether the cycle was an anovulatory cycle 415. It may be used as a classifier to train a random convolutional kernel transform. In some implementations, the system may determine a matrix dissimilarity score to compare the user's current cycle to past cycles. In such a case, if the matrix dissimilarity score exceeds a non-probability statistical threshold, the cycle may be labeled as an anovulatory cycle 415.

The system may identify a fertilization window based on identifying one or more ovulatory cycles, identifying one or more indicators of anovulatory cycles 415, or both. In some implementations, the system may predict success of infertility treatment. For example, an application on a user device may operate in a fertility mode and/or an ovulation tracking mode in the context of a user seeking infertility treatment, such as in vitro fertilization (IVF) or artificial insemination (IUI). Clinical fertility centers can quantify a variety of fertility-related outcomes, including follicle counts, pregnancy success, number of treatment rounds, and treatment complications such as ovarian hyperstimulation syndrome.

Cycle phase data may be converted into a set of physiological machine learning features that can be incorporated into a fertility treatment success prediction algorithm that can predict the likelihood of becoming pregnant at a given number of fertility treatments. Examples of features include average cycle length (e.g., based on temperature estimation period start date), minimum and maximum cycle lengths, number of detected anovulatory cycles, cycle irregularity (e.g., standard deviation of cycle length distribution), basic demographics, body mass index, and age-adjusted versions of these characteristics. Physiologically-based cycle characteristics may be used in combination with additional information regarding sleep, activity, metabolic or cardiovascular parameters, and in combination with clinically collected blood test values. In some implementations, the provider identifier assigned to the infertility treatment location or treating physician, the featurization of any previous failed infertility treatment attempts, the characterization of the treatment protocol, and the biological/ Clinical readouts may be incorporated for additional predictive value.

FIG. 5 illustrates an example GUI 500 that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure. GUI 500 may implement aspects of or be implemented by system 100, system 200, system 300, timing diagram 400, or any combination thereof. For example, GUI 500 may be an example of GUI 275 of user device 106 (eg, user devices 106-a, 106-b, 106-c) corresponding to user 102.

In some examples, GUI 500 may show a series of application pages 505 that may be displayed to a user via GUI 500 (eg, GUI 275 shown in FIG. 2). The system's server may cause the GUI 500 of the user device (eg, mobile device) to display an inquiry whether the user wants to activate ovulation tracking mode and track his or her ovulation (eg, via application page 505). In such cases, the system generates a personalized ovulation tracking experience on the GUI 500 of the user device to identify possible windows of ovulation (e.g. in a calendar view) and to identify possible windows in which egg release did not occur. Detect sexual cycles (e.g. a user experiences an anovulatory cycle) and provide personalized information, e.g. how a user's unique cycle characteristics may deviate from other users. , along with the meaning associated with the deviation, or a combination thereof, etc.

Continuing with the example above, prior to identifying ovulatory and anovulatory cycles, upon opening the wearable application, the user may be presented with application page 505. Application page 505 may display a request to activate ovulation tracking mode and enable the system to track the user's ovulation cycle. In such a case, application page 505 may display an invitation card in which the user is invited to register with the ovulation tracking application. Application page 505 may prompt the user to confirm whether to track the user's ovulation or to close the message if not to track the user's ovulation. The system may receive an indication that the user elects to opt in or opt out of the ovulation tracking mode.

Upon selecting “Yes” for ovulation tracking mode, the user may be presented with application page 505. Application page 505 may prompt the user to confirm the primary reason for tracking ovulation (eg, fertility awareness, achieving pregnancy, contraception, etc.). In such cases, the ovulation tracking mode may be used to help track the points in the cycle where pregnancy is most likely to occur, either for the purpose of attempting to achieve pregnancy or for the purpose of avoiding pregnancy. Application page 505 may prompt the user to confirm the intent to track ovulation. For example, the system may receive confirmation of the intended use of the ovulation tracking mode via the user device.

In some cases, upon confirming intent, the user may be presented with an application page 505. Application page 505 may prompt the user to ascertain the average menstrual cycle length (eg, the period of time between the first day of the first menstrual cycle and the first day of the second menstrual cycle). In some cases, application page 505 may display a prompt to the user to indicate whether the user is experiencing irregular cycles for which an average cycle length may not be determined. For example, the system may receive confirmation of the average period length via the user device.

In some examples, upon entering the average cycle length or irregular cycles, the user may be presented with application page 505. Application page 505 may prompt the user to confirm the last cycle start date (eg, the first day of the most recent menstrual cycle). Application page 505 may display a prompt to the user to indicate whether the user may not be able to identify the last cycle start date. For example, the system may receive confirmation of the last cycle start date via the user device.

In some cases, upon confirming the start date of the last cycle, the user may be presented with an application page 505. The application page may prompt the user to confirm whether the user is using hormonal contraceptives. For example, the system may receive confirmation via the user device whether hormonal contraceptives are in use. Upon confirming that hormonal contraceptives are not in use, the user may be presented with GUI 500, which may be further illustrated and explained with reference to application page 505.

The system's server may generate a message indicating the identified one or more anovulatory cycle indicators for display on the GUI 500 on the user device. For example, a server of the system may cause a GUI 500 of a user device (eg, a mobile device) to display an indicator of one or more identified anovulatory cycles (eg, via application page 505). In such cases, the system outputs the identified one or more anovulatory cycles on the GUI 500 of the user device and indicates that no eggs or oocytes have been released that would cause the user to skip ovulation for that month. It may indicate that the user has had a missed menstrual cycle and therefore has no chance of becoming pregnant until the next ovulatory cycle.

Continuing with the above example, upon identifying one or more indicators of an anovulatory cycle, the user may be presented with application page 505 upon opening the wearable application. As shown in FIG. 5, the application page 505 may display an indication that one or more indicators of an anovulatory cycle have been identified via an alert 510, a graphical display 515, a message 520, or a combination thereof. In such a case, the application page 505 may include an alert 510, a graphical display 515, and a message 520 on the home page. If the user's anovulatory cycles can be identified, the server may send a message 520 to the user, as described herein, where the message 520 includes one or more of the user's identified anovulatory cycles. Associated with anovulatory cycles. In some cases, the server may send message 520 to a clinician, a fertility specialist, a caregiver, the user's partner, or a combination thereof. In such a case, the system may present application page 505 on a user device associated with a clinician, fertility specialist, caregiver, partner of the user, or a combination thereof.

For example, the user device may receive a message 520, which may indicate a time interval during which an identified ovulation cycle occurred, a period between a future ovulation cycle and a previous ovulation cycle, a time interval during which an ovulation cycle is predicted to occur, a request to enter symptoms associated with the identified ovulation cycle, educational content associated with the identified ovulation cycle, etc. In some implementations, the message 520 may indicate a time interval during which an identified anovulation cycle occurred, a period between a future anovulation cycle and a previous ovulation cycle, a time interval during which ovulation is predicted to occur, a request to enter symptoms associated with the identified anovulation cycle, educational content associated with the identified anovulation cycle, etc. In some examples, the message 520 may indicate that an anovulation cycle is likely to occur during the user's next menstrual cycle or that anovulation occurred during the user's previous menstrual cycle.

