Method of operating a device for pregnancy detection from wearable-based physiological data, device and computer-readable medium

Abstract

Methods, systems and devices for pregnancy detection 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 system may then identify a body temperature rise in the time series of body temperature values ​​relative to a body temperature baseline of the user, and detect an indication of pregnancy in the user based on the identified body temperature rise. The indication of pregnancy may be detectable from the identified body temperature rise before it is detectable from a threshold increase in hormone rise relative to the user's hormone baseline. The system may cause a graphical user interface to display the detected indication of pregnancy.

Description

[Technical Field]

[0001] cross reference This application claims the benefit of U.S. Non-Provisional Patent Application No. 17 / 709,938, entitled "PREGNANCY DETECTION FROM WEARABLE-BASED PHYSIOLOGICAL DATA", filed by Thigpen et al. on March 31, 2022, which claims the benefit of U.S. Provisional Patent Application No. 63 / 169,314, entitled "WOMEN'S HEALTH TRACKING", filed by Aschbacher et al. on April 1, 2021. Each of the foregoing is assigned to the assignee of the present application and is expressly incorporated herein by reference.

[0002] Technical field The following relates to wearable devices and data processing, including pregnancy detection from wearable-based physiological data. [Background Art]

[0003] Some wearable devices may be configured to collect data related to body temperature and heart rate from a user. For example, some wearable devices may be configured to detect cycles associated with reproductive health. However, conventional cycle detection techniques implemented by wearable devices are insufficient. [Brief Description of the Drawings]

[0004] [Figure 1] FIG. 1 is a diagram illustrating an example of a system that supports pregnancy detection from wearable-based physiological data in accordance with aspects of the present disclosure.

[0005] [Figure 2] FIG. 2 is a diagram illustrating an example of a system that supports pregnancy detection from wearable-based physiological data in accordance with aspects of the present disclosure.

[0006] [Figure 3] This figure shows an example of a system that supports pregnancy detection from wearable-based physiological data according to the embodiments of this disclosure.

[0007] [Figure 4] This figure shows an example of a timing diagram that supports pregnancy detection from wearable-based physiological data according to an aspect of the present invention.

[0008] [Figure 5] This figure shows an example of a timing diagram that supports pregnancy detection from wearable-based physiological data according to the embodiments of this disclosure.

[0009] [Figure 6] This figure shows an example of a timing diagram that supports pregnancy detection from wearable-based physiological data according to the embodiments of this disclosure.

[0010] [Figure 7] This figure shows an example of a graphical user interface (GUI) that supports pregnancy detection from wearable-based physiological data according to an aspect of this disclosure.

[0011] [Figure 8] This is a block diagram of a device that supports pregnancy detection from wearable-based physiological data according to an aspect of the present disclosure.

[0012] [Figure 9] This is a block diagram of a wearable application that supports pregnancy detection from wearable-based physiological data according to the embodiments of this disclosure.

[0013] [Figure 10] This is a diagram of a system including a device that supports pregnancy detection from wearable-based physiological data, according to an aspect of the present disclosure.

[0014] [Figure 11] This flowchart illustrates a method for supporting pregnancy detection from wearable-based physiological data according to the embodiments of this disclosure. [Figure 12] This flowchart illustrates a method for supporting pregnancy detection from wearable-based physiological data according to the embodiments of this disclosure. [Figure 13] This flowchart illustrates a method for supporting pregnancy detection from wearable-based physiological data according to the embodiments of this disclosure. [Modes for carrying out the invention]

[0015] Some wearable devices are configured to collect physiological data from users, including body temperature and heart rate data. This collected physiological data may be used to analyze the user's movements and other activities, such as sleep patterns. Many users desire greater insight into their physical health, including sleep patterns, activity levels, and overall physical health. In particular, many users may desire greater insight into women's health, including their menstrual cycle, ovulation, fertilization patterns, and pregnancy. However, typical cycle tracking or women's health devices and applications lack the ability to provide robust prediction, detection, and insight for several reasons.

[0016] First, typical cycle prediction applications require users to manually measure their own body temperature daily at discrete times using a device. This single body temperature data point may not provide sufficient context to accurately capture or predict true temperature changes indicative of a woman's health cycle pattern, and accurate capture may be difficult considering the sensitivity of the measurement device to the user's movements or actions. Second, even for wearable devices or devices that measure a user's body temperature more frequently throughout the day, typical devices and applications lack the ability to collect other physiological, behavioral or contextual inputs from the user that can be combined with measured body temperature to more comprehensively understand the full set of physiological contributions to a woman's cycle.