Messages 520 may provide scores, insights, recommendations, and other features that may be tailored to fertility management goals. In ovulation tracking mode, users may receive visualizations and messages to help identify anovulatory cycles, which can be useful for fertility tracking. Application page 505 may provide relevant information associated with pregnancy planning based on detection of an anovulatory cycle. Messages 520 may be configurable/customizable such that a user may receive different messages 520 based on the identified anovulatory cycle, as previously described herein.

As shown in FIG. 5, application page 505 may display an indicator of one or more identified anovulatory cycles via alert 510. Additionally, in some implementations, application page 505 may display the user's one or more scores (eg, sleep score, readiness score, etc.) for each day. Additionally, in some cases, the anovulatory cycle may be used to update (eg, modify) one or more scores (eg, sleep score, readiness score, etc.) associated with the user. That is, data associated with the identified one or more anovulatory cycles may be used to update the user's score for the next calendar day after the anovulatory cycle is identified. In such cases, the system may notify the user of the score update via alert 510.

In some cases, the readiness score may be updated based on one or more identified anovulatory cycles. For example, an increase in body temperature prior to an identified anovulatory cycle may cause the system to alert the user via alert 510 regarding the user's bodily signals (eg, increase in body temperature). In such cases, the readiness score may indicate to the user a "need for attention" (eg, that an anovulatory cycle may be predicted) based on the phase of the menstrual cycle. If the readiness score changes for a user, the system may implement a cycle recovery mode for users who may have severe cycle symptoms and benefit from several days of adjusted activity and readiness guidance. I can receive it.

In other examples, readiness scores may be updated based on sleep scores and body temperature increases. However, the system may determine that the user is experiencing an anovulatory cycle and may adjust (e.g., increase) the readiness score and/or sleep score to offset the effects of the anovulatory cycle. . For users whose body signals (e.g., body temperature, heart rate, HRV, etc.) may be responsive to ovulation, the system displays low activity goals before and after ovulatory and/or anovulatory cycles. Sometimes. In such cases, accurate identification of ovulatory and anovulatory cycles can improve the accuracy and efficiency of readiness and activity scores.

In some cases, the message 520 displayed to the user via the user device's GUI 500 may indicate how the identified anovulatory cycle contributes to the overall score (e.g., overall readiness score) and/or individual contributing factors. You can show how it affected you. For example, the message might be, ``Your body seems to be under strain right now, but if you feel good, doing some light or moderate-intensity exercise may help your body fight the symptoms.'' your body is under strain right now, but if you're feeling ok, doing a light or medium intensity exercise can help your body battle the symptoms)" From your recovery metrics it looks like your body is still doing ok, so some light activity can help relieve the symptoms. Hope you 'll feel better tomorrow!' If an anovulatory cycle is identified, message 520 may provide suggestions to the user to improve the user's overall health. For example, a message might be, “If you feel really low on energy, why not switch to rest mode for today” or “If you feel really low on energy, why not switch to rest mode for today” You may say, "Since you have cramps and a headache, devote today for rest." In such cases, messages 520 displayed to the user may provide targeted insights to help the user adjust their lifestyle during portions of their menstrual cycle.

In some cases, the system may prompt the user via message 520 to ask whether the user is pregnant, or to switch to an alternate mode (e.g., pregnancy mode) or deactivate ovulation tracking mode. We propose that In some cases, the ovulation tracking mode may include an assisted reproduction mode.

The application page 505 may show one or more parameters of the anovulatory cycle, including temperature, heart rate, HRV, respiratory rate, etc., experienced by the user during the anovulatory cycle, via a graphical display 515. The graphical display 515 may be an example of the timing diagram 400-b described with reference to FIG. 4. In such a case, the system may cause the user device GUI 500 to display a message 520, alert 510, or graphical display 515 associated with the identified ovulatory cycle.

In some cases, the user may record symptoms via user input 525. For example, the system may receive user input 525 (e.g., tags) to record symptoms associated with ovulation (e.g., flow, abdominal pain, headaches, pain, hypersexuality, vaginal discharge, cervical mucus/fluids, etc.). can be received. The system may recommend tags to the user based on user history, identified ovulatory cycles, and identified anovulatory cycles. In some cases, the system provides ovulation tracking to the GUI 500 of the user device based on correlations between previous user symptom tags and the timing of one or more ovulatory cycles and/or one or more anovulatory cycles. A tag may also be displayed. For example, the system may receive user input 525 (eg, tags) to track symptoms of abnormal discharge based on color, amount, concentration, odor, etc. In response to the user-selected tag, the system provides additional educational material as well as a message 520 indicating "You are experiencing a change in the color of your vaginal discharge, have you discussed it with your doctor?" Good too.

Application page 505 may also include messages 520 that include insights, recommendations, etc. associated with ovulatory and anovulatory cycles. The system's server may cause the user device's GUI 500 to display messages 520 associated with the identified ovulatory and anovulatory cycles. The user device may display recommendations and/or information associated with ovulatory and anovulatory cycles via message 520. As previously mentioned herein, accurately identified ovulatory and anovulatory cycles can be beneficial to the user's overall health.

In some implementations, the system may provide additional insight regarding the user's identified anovulatory cycles. For example, application page 505 may indicate one or more physiological parameters (eg, contributing factors) that led to the user's identified anovulatory cycle, such as decreased body temperature. In other words, the system may be configured to provide some information or other insight regarding the identified anovulatory cycle. Personalized insights may indicate aspects of the collected physiological data (eg, contributing factors within the physiological data) that were used to generate the identified anovulatory cycles.

In some implementations, the system trains classifiers (e.g., supervised learning for machine learning classifiers) to improve anovulatory cycle determination and/or prediction techniques. The ovulation cycle may be configured to receive user input regarding the ovulation cycle. For example, a user device may receive user inputs 525, and these user inputs 525 may then be input to a classifier to train the classifier.

In some cases, the system may be configured to identify an ovulatory cycle based on user input, such as a menstrual cycle phase and symptoms associated with that phase. For example, the system may receive user input 525 of the identified time period and symptoms associated with the time period. The system may identify a pattern in user input 525 that indicates a period that occurs 10 days before the identified ovulation cycle. In such a case, the system may adjust the identified ovulation cycle to be 10 days (eg, prior to) the received user input 525 for that period. In some cases, the system may identify an anovulatory cycle based on user input 525. For example, the system may identify ovulation in response to determining that an anovulatory cycle did not occur within 10 days of the received user input 525 for that period.