[0017] Aspects of the present disclosure are directed to techniques for pregnancy detection. 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 acquired over a plurality of days. For example, aspects of the present disclosure may identify one or more morphological features, such as a temperature rise in the time series of body temperature values, from a graphical representation of the time series of body temperature values. Accordingly, aspects of the present disclosure detect an indication of pregnancy in the time series based on identifying morphological features (e.g., body temperature rise). In such cases, the detected indication of pregnancy may be associated with the body temperature rise in the time series and / or one or more additional morphological features in the time series of body temperature values relative to the user's body temperature baseline. The user's body temperature baseline may be based on the user's non-pregnant body temperature baseline, the user's menstrual cycle-specific body temperature baseline, or both. In some cases, detecting early pregnancy may indicate an indication of pregnancy that is detectable from the identified body temperature rise before it is detectable from a threshold increase in hormone elevation relative to the user's hormone baseline. For example, early pregnancy detection may be possible before the user's missed period, before confirmation using a home pregnancy test, or both.

[0018] In some implementations, the system can analyze historical body temperature data from a user, detect an indicator of pregnancy, and generate an indicator for the user indicating that the user has been detected as pregnant. The user may confirm whether pregnancy is confirmed as indicated by the system from the historical data, and the system may incorporate this user input into a prediction function (e.g., a machine learning model for detecting pregnancy indicators). The system may also analyze temperature series data in real time, and may detect pregnancy in real time based on identifying one or more morphological features in the time series of body temperature data and / or based on user input from pregnancy confirmation.

[0019] For the purposes of the present disclosure, the terms "early pregnancy", "early pregnancy detection", "pregnancy" and the like may be used to refer to the period during which one or more offspring develop in the uterus. During pregnancy, a user may experience a series of changes in hormone production and the structure of the uterus of the female reproductive system throughout the pregnancy. Pregnancy begins with conception, when sperm fertilizes an egg, and typically ends with childbirth at about 40 weeks. Early pregnancy detection may refer to detecting pregnancy before an increase in hormones relative to the user's baseline hormone level becomes detectable using conventional home pregnancy tests. For example, early pregnancy detection can be performed before pregnancy is confirmed by conventional methods of detecting increased hormone levels, such as home pregnancy tests, blood tests, ultrasound examinations, or combinations thereof. In some cases, early pregnancy detection may be performed before the user experiences missed menstruation (e.g., missed menstrual period) indicating an increase in hormones relative to the user's baseline hormone level.

[0020] Some aspects of this disclosure relate to the detection of indicators of pregnancy before a user experiences any symptoms or effects of pregnancy. However, the techniques described herein may also be used to detect indicators of pregnancy when a user does not exhibit symptoms or is unaware of any symptoms. In some implementations, a computing device may use a temperature sensor to detect indicators of pregnancy. In such cases, the computing device may detect indicators of pregnancy without the user tagging or labeling these events.

[0021] In conventional systems, pregnancy may be detected by a home pregnancy test, blood test, ultrasound, or a combination thereof, after one or more hormones indicating pregnancy (e.g., human chorionic gonadotropin (HCG)) have risen relative to the user's hormonal baseline. In other cases, pregnancy may be detected based on symptoms experienced by the user (e.g., cessation of menstruation, nausea, fatigue, tender breasts, etc.). In such cases, pregnancy may be detected after a change (e.g., an increase) in the user's hormone levels and / or confirmed during a medical examination. The techniques described herein may continuously collect physiological data from the user based on measurements taken from a wearable that continuously measures the user's surface body temperature and signals extracted from blood flow, such as arterial blood flow (e.g., via PPG). In some implementations, a computing device may continuously sample the user's body temperature throughout the day and night. Sampling at a sufficient rate (e.g., one sample per minute) throughout the night (or during specific phases of the night, and / or between specific phases of the sleep cycle, as described in more detail below) may provide sufficient body temperature data for the analysis described herein.

[0022] In some cases, continuous finger temperature measurement can capture temperature fluctuations (e.g., small or large fluctuations) that may not be apparent from core body temperature. For example, continuous finger temperature measurement can capture minute-by-minute or hourly temperature fluctuations that may not be provided by other temperature measurements elsewhere on the body, or if the user were manually measuring their temperature once a day. Therefore, data collected by computing devices can be used to detect when a user is pregnant.

[0023] The technologies described herein may notify the user of detected pregnancy indicators in various ways. For example, the system may notify the user of detected pregnancy indicators and provide recommendations to the user by displaying messages or other notifications on the user device's graphical user interface (GUI). In one example, the GUI may display the time interval at which the pregnancy was detected and recommendations for the user to prepare for different stages of pregnancy. In some implementations, the system may provide tag recommendations to the user. For example, the system may recommend mood and symptom tags (e.g., nausea, fatigue, etc.) to the user at predetermined times during pregnancy (e.g., 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.

[0024] The system may also include graphics or text that show the data used to detect indicators of pregnancy. For example, the GUI may display a notification that pregnancy has been detected based on a deviation in the user's body temperature from a normal baseline. In some cases, the GUI may display a notification that pregnancy has been detected based on a deviation in the user's heart rate from a normal baseline, a deviation in the user's respiratory rate from a normal baseline, or both. Based on early detection (e.g., before the user experiences symptoms), the user can take early measures that may help mitigate the severity of future symptoms associated with pregnancy. In addition, based on early warning of pregnancy, the user can modify / schedule their daily activities (e.g., work and leisure time).

[0025] Aspects of this disclosure are first described in the context of a system that supports the collection of physiological data from a user via a wearable device. Additional aspects of this disclosure are described in the context of exemplary timing diagrams and exemplary GUIs. Aspects of this disclosure are further illustrated and described by device diagrams, system diagrams and flowcharts related to pregnancy detection from wearable-based physiological data.

[0026] Figure 1 shows an example of a system 100 that supports pregnancy detection from wearable-based physiological data according to an aspect of the present disclosure. The system 100 includes a plurality of electronic devices (e.g., a wearable device 104, a user device 106) that can be worn and / or operated by one or more users 102. The system 100 further includes a network 108 and one or more servers 110.

[0027] The electronic devices may include any electronic devices known in the art, including wearable devices 104 (e.g., ring wearable devices, wristwatch wearable devices, etc.) and user devices 106 (e.g., smartphones, laptops, tablets). Each electronic device associated with a user 102 may include one or more of the following functions: namely, 1) measuring physiological data, 2) storing the measured data, 3) processing the data, 4) providing output to the user 102 based on the processed data (e.g., via a GUI), and 5) communicating data with each other and / or other computing devices. Different electronic devices may perform one or more of these functions.

[0028] Exemplary wearable devices 104 may include wearable computing devices such as a ring computing device (hereinafter, "ring") configured to be worn on the finger of user 102, a wrist computing device (e.g., a smartwatch, fitness band, or bracelet) configured to be worn on the wrist of user 102, and / or a head-mounted computing device (e.g., glasses / goggles). Wearable devices 104 may also include bands around the head (e.g., a headband on the forehead), bands around the arms (e.g., a forearm band and / or an upper arm band) and / or bands around the legs (e.g., a thigh or calf band), bands, straps (e.g., flexible or non-flexible bands or straps), stick-on sensors, etc., which may be positioned in other locations such as behind the ears or under the armpits. Wearable devices 104 may also be attached to clothing or included in clothing. For example, wearable devices 104 may be included in the pockets and / or pouches of clothing. As another example, the wearable device 104 may be clipped and / or pinned to clothing, or otherwise kept close to the user 102. Exemplary clothing items may include, but are not limited to, hats, shirts, gloves, trousers, socks, outerwear (e.g., jackets), and underwear. In some implementations, the wearable device 104 may be included with other types of devices, such as training / sports devices used during physical activity. For example, the wearable device 104 may be attached to, or included with, bicycles, skis, tennis rackets, golf clubs, and / or training weights.

[0029] Much of this disclosure can be described in the context of the ring wearable device 104. Therefore, the terms “ring 104,” “wearable device 104,” and similar terms may be used interchangeably unless otherwise specified herein. However, since embodiments of this disclosure are intended to be performed using other wearable devices (e.g., wristwatch wearable devices, necklace wearable devices, bracelet wearable devices, earring wearable devices, anklet wearable devices, etc.), the use of the term “ring 104” should not be considered restrictive.