Upon identifying ovulatory and anovulatory cycles on application page 505, GUI 500 displays the current date the user is viewing application page 505, a date range that includes the dates when ovulation is predicted and/or identified, and anovulatory cycles. A calendar view may be displayed that may indicate a date range that includes dates for which cycles are predicted and/or identified. For example, a date range may enclose calendar days using a dashed line configuration, the current date may enclose calendar days, and dates where ovulatory and/or anovulatory cycles are predicted may be encircled. The calendar view may also include a message that includes the current calendar day and an indication of the user's cycle date (eg, day 28 of the cycle).

In some cases, the system may retrospectively label past ovulatory and/or anovulatory cycles. A calendar view may include tagged events. For example, tagged events may be shown as open/filled circles below the calendar date. Some tagged events (eg, filled circles) may indicate detected cyclic events, such as menstrual onset events, ovulation symptoms, etc.

FIG. 6 illustrates a block diagram 600 of a device 605 supporting wearable-based detection of anovulatory cycles from physiological data, according to aspects of the present disclosure. The device 605 may include an input module 610, an output module 615, and a wearable application 620. The device 605 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

The input module 610 is configured to receive information such as packets associated with various information channels (e.g., control channels, data channels, information channels related to disease detection technology), user data, control information, or any combination thereof. can provide means for Information may be passed to other components of device 605. Input module 610 may utilize a single antenna or a set of multiple antennas.

Output module 615 may provide a means for transmitting signals generated by other components of device 605. For example, the output module 615 may transmit information such as packets associated with various information channels (e.g., control channels, data channels, information channels related to disease detection technology), user data, control information, or any combination thereof. It's fine. In some examples, output module 615 may be co-located with input module 610 within the transceiver module. Output module 615 may utilize a single antenna or a set of multiple antennas.

For example, wearable application 620 may include a data acquisition component 625, a temperature data component 630, an ovulatory cycle component 635, an anovulatory cycle component 640, a user interface component 645, or any combination thereof. In some examples, wearable application 620 or its various components use or otherwise cooperate with input module 610, output module 615, or both to perform various operations (e.g., receive, monitor, etc.). , transmission). For example, wearable application 620 may receive information from input module 610, send information to output module 615, or be integrated in combination with input module 610, output module 615, or both to receive information and send information. or perform various other operations as described herein.

Data acquisition component 625 may be configured as a means for receiving, or otherwise support, physiological data associated with a user from a wearable device, the physiological data including at least body temperature data. including. The body temperature data component 630 may be configured as a means for determining a time series of multiple body temperature values obtained over multiple days based at least in part on the received body temperature data, or otherwise configured. , where the time series includes multiple ovulation cycles of the user. Ovulation cycle component 635 is configured as a means for identifying a plurality of ovulation cycles in a time series of plurality of temperature values based at least in part on one or more positive slopes of the time series of plurality of temperature values. This may be supported in other ways. The anovulatory cycle component 640 identifies an indicator of one or more anovulatory cycles in the time series of the plurality of temperature values based at least in part on the deviation of the plurality of temperature values from the identified plurality of ovulatory cycles. or may support this in other ways. User interface component 645 may be configured as a means for generating a message indicative of one or more anovulatory cycles for display on a graphical user interface on a user device, or otherwise configured. may support this.

FIG. 7 illustrates a block diagram 700 of a wearable application 720 that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure. Wearable application 720 may be an example of aspects of wearable application and/or wearable application 620, as described herein. Wearable application 720 or its various components may be an example of a means for performing various aspects of anovulatory cycle detection from wearable-based physiological data as described herein. For example, wearable application 720 may include a data acquisition component 725, a temperature data component 730, an ovulatory cycle component 735, an anovulatory cycle component 740, a user interface component 745, or any combination thereof. Each of these components may communicate with each other (eg, via one or more buses), directly or indirectly.

Data acquisition component 725 may be configured as a means for receiving, or otherwise support, physiological data associated with a user from a wearable device, where the physiological data includes at least body temperature data. including. The body temperature data component 730 may be configured as a means for determining a time series of multiple body temperature values obtained over multiple days based at least in part on the received body temperature data, or otherwise configured. , where the time series includes multiple ovulation cycles of the user. Ovulation cycle component 735 is configured as a means for identifying a plurality of ovulation cycles in a time series of plurality of temperature values based at least in part on one or more positive slopes of the time series of plurality of temperature values. This may be supported in other ways. The anovulatory cycle component 740 identifies an indicator of one or more anovulatory cycles in the time series of the plurality of temperature values based at least in part on the deviation of the plurality of temperature values from the identified plurality of ovulatory cycles. or may support this in other ways. User interface component 745 may be configured as a means for generating a message indicative of one or more anovulatory cycles for display on a graphical user interface on a user device, or otherwise This may be supported.

In some examples, the anovulatory cycle component 740 may be configured as a means for calculating deviations in a time series of multiple body temperature values, or may support this in other ways; , the deviation includes a decrease in temperature values from the temperature values of the identified ovulatory cycles, an absence of the temperature values following the identified ovulatory cycles, or both; Identifying an indicator of an anovulatory cycle is based at least in part on calculating a deviation.

In some examples, the temperature data component 730 may be configured as a means for comparing the temperature values of the identified ovulatory cycles to the current value of the temperature values, or otherwise This may be supported. In some examples, the anovulatory cycle component 740 may be configured as a means for determining, based at least in part on the comparison, that the dissimilarity matrix score meets a threshold, or otherwise may support, where identifying one or more indicators of anovulatory cycles is based at least in part on determining that the dissimilarity matrix score satisfies a threshold.

In some examples, data acquisition component 725 as a means for identifying one or more positive slopes of spectral power for an ultradian frequency range based at least in part on receiving physiological data. The method may be configured or otherwise supported, wherein the identified multiple ovulatory cycles are associated with a positive slope of spectral power.

In some examples, the ovulation cycle component 735 is configured as a means for identifying one or more positive slopes of the time series of the plurality of body temperature values based at least in part on determining the time series. or otherwise supported, wherein identifying multiple ovulatory cycles is based at least in part on identifying one or more positive slopes.

In some examples, body temperature data component 730 may be configured as a means for determining each body temperature value of a plurality of body temperature values based at least in part on receiving body temperature data, or otherwise. This may be supported in a manner where the temperature data includes continuous daytime temperature data.

In some examples, the physiological data further includes heart rate data, and the data acquisition component 725 determines that the received heart rate data exceeds the user's threshold heart rate during at least a portion of the plurality of days. may be configured as a means for determining or otherwise support this, wherein identifying one or more indicators of an anovulatory cycle in a time series comprises a received heart rate. The data is based at least in part on determining that the data exceeds a threshold heart rate of the user.

In some examples, the physiological data further includes heart rate variability data, and the data acquisition component 725 determines whether the received heart rate variability data exceeds the user's threshold heart rate variability during at least a portion of the plurality of days. may be configured as a means for determining or otherwise support this, wherein identifying an indicator of an anovulatory cycle in the time series is based on the received heart rate variability. The data is based at least in part on determining that the data is below a threshold heart rate variability of the user.

In some examples, the physiological data further includes continuous nocturnal respiration rate data, and the data acquisition component 725 determines that the received respiration rate data corresponds to the user's threshold respiration during at least a portion of the plurality of days. may be configured as a means for determining, or otherwise support, the identification of one or more indicators of anovulatory cycles in a time series. , based at least in part on determining that the received respiration rate data exceeds a threshold respiration rate of the user.

In some examples, the ovulatory cycle component 735 determines the fertilization window based at least in part on identifying a plurality of ovulatory cycles and identifying an indicator of one or more anovulatory cycles in the time series. may be configured as a means for identifying or may support this in other ways.

In some examples, user interface component 745 may be configured as a means for transmitting or otherwise transmitting a message to a user device indicating an identified indicator of one or more anovulatory cycles. , where the user device is associated with a clinician.

In some examples, user interface component 745 as a means for displaying an ovulation tracking tag on a graphical user interface of a user device associated with the user based at least in part on identifying multiple ovulation cycles. This may be configured or supported in other ways.

In some examples, user interface component 745 may be configured as a means for causing a graphical user interface of a user device associated with the user to display a message associated with the identified plurality of ovulation cycles, or This may be supported in other ways.

In some examples, the message includes the time interval in which the identified ovulatory cycles occurred, the period between future ovulatory cycles and previous ovulatory cycles, the time interval in which the ovulatory cycles are expected to occur, the identification The method further includes a request to enter symptoms associated with the identified plurality of ovulatory cycles, educational content associated with the identified plurality of ovulatory cycles, or a combination thereof.

In some examples, data acquisition component 725 may be configured as a means for inputting physiological data into a machine learning classifier or otherwise support it, where: Identifying one or more indicators of anovulatory cycles is based at least in part on inputting physiological data to a machine learning classifier.

In some examples, the wearable device includes a wearable ring device.

In some examples, the wearable device collects physiological data from the user based on arterial blood flow.

FIG. 8 depicts a diagram of a system 800 that includes a device 805 that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure. Device 805 may be an example of or include a component of device 605 as described herein. Device 805 may include examples of user devices 106 as previously described herein. Device 805 is configured to send and receive communications from wearable device 104 and server 110, such as wearable application 820, communication module 810, antenna 815, user interface components 825, database (application data) 830, memory 835 and processor 840. may include components for two-way communication, including components of. These components may be in electronic communication or otherwise be connected (e.g., operatively, communicatively, functionally, electronically, etc.) via one or more buses (e.g., bus 845). may be electrically coupled.

Communication module 810 may manage input and output signals for device 805 via antenna 815. Communication module 810 may include an example of communication module 220-b of user device 106 shown and described in FIG. In this regard, communications module 810 may manage communications with ring 104 and server 110, as illustrated in FIG. Communication module 810 may also manage peripherals that are not integrated into device 805. In some cases, communication module 810 may represent a physical connection or port to an external peripheral. In some cases, the communication module 810 supports iOS, ANDROID, MS-DOS, MS-WINDOWS, OS/2, UNIX , LINUX or another known operating system may be utilized. In other cases, communication module 810 may represent or interact with a wearable device (eg, ring 104), modem, keyboard, mouse, touch screen, or similar device. In some cases, communication module 810 may be implemented as part of processor 840. In some examples, a user may interact with device 805 through communications module 810, user interface component 825, or through hardware components controlled by communications module 810.

In some cases, device 805 may include a single antenna 815. However, in some other cases, device 805 may have more than one antenna 815, which can transmit or receive multiple wireless transmissions simultaneously. Communication module 810 may communicate bi-directionally via one or more antennas 815, via wired or wireless links, as described herein. For example, communication module 810 may represent a wireless transceiver and may communicate bi-directionally with another wireless transceiver. Communication module 810 also includes a modem for modulating packets, providing modulated packets to one or more antennas 815 for transmission, and demodulating packets received from one or more antennas 815. good.

User interface component 825 may manage data storage and processing within database 830. In some cases, a user may interact with user interface component 825. In other cases, user interface component 825 may operate automatically without user interaction. Database 830 may be an example of a single database, a distributed database, multiple distributed databases, a data store, a data lake, or an emergency backup database.

Memory 835 may include RAM and ROM. Memory 835 may store computer-readable and computer-executable software containing instructions that, when executed, cause processor 840 to perform various functions described herein. In some cases, memory 835 may include a BIOS, which may control basic hardware or software operations such as interaction with peripheral components or devices, among other things.

The processor 840 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the processor 840 may be configured to operate a memory array using a memory controller. In other cases, the memory controller may be incorporated into the processor 840. The processor 840 may be configured to execute computer-readable instructions stored in the memory 835 to perform various functions (e.g., functions or tasks supporting the methods and systems for the sleep stage algorithm).

For example, wearable application 820 may be configured as a means for receiving physiological data associated with a user from a wearable device, or may otherwise support such means, where the physiological data includes at least body temperature. Contains data. Wearable application 820 may be configured as a means for determining a time series of multiple body temperature values obtained over multiple days based at least in part on received body temperature data, or otherwise A means may be supported, where the time series includes multiple ovulation cycles of the user. Wearable application 820 is configured as a means for identifying ovulation cycles in a time series of body temperature values based at least in part on one or more positive slopes of the time series of body temperature values. Such means may be supported or otherwise supported. The wearable application 820 is configured to identify an indicator of one or more anovulatory cycles in the time series of the plurality of temperature values based at least in part on the deviation of the plurality of temperature values from the identified plurality of ovulatory cycles. may be configured as means or may support such means in other ways. Wearable application 820 may be configured as a means for generating messages indicative of one or more anovulatory cycles for display on a graphical user interface of a user device, or otherwise configured to do so. may support other means.

By including or configuring wearable application 820 in accordance with embodiments described herein, device 805 can provide an improved user experience associated with improved communication reliability, reduced latency, and reduced processing. , may support techniques for reduced power consumption, more efficient utilization of communication resources, improved coordination between devices, longer battery life, and improved utilization of processing power.

Wearable application 820 may include an application (e.g., an “app”), program, software, or other component that facilitates communication with ring 104, server 110, other user devices 106, etc. configured. For example, wearable application 820 is configured to receive data (e.g., physiological data) from ring 104, perform processing operations on the received data, send and receive data to and from server 110, and cause data to be presented to user 102. The application may include applications executable on the user device 106 that are executed on the user device 106.

FIG. 9 is a flowchart illustrating a method 900 that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure. The operations of method 900 may be implemented by a user device or components thereof as described herein. For example, the operations of method 900 may be performed by a user device such as that described with reference to FIGS. 1-8. In some examples, a user device may execute a set of instructions to control functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may use specialized hardware to perform aspects of the described functionality.

At 905, the method may include receiving physiological data associated with the user from the wearable device, the physiological data including at least body temperature data. The operations at 905 may be performed according to examples disclosed herein. In some examples, aspects of the operation of 905 may be performed by data acquisition component 725, as described with reference to FIG.