[0030] In some embodiments, the user device 106 may include handheld mobile computing devices such as smartphones and tablet computing devices. The user device 106 may also include personal computers such as laptops and desktop computing devices. Other exemplary user devices 106 may include server computing devices that can communicate with other electronic devices (e.g., via the Internet). In some implementations, the computing device may include medical devices such as external wearable computing devices (e.g., Holter monitors). The medical device may also include implantable medical devices such as pacemakers and electrodefibrillators. Other exemplary user devices 106 may include home computing devices such as Internet of Things (IoT) devices (e.g., IoT devices), smart TVs, smart speakers, smart displays (e.g., video call displays), hubs (e.g., wireless communication hubs), security systems, smart appliances (e.g., thermostats and refrigerators), and fitness equipment.

[0031] Several electronic devices (e.g., wearable device 104, user device 106) may measure the physiological parameters of each user 102, such as photoplethysmography waveforms, continuous skin temperature, pulsed waveforms, respiratory rate, heart rate, heart rate variability (HRV), actigraphy, galvanic skin response, pulsed oxygen concentration, 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 the received physiological data measured by other devices.

[0032] In some implementations, 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, user 102 may have a ring (e.g., wearable device 104) for measuring physiological parameters. User 102 may have or be associated with a user device 106 (e.g., a mobile device, smartphone), where the wearable device 104 and user device 106 are communicatively coupled to each other. In some cases, user device 106 may receive data from wearable device 104 and perform some / all of the calculations described herein. In some implementations, user device 106 may also measure physiological parameters described herein, such as exercise / activity parameters.

[0033] For example, as shown in Figure 1, a first user 102-a (user 1) may operate or be associated with a wearable device 104-a (e.g., a ring 104-a) and a user device 106-a, which may operate as described herein. In this example, the user device 106-a associated with user 102-a may process / store physiological parameters measured by the ring 104. In comparison, a second user 102-b (user 2) may be associated with a ring 104-b, a wristwatch wearable device 104-c (e.g., a watch 104-c), and a user device 106-b, where the user device 106-b associated with user 102-b may process / store physiological parameters measured by the ring 104-b and / or the watch 104-c. Furthermore, the nth user 102-n (user N) may be associated with the configuration of the electronic devices described herein (e.g., ring 104, user device 106-n). In some embodiments, the wearable device 104 (e.g., ring 104, watch 104) and other electronic devices may be communicably coupled to the user device 106 of each user 102 via Bluetooth®, Wi-Fi, and other radio protocols.

[0034] In some implementations, the ring 104 of system 100 (e.g., wearable device 104) may be configured to collect physiological data from each user 102 based on arterial blood flow in the user's finger. In particular, the ring 104 may collect physiological data based on arterial blood flow in the user's finger by utilizing one or more LEDs (e.g., red LED, green LED) that emit light on the palm side of the user's finger. In some implementations, the ring 104 may use a combination of both green and red LEDs to acquire physiological data. Physiological data may include, but is not limited to, any physiological data known in the art, including body temperature data, accelerometer data (e.g., motion / exercise data), heart rate data, HRV data, blood oxygen saturation data, or any combination thereof.

[0035] Since red and green LEDs have been found to have distinct advantages when acquiring physiological data via different parts of the body under different conditions (e.g., bright / dark, active / inactive), using both green and red LEDs may offer several advantages over other solutions. For example, green LEDs have been shown to perform better during exercise. Furthermore, using multiple LEDs (e.g., green and red LEDs) dispersed around the ring 104 has been shown to perform better than wearable devices that utilize LEDs placed in close proximity to each other, such as within a wristwatch wearable device. In addition, blood vessels in the fingers (e.g., arteries, capillaries) are more easily accessible via LEDs than blood vessels in the wrist. In particular, arteries in the wrist are located in the lower part of the wrist (e.g., the palm side of the wrist), meaning that only capillaries are accessible in the upper part of the wrist (e.g., the back of the wrist), where wearable watch devices and similar devices are typically worn. Thus, utilizing LEDs and other sensors within the ring 104 has been shown to exhibit superior performance compared to wearable devices worn on the wrist, because the ring 104 has greater access to arteries (compared to capillaries), thereby yielding stronger signals and more valuable physiological data.

[0036] The electronic devices of system 100 (e.g., 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 Figure 1, the electronic device (e.g., user device 106) may be communicatively coupled to one or more servers 110 via network 108. Network 108 may implement a transmission control protocol such as the Internet and the Internet Protocol (TCP / IP), or it may implement other network 108 protocols. The network connection between network 108 and each electronic device can facilitate the transfer of data via email, web, text messages, mail, or any other suitable form of interaction within the computer network 108. For example, in some implementations, a ring 104-a associated with a first user 102-a may be communicatively coupled to a user device 106-a, which is communicatively coupled to a server 110 via network 108. In additional or alternative cases, the wearable device 104 (e.g., a ring 104, a watch 104) may be coupled to the network 108 so as to be able to communicate directly with it.