At 910, the method may include determining a time series of multiple body temperature values obtained over multiple days based at least in part on the received body temperature data, where the time series is based on the user's body temperature data. Contains multiple ovulation cycles. The operations at 910 may be performed according to examples disclosed herein. In some examples, aspects of the operation of 910 may be performed by temperature data component 730, as described with reference to FIG.

At 915, the method may include identifying the plurality of ovulatory cycles in the time series of the plurality of temperature values based at least in part on the one or more positive slopes of the time series of the plurality of temperature values. . The operations at 915 may be performed according to examples disclosed herein. In some examples, aspects of operation of 915 may be performed by ovulation cycle component 735, as described with reference to FIG.

At 920, the method identifies an indicator of one or more anovulatory cycles in the time series of the plurality of temperature values based at least in part on the deviation of the plurality of temperature values from the identified plurality of ovulatory cycles. May contain steps. The operations at 920 may be performed according to examples disclosed herein. In some examples, aspects of operation of 920 may be performed by anovulatory cycle component 740, as described with reference to FIG.

At 925, the method may include generating a message indicative of one or more anovulatory cycles for display on a graphical user interface of the user device. The operations at 925 may be performed according to examples disclosed herein. In some examples, aspects of the operation of 925 may be performed by user interface component 745, as described with reference to FIG.

FIG. 10 is a flowchart illustrating a method 1000 that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure. The operations of method 1000 may be implemented by a user device or components thereof as described herein. For example, the operations of method 1000 may be performed by a user device such as that described with reference to FIGS. 1-8. In some examples, a user device may execute a set of instructions to control functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may use specialized hardware to perform aspects of the described functionality.

At 1005, the method may include receiving physiological data associated with the user from the wearable device, the physiological data including at least body temperature data. The operations of 1005 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1005 may be performed by a data acquisition component 725, as described with reference to FIG. 7.

At 1010, the method may include determining a time series of a plurality of body temperature values taken over a plurality of days based at least in part on the received body temperature data, where the time series includes a plurality of ovulation cycles of the user. The operations of 1010 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1010 may be performed by body temperature data component 730, as described with reference to FIG. 7.

At 1015, the method may include identifying a plurality of ovulatory cycles in the time series of the plurality of temperature values based at least in part on the one or more positive slopes of the time series of the plurality of temperature values. . The operations at 1015 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1015 may be performed by ovulation cycle component 735, as described with reference to FIG.

At 1020, the method may include calculating a deviation in the time series of the plurality of temperature values, where the deviation is the deviation of the plurality of temperature values from the plurality of temperature values of the identified plurality of ovulation cycles. Identifying an indicator of one or more anovulatory cycles, including a decrease, an absence of multiple temperature values following identified multiple ovulatory cycles, or both, is based at least in part on calculating the deviation. The operations of 1020 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1020 may be performed by anovulatory cycle component 740, as described with reference to FIG.

At 1025, the method identifies an indicator of one or more anovulatory cycles in the time series of the plurality of temperature values based at least in part on deviations in the plurality of temperature values from the identified plurality of ovulatory cycles. May contain steps. The operations at 1025 may be performed according to examples disclosed herein. In some examples, the operational aspects of 1025 may be performed by anovulatory cycle component 740, as described with reference to FIG.

At 1030, the method may include generating a message for display on a graphical user interface on a user device indicating one or more indicators of an anovulatory cycle. The operations of 1030 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1030 may be performed by a user interface component 745, as described with reference to FIG. 7.

FIG. 11 is a flowchart illustrating a method 1100 that supports detection of anovulatory cycles from wearable-based physiological data in accordance with aspects of the present disclosure. The operations of method 1100 may be implemented by a user device or components thereof as described herein. For example, the operations of method 1100 may be performed by a user device such as that described with reference to FIGS. 1-8. In some examples, a user device may execute a set of instructions to control functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may use specialized hardware to perform aspects of the described functionality.

At 1105, the method may include receiving physiological data associated with the user from the wearable device, the physiological data including at least body temperature data. The operations at 1105 may be performed according to examples disclosed herein. In some examples, aspects of the operation of 1105 may be performed by data acquisition component 725, as described with reference to FIG.

At 1110, the method may include determining a time series of multiple body temperature values obtained over multiple days based at least in part on the received body temperature data, where the time series is a time series of multiple body temperature values obtained over multiple days. Contains multiple ovulation cycles. The operations at 1110 may be performed according to examples disclosed herein. In some examples, aspects of the operation of 1110 may be performed by temperature data component 730, as described with reference to FIG.

At 1115, the method may include identifying one or more positive slopes of the time series of the plurality of body temperature values based at least in part on determining the time series, where the plurality of temperature value time series Identifying an ovulatory cycle is based at least in part on identifying one or more positive slopes. The operations at 1115 may be performed according to examples disclosed herein. In some examples, aspects of the operation of 1115 may be performed by ovulation cycle component 735, as described with reference to FIG.

At 1120, the method may include identifying the plurality of ovulatory cycles in the plurality of temperature value time series based at least in part on the one or more positive slopes of the plurality of temperature value time series. . The operations of 1120 may be performed according to examples disclosed herein. In some examples, the operational aspects of 1120 may be performed by ovulation cycle component 735, as described with reference to FIG.

At 1125, the method identifies an indicator of one or more anovulatory cycles in the time series of the plurality of temperature values based at least in part on deviations in the plurality of temperature values from the identified plurality of ovulatory cycles. May contain steps. The operations at 1125 may be performed according to examples disclosed herein. In some examples, the operational aspects of 1125 may be performed by anovulatory cycle component 740, as described with reference to FIG.

At 1130, the method may include generating a message for display on a graphical user interface on a user device indicating one or more indicators of an anovulatory cycle. The operations of 1130 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1130 may be performed by a user interface component 745, as described with reference to FIG. 7.

It should be noted that the methods described above describe possible implementations; acts and steps may be rearranged or otherwise modified, and other implementations are possible. Additionally, aspects from two or more of the methods described above may be combined.

Explain how. The method includes the steps of: receiving physiological data associated with a user from a wearable device, the physiological data including at least body temperature data; determining a time series of a plurality of body temperature values obtained over a period of time, the time series comprising a plurality of ovulation cycles of the user; and one or more positive slopes of the time series of the plurality of body temperature values. identifying a plurality of ovulatory cycles in the plurality of temperature value time series based at least in part on the plurality of body temperature values; identifying one or more indicators of an anovulatory cycle in a time series of body temperature values; and generating a message indicating the one or more indicators of an ovulatory cycle for display on a graphical user interface of a user device. and may include.