[0037] System 100 may provide an on-demand database service between the user device 106 and one or more servers 110. In some cases, the server 110 may receive data from the user device 106 via the network 108, store the data, and analyze it. Similarly, the server 110 may provide data to the user device 106 via the network 108. In some cases, the server 110 may be located in one or more data centers. The server 110 may be used for data storage, management, and processing. In some implementations, the server 110 may provide a web-based interface to the user device 106 via a web browser.

[0038] In some embodiments, the system 100 may detect the duration of time that user 102 is asleep and classify that duration into one or more sleep stages (e.g., sleep stage classification). For example, as shown in Figure 1, user 102-a may be associated with a wearable device 104-a (e.g., a ring 104-a) and a user device 106-a. In this example, the ring 104-a may collect physiological data associated with user 102-a, including body temperature, heart rate, HRV, respiratory rate, etc. In some embodiments, the data collected by the ring 104-a may be input to a machine learning classifier, which is configured to determine the duration of time that user 102-a is asleep (or was asleep). Furthermore, the machine learning classifier may be configured to classify the duration into different sleep stages, including wakefulness, rapid eye movement (REM) sleep, light sleep (non-REM) and deep sleep (NREM). In some embodiments, the classified sleep stages may be displayed to user 102-a via the GUI of user device 106-a. The sleep stage classification may also be used to provide user 102-a with feedback on the user's sleep patterns, such as recommended bedtimes and recommended wake-up times. Furthermore, in some implementations, the sleep stage classification techniques described herein may be used to calculate scores for each user, such as sleep scores and readiness scores.

[0039] In some embodiments, system 100 may further improve physiological data acquisition, data processing procedures, and other techniques described herein by utilizing features derived from circadian rhythms. The term circadian rhythm may refer to a 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 adjustment models to improve physiological data acquisition, analysis, and data processing. For example, a circadian rhythm adjustment model may be input into a machine learning classifier along with physiological data collected from user 102 via a wearable device 104-a. In this example, the circadian rhythm adjustment model may be configured to "weight" or adjust the physiological data collected through the user's natural, approximately 24-hour circadian rhythm. In some implementations, the system may initially start with a "baseline" circadian rhythm adjustment model and modify the baseline model using physiological data collected from each user 102 to generate a specific, adjusted, and individualized circadian rhythm adjustment model for each user 102.

[0040] In some embodiments, system 100 may utilize other biological rhythms to further improve the collection, analysis, and processing of physiological data by 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 “weights” of the data by the day of the week. Biological rhythms that may require adjustment to the model in this manner include: 1) ultradian (faster than the 24-hour rhythm, including sleep cycles in the sleep state and fluctuations in measured physiological variables between the wakefulness state ranging from less than an hour to several hours); 2) circadian rhythms; 3) non-endogenous daily rhythms that are shown to be imposed on top of the circadian rhythm, such as work schedules; 4) weekly rhythms, or other artificial time cycles imposed exogenously (e.g., in a hypothetical culture with a 12-day “week,” a 12-day rhythm can be used); 5) multi-day ovarian rhythms in women and spermatogenesis rhythms in men; 6) lunar rhythms (associated with people living in environments with little or no artificial light); and 7) seasonal rhythms.

[0041] Biological rhythms are not always steady. For example, many women experience variability in ovarian cycle length over multiple cycles, and ultradian rhythms, even within a single user, are not expected to occur at exactly the same time or periodicity across days. Therefore, the detection of these rhythms can be improved by using signal processing techniques sufficient to quantify their frequency composition while maintaining the temporal resolution of these rhythms in physiological data, and by assigning the phase of each rhythm to each moment measured, thereby correcting the adjustment model and time interval comparison. Biological rhythm-adjustment models and parameters can be added, in a combination of linear or nonlinear approaches, as needed, to more accurately capture the dynamic physiological baseline of an individual or group of individuals.