Describe the device. The apparatus may include a processor, a memory coupled to the processor, and instructions stored in the memory. The instructions are executable by the processor and cause the apparatus to receive physiological data associated with the user from the wearable device, the physiological data including at least body temperature data and based at least in part on the received body temperature data. to determine a time series of a plurality of body temperature values obtained over a plurality of days, the time series including a plurality of ovulation cycles of the user, at least in part with a positive slope of one or more of the time series of body temperature values. identifying multiple ovulatory cycles in the time series of multiple temperature values based at least in part on deviations in the multiple temperature values from the identified multiple ovulatory cycles; The one or more indicators of anovulatory cycles in the series may be identified and a message indicating the one or more indicators of anovulatory cycles may be generated for display on a graphical user interface of the user device.

Another device will be described. The apparatus includes means for receiving physiological data associated with a user from a wearable device, the physiological data comprising: a plurality of physiological data based at least in part on the received body temperature data; means for determining a time series of a plurality of body temperature values obtained over a day, the time series comprising a plurality of ovulation cycles of the user; means for identifying a plurality of ovulatory cycles in a time series of a plurality of body temperature values based at least in part on a slope of the plurality of body temperature values; means for identifying one or more indicators of an ovulatory cycle in a time series of a plurality of body temperature values based on a plurality of body temperature values; and means for generating a message indicating the message.

A non-transitory computer-readable medium for storing code is described. The code is instructions executable by the processor to receive physiological data associated with a user from the wearable device, the physiological data including at least body temperature data, and based at least in part on the received body temperature data; determining a time series of a plurality of body temperature values obtained over a plurality of days, the time series including a plurality of ovulatory cycles of the user; identifying a plurality of ovulatory cycles in a time series of plurality of temperature values based at least in part on the deviation in the plurality of temperature values from the identified plurality of ovulation cycles; The method may include instructions for identifying the one or more indicators of an ovulatory cycle and generating a message indicative of the one or more indicators of the anovulatory cycle for display on a graphical user interface of a user device.

Some examples of methods, apparatus, and non-transitory computer-readable media described herein may further include acts, features, means, or instructions for calculating deviations in a time series of multiple body temperature values. Often, the deviation includes a decrease in temperature values from the temperature values of the identified ovulatory cycles, an absence of temperature values following the identified ovulatory cycles, or both; Identifying the above indicators of anovulatory cycles may be based at least in part on calculating a deviation.

Some examples of methods, apparatus, and non-transitory computer-readable media described herein compare a plurality of body temperature values of a plurality of identified ovulatory cycles to a current value of a plurality of body temperature values; The method may further include an operation, characteristic, means or instructions for determining, based at least in part on the comparison, that the dissimilarity matrix score satisfies a threshold, identifying one or more indicators of an anovulatory cycle. may be based at least in part on determining that the dissimilarity matrix score satisfies a threshold.

Some examples of the methods, apparatus, and non-transitory computer-readable media described herein are based, at least in part, on receiving physiological data at one or more spectral powers for an ultradian frequency range. The method may further include an operation, feature, means, or instruction for identifying a positive slope of the spectral power, and the identified plurality of ovulatory cycles may be associated with a positive slope of the spectral power.

Some examples of the methods, devices, and non-transitory computer readable media described herein may further include operations, features, means, or instructions for identifying one or more positive slopes in the time series of the plurality of body temperature values based at least in part on determining the time series, and identifying the plurality of ovulation cycles may be based at least in part on identifying the one or more positive slopes.

Some examples of methods, apparatus, and non-transitory computer-readable media described herein are for determining each body temperature value of a plurality of body temperature values based at least in part on receiving body temperature data. The temperature data may include continuous daytime temperature data.

In some examples of the methods, devices and non-transitory computer-readable media described herein, the physiological data further includes heart rate data, and the methods, devices and non-transitory computer-readable media include a plurality of may further include an operation, feature, means or instructions for determining that the received heart rate data exceeds a threshold heart rate of the user during at least a portion of the day, the one or more anovulatory events in the time series; Identifying the periodicity indicator may be based at least in part on determining that the received heart rate data exceeds a threshold heart rate of the user.

In some examples of the methods, apparatus and non-transitory computer-readable medium described herein, the physiological data further includes heart rate variability data, and the methods, apparatus and non-transitory computer-readable medium include: The operation, feature, means or instructions for determining that the received heart rate variability data is below a threshold heart rate variability of the user during at least a portion of the plurality of days, the anovulatory cycle in time series. Identifying the indicator of may be based at least in part on determining that the received heart rate variability data is below a threshold heart rate variability of the user.

In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the physiological data further includes continuous nocturnal respiratory rate data; The enabled medium may further include operations, features, means or instructions for determining that the received respiration rate data exceeds the user's threshold respiration rate for at least a portion of a plurality of days, one in a time series. Identifying the above indicators of an anovulatory cycle may be based, at least in part, on determining that the received respiration rate data exceeds a threshold respiration rate of the user.

Some examples of methods, apparatus, and non-transitory computer-readable media described herein identify multiple ovulatory cycles and identify an indicator of one or more anovulatory cycles in a time series. The method may further include acts, features, means or instructions for identifying a fertilization window based at least in part on the above.

Some examples of the methods, apparatus, and non-transitory computer-readable media described herein operate for transmitting a message to a user device indicating an identified indicator of one or more anovulatory cycles. , the user device may be associated with the clinician.

Some examples of the methods, apparatus, and non-transitory computer-readable media described herein are based at least in part on identifying multiple ovulation cycles. The method may further include acts, features, means or instructions for causing the interface to display an ovulation tracking tag.

Some examples of the methods, apparatus, and non-transitory computer-readable medium described herein display messages associated with identified multiple ovulation cycles on a graphical user interface of a user device associated with a user. It may further include acts, features, means, or instructions for causing.

In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the message includes the time intervals during which the identified plurality of ovulatory cycles occurred, future ovulatory cycles and previous ovulatory cycles. the time interval during which an ovulatory cycle can be expected to occur, a request to enter symptoms associated with the identified multiple ovulatory cycles, educational content associated with the identified multiple ovulatory cycles, or It may further include combinations.

Some examples of methods, apparatus, and non-transitory computer-readable media described herein may further include acts, features, means, or instructions for inputting physiological data into a machine learning classifier. , identifying one or more indicators of anovulatory cycles may be based at least in part on inputting physiological data to a machine learning classifier.

In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the wearable device includes a wearable ring device.

In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the wearable device collects physiological data from a user based on arterial blood flow.

The following provides an overview of aspects of the present disclosure:

Aspect 1: Receiving physiological data associated with a user from a wearable device, the physiological data including at least body temperature data; determining a time series of a plurality of body temperature values obtained over a period of time, said time series comprising a plurality of ovulation cycles of said user; and one or more of said time series of said plurality of body temperature values. identifying the plurality of ovulatory cycles in the time series of the plurality of body temperature values based at least in part on a positive slope of the plurality of body temperature values; a deviation in the plurality of body temperature values from the identified plurality of ovulatory cycles; identifying one or more indicators of anovulatory cycles in the time series of the plurality of body temperature values based at least in part on; A method comprising: generating a message indicative of an anovulatory cycle.

Aspect 2: further comprising calculating a deviation in the time series of the plurality of body temperature values, the deviation being a decrease in the plurality of body temperature values from the plurality of body temperature values of the identified plurality of ovulation cycles; The step of identifying the indicator of the one or more anovulatory cycles, including the absence of the plurality of temperature values following the identified plurality of ovulatory cycles, or both, includes at least part of the step of identifying the indicator of the one or more anovulatory cycles. A method according to aspect 1, based on.

Aspect 3: The method of any of aspects 1 or 2, further comprising: comparing the plurality of body temperature values of the identified plurality of ovulation cycles to a current value of the plurality of body temperature values; and determining, based at least in part on the comparison, that a dissimilarity matrix score meets a threshold, wherein identifying the indicator of one or more anovulation cycles is based at least in part on determining that the dissimilarity matrix score meets the threshold.

Aspect 4: Based at least in part on receiving the physiological data, the identified plurality of ovulatory cycles further comprises identifying one or more positive slopes of spectral power for an ultradian frequency range; , a positive slope of the spectral power.

Aspect 5: Based at least in part on determining the time series, further comprising identifying one or more positive slopes of the time series of the plurality of body temperature values, identifying the plurality of ovulation cycles. 5. The method of any of aspects 1-4, wherein the step of determining is based at least in part on identifying the one or more positive slopes.

Aspect 6: further comprising determining each body temperature value of the plurality of body temperature values based at least in part on receiving the body temperature data, the body temperature data comprising continuous daytime body temperature data. The method according to any one of aspects 1 to 5.

Aspect 7: The physiological data further includes heart rate data, and the method includes determining that the received heart rate data exceeds a threshold heart rate of the user during at least a portion of the plurality of days. further comprising: identifying the indicator of the one or more anovulatory cycles in the time series is based at least in part on determining that the received heart rate data exceeds a threshold heart rate of the user. , the method according to any one of aspects 1 to 6.

Aspect 8: The physiological data further comprises heart rate variability data, and the method determines that the received heart rate variability data is below a threshold heart rate variability of the user during at least a portion of the plurality of days. further comprising the step of identifying an indicator of an anovulatory cycle in the time series is based at least in part on determining that the received heart rate variability data is below a threshold heart rate variability of the user. The method according to any one of aspects 1 to 7.

Aspect 9: The physiological data further comprises continuous nocturnal respiration rate data, and the method further comprises: during at least a portion of the plurality of days, the received respiration rate data exceeds a threshold respiration rate of the user. further comprising the step of determining the indicator of the one or more anovulatory cycles in the time series, wherein the step of identifying the indicator of the one or more anovulatory cycles in the time series includes determining that the received respiration rate data exceeds a threshold respiration rate of the user. 9. A method according to any of aspects 1 to 8, based at least in part.

Aspect 10: further comprising identifying a fertilization window based at least in part on identifying the plurality of ovulatory cycles and identifying the one or more indicators of anovulatory cycles in the time series. , the method according to any one of aspects 1 to 9.

Aspect 11: The method of any of aspects 1 to 10, further comprising transmitting the message indicating the identified indicator of the one or more anovulatory cycles to the user device, the user device being associated with a clinician. Method described in Crab.

Aspect 12: Any of Aspects 1-11, further comprising causing an ovulation tracking tag to be displayed on a graphical user interface of a user device associated with the user based at least in part on identifying the plurality of ovulation cycles. The method described in.

Aspect 13: The method of any of aspects 1 to 12, further comprising causing a graphical user interface of a user device associated with the user to display a message associated with the identified plurality of ovulation cycles.

Aspect 14: The message includes a time interval in which the identified plurality of ovulation cycles have occurred, a time period between a future ovulation cycle and a previous ovulation cycle, a time interval in which an ovulation cycle is expected to occur, and the identification. 14. The method of aspect 13, further comprising a request to enter symptoms associated with the identified plurality of ovulatory cycles, educational content associated with the identified plurality of ovulatory cycles, or a combination thereof.

Aspect 15: further comprising inputting the physiological data to a machine learning classifier, wherein identifying the one or more indicators of anovulatory cycles comprises inputting the physiological data to the machine learning classifier. 15. A method according to any of aspects 1 to 14, based at least in part on.

Aspect 16: The method according to any one of aspects 1 to 15, wherein the wearable device includes a wearable ring device.

Aspect 17: The method of any of aspects 1 to 16, wherein the wearable device collects the physiological data from the user based on arterial blood flow.

Aspect 18: An apparatus comprising: a processor; a memory coupled to the processor; a method stored in the memory and executable by the processor; A device comprising instructions for execution;

Aspect 19: An apparatus comprising at least one means for carrying out the method according to any of aspects 1 to 17.

Aspect 20: A non-transitory computer-readable medium storing code, the code comprising instructions executable by a processor to perform the method of any of aspects 1-17. Computer-readable medium.

The description herein, taken in conjunction with the accompanying drawings, describes example configurations and is not representative of all examples that may be implemented or within the scope of the claims. As used herein, the term "exemplary" means "serving as an example, illustration, or illustration," and does not imply "preferred" or "advantageous over other instances." The detailed description includes specific details for the purpose of providing an understanding of the described technology. However, these techniques may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the described examples.

In the accompanying drawings, similar components or features may have the same reference label. Additionally, various components of the same type may be distinguished by a second label following the reference label by a hyphen and distinguishing similar components. If only a first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label, regardless of the second reference label.

The information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

Various example blocks and modules described in connection with the disclosure herein may include general purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or It may be implemented or implemented in any combination thereof designed to perform the functions described in the specification. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller or state machine. A processor may also be implemented as a combination of computing devices, such as a DSP and microprocessor combination, multiple microprocessors, one or more microprocessors associated with a DSP core, or any other such configuration. .

The functionality described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope of this disclosure and the following claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hard wiring, or any combination thereof. Features implementing functionality may also be physically located at various locations, including distributed such that portions of the functionality are implemented at different physical locations. Also, as used herein, including in the claims, a list of items (e.g., an item preceded by the phrase "at least one of" or "one or more of") "or" is used in a list of A, B, or C, for example, if at least one list of A, B, or C is A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein, the phrase "based on" shall not be construed to refer to a closed set of conditions. For example, an exemplary step described as "based on condition A" may be based on both condition A and condition B without departing from the scope of this disclosure. In other words, as used herein, the phrase "based on" shall be construed in the same manner as the phrase "based at least in part."

Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. Non-transitory storage media may be any available media that can be accessed by a general purpose or special purpose computer. By way of example and not limitation, non-transitory computer-readable media may include RAM, ROM, electrically erasable programmable read only memory (EEPROM), compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic A storage device or any other device that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general purpose or special purpose computer or a general purpose or special purpose processor. It can include non-transitory media. Also, any connection is properly termed a computer-readable medium. For example, the software may be transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technology such as infrared, radio, or microwave. If so, such coaxial cables, fiber optic cables, twisted pair, DSL, or wireless technologies such as infrared, radio, or microwave are included in the definition of media. As used herein, a disk (disk or disc) includes a CD, a laser disc, an optical disc, a digital versatile disc (DVD), a floppy disc, and a Blu-ray disc. ) typically reproduce data magnetically, while discs (discs) reproduce data optically using a laser. Combinations of the above should also be included within the scope of computer-readable media.

The description herein is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to this disclosure will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of this disclosure. Therefore, this disclosure is not limited to the embodiments and designs described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (20)

receiving physiological data associated with a user from a wearable device, the physiological data including at least body temperature data;
determining a time series of a plurality of temperature values obtained over a plurality of days based at least in part on the received temperature data, the time series including a plurality of ovulatory cycles of the user; ,
identifying the plurality of ovulatory cycles in the time series of the plurality of body temperature values based at least in part on one or more positive slopes of the time series of the plurality of body temperature values;
identifying an indicator of one or more anovulatory cycles in the time series of the plurality of body temperature values based at least in part on deviations in the plurality of body temperature values from the identified plurality of ovulatory cycles;
generating a message indicative of the one or more anovulatory cycles for display on a graphical user interface of a user device;
including methods. further comprising calculating a deviation in the time series of the plurality of body temperature values, the deviation being a decrease in the plurality of body temperature values from the plurality of body temperature values of the identified plurality of ovulatory cycles; and/or the absence of the plurality of body temperature values following a plurality of ovulatory cycles, wherein identifying the indicator of the one or more anovulatory cycles is based at least in part on calculating the deviation. ,
The method according to claim 1. comparing the plurality of body temperature values of the plurality of identified ovulation cycles with a current value of the plurality of body temperature values;
determining, based at least in part on the comparison, that the dissimilarity matrix score satisfies a threshold;
further comprising: identifying the indicator of the one or more anovulatory cycles is based at least in part on determining that the dissimilarity matrix score meets the threshold;
The method according to claim 1. further comprising identifying one or more positive slopes of spectral power for an ultradian frequency range based at least in part on receiving the physiological data, wherein the identified plurality of ovulatory cycles associated with a positive slope of power,
The method according to claim 1. further comprising identifying one or more positive slopes of the time series of the plurality of body temperature values based at least in part on determining the time series, wherein the step of identifying the plurality of ovulatory cycles comprises: , based at least in part on identifying the one or more positive slopes;
The method according to claim 1. further comprising determining each body temperature value of the plurality of body temperature values based at least in part on receiving the body temperature data, the body temperature data comprising continuous daytime body temperature data;
The method according to claim 1. The physiological data further includes heart rate data, and the method further comprises:
further comprising determining that the received heart rate data exceeds a threshold heart rate of the user during at least a portion of the plurality of days, and determining the indicator of the one or more anovulatory cycles in the time series. the step of identifying is based at least in part on determining that the received heart rate data exceeds a threshold heart rate for the user;
The method according to claim 1. The physiological data further includes heart rate variability data, and the method includes:
further comprising determining that the received heart rate variability data is below a threshold heart rate variability of the user during at least a portion of the plurality of days, and identifying an indicator of an anovulatory cycle in the time series. , based at least in part on determining that the received heart rate variability data is below a threshold heart rate variability of the user;
The method according to claim 1. The physiological data further includes continuous nocturnal respiration rate data, and the method includes:
further comprising determining that, during at least a portion of the plurality of days, the received respiration rate data exceeds a threshold respiration rate for the user; the step of identifying is based at least in part on determining that the received respiration rate data exceeds a threshold respiration rate for the user;
The method according to claim 1. further comprising identifying a fertilization window based at least in part on identifying the plurality of ovulatory cycles and identifying the one or more indicators of anovulatory cycles in the time series;
The method according to claim 1. further comprising transmitting the message indicating the identified indicator of the one or more anovulatory cycles to the user device, the user device being associated with a clinician;
The method according to claim 1. further comprising causing an ovulation tracking tag to be displayed on a graphical user interface of a user device associated with the user based at least in part on identifying the plurality of ovulation cycles;
The method according to claim 1. further comprising causing a graphical user interface of a user device associated with the user to display a message associated with the identified plurality of ovulation cycles;
The method according to claim 1. The message includes a time interval in which the identified plurality of ovulatory cycles have occurred, a period of time between a future ovulatory cycle and a previous ovulatory cycle, a time interval in which the ovulatory cycle is expected to occur, and the identified plurality of ovulatory cycles. further comprising a request to enter symptoms associated with the identified ovulatory cycles, educational content associated with the identified plurality of ovulatory cycles, or a combination thereof;
14. The method according to claim 13. further comprising inputting the physiological data to a machine learning classifier, wherein identifying the one or more indicators of anovulatory cycles includes inputting the physiological data to the machine learning classifier at least in part. based on
The method according to claim 1. The wearable device includes a wearable ring device.
The method according to claim 1. the wearable device collects the physiological data from the user based on arterial blood flow;
The method according to claim 1. A device,
a processor;
a memory coupled to the processor;
instructions stored in the memory and executable by the processor, the instructions comprising:
receiving physiological data associated with the user from the wearable device, the physiological data including at least body temperature data;
determining a time series of a plurality of temperature values obtained over a plurality of days based at least in part on the received temperature data, the time series including a plurality of ovulation cycles of the user;
identifying the plurality of ovulatory cycles in the time series of the plurality of body temperature values based at least in part on one or more positive slopes of the time series of the plurality of body temperature values;
identifying an indicator of one or more anovulatory cycles in the time series of the plurality of body temperature values based at least in part on deviations in the plurality of temperature values from the identified plurality of ovulatory cycles;
generating a message indicative of the one or more anovulatory cycles for display on a graphical user interface of a user device;
command and
A device comprising: The instructions are further executable by the processor, causing the apparatus to:
calculating a deviation in the time series of the plurality of body temperature values, the deviation being a decrease in the plurality of body temperature values from the plurality of body temperature values of the identified plurality of ovulation cycles; identifying the indicator of the one or more anovulatory cycles is based at least in part on calculating the deviation, including the absence of the plurality of body temperature values following a cycle, or both;
19. Apparatus according to claim 18. a non-transitory computer-readable medium storing code, the code being instructions executable by a processor;
receiving physiological data associated with a user from a wearable device, the physiological data including at least body temperature data;
determining a time series of a plurality of temperature values obtained over a plurality of days based at least in part on the received body temperature data, the time series including a plurality of ovulation cycles of the user;
identifying the plurality of ovulatory cycles in the time series of the plurality of body temperature values based at least in part on one or more positive slopes of the time series of the plurality of body temperature values;
identifying an indicator of one or more anovulatory cycles in the time series of the plurality of body temperature values based at least in part on deviations in the plurality of temperature values from the identified plurality of ovulatory cycles;
generating a message indicative of the one or more anovulatory cycles for display on a graphical user interface of a user device;
A non-transitory computer-readable medium containing instructions for.

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