[0042] In some embodiments, each device of system 100 may support techniques for pregnancy detection based on data collected by wearable device 104. In particular, system 100 as shown in Figure 1 may support techniques for detecting indicators of pregnancy in user 102 and displaying the detected indicators of pregnancy on user device 106 corresponding to user 102. For example, as shown in Figure 1, user 1 (user 102-a) may be associated with wearable device 104-a (e.g., 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, etc. In some embodiments, the data collected by ring 104-a may be used to detect indicators of pregnancy while user 1 is experiencing pregnancy. Detecting indicators of pregnancy 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. When the system 100 detects a user's pregnancy, it may selectively display pregnancy indicators on the GUI of the user device 106-a.

[0043] In some implementations, upon receiving physiological data (including, for example, body temperature data), system 100 may determine a time series of body temperature values ​​obtained over several days. System 100 may identify an increase in body temperature in the time series of body temperature values ​​relative to the user's temperature baseline. In such cases, system 100 may detect an indicator of pregnancy based on the identified increase in body temperature. In some cases, the indicator of pregnancy may be detectable from the identified increase in body temperature before it is detectable from a threshold increase in hormone levels relative to the user's hormone baseline. An example of a pregnancy indicator may be detecting that the user is currently pregnant and / or is already pregnant.

[0044] In some cases, system 100 may prompt user 1 (e.g., via the GUI of user device 106) to confirm whether user 102-a has experienced a confirmed pregnancy (e.g., blood test, home pregnancy test, ultrasound, etc.), and may selectively adjust user 102-a's readiness score based on the user's confirmation of pregnancy. In some implementations, system 100 may generate alerts, messages, or recommendations for user 1 (e.g., via ring 104-a, user device 106-a, or both) based on indicators of detected pregnancy, where alerts may provide insights into the detected pregnancy, such as the timing and / or duration of the pregnancy. In some cases, messages may provide insights into symptoms associated with the detected user's pregnancy, one or more medical conditions associated with the detected pregnancy, educational videos and / or text (e.g., content), or a combination thereof, related to any phase of the pregnancy.

[0045] Those skilled in the art will understand that one or more aspects of this disclosure may be implemented in System 100 to additionally or alternatively address other problems not described above. Furthermore, aspects of this disclosure may provide technical improvements to the “conventional” systems or processes described herein. However, this specification and the accompanying drawings only include illustrative technical improvements resulting from implementing aspects of this disclosure and do not represent all of the technical improvements provided within the claims.

[0046] Figure 2 shows an example of a system 200 that supports pregnancy detection from wearable-based physiological data according to an aspect of the present disclosure. System 200 may implement or be implemented by system 100. In particular, system 200 shows an example of a ring 104 (e.g., wearable device 104), a user device 106, and a server 110, as described with reference to Figure 1.

[0047] In some embodiments, the ring 104 may be configured to be worn around the user's finger and may determine one or more of the user's physiological parameters 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, respiratory rate, heart rate, HRV, blood oxygen level, etc.

[0048] The system 200 further includes a user device 106 (e.g., a smartphone) that communicates with the ring 104. For example, the ring 104 may communicate wirelessly and / or via wired communication with the user device 106. In some implementations, the ring 104 may transmit measured and processed data (e.g., body temperature data, photoplethysmogram (PPG) data, exercise / accelerometer data, ring input data, etc.) to the user device 106. The user device 106 may also transmit data to the ring 104, such as ring 104 firmware / configuration updates. The user device 106 may process the data. In some implementations, the user device 106 may transmit the data to the server 110 for processing and / or storage.

[0049] The ring 104 may include a housing 205 which may include an inner housing 205-a and an outer housing 205-b. In some embodiments, the housing 205 of the ring 104 may store or otherwise include various components of the ring, including, but not limited to, device electronics, a power supply (e.g., a battery 210 and / or a capacitor), and one or more substrates (e.g., printed circuit boards) interconnecting the device electronics and / or the power supply. The device electronics may include device modules (e.g., hardware / software) such as a processing module 230-a, a memory 215, a communication module 220-a, 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 (e.g., a PPG system 235), and one or more motion sensors 245.

[0050] The sensor may include associated modules (not shown) configured to communicate with each component / module of the ring 104 and generate signals associated with each sensor. In some embodiments, each component / module of the ring 104 may be coupled to communicate with one another via wired or wireless connections. Furthermore, the ring 104 may include additional and / or alternative sensors or other components configured to collect physiological data from the user, including light sensors (e.g., LEDs), oximeters, etc.

[0051] The ring 104 illustrated and described with reference to Figure 2 is provided for illustrative purposes only. Thus, the ring 104 may include additional or alternative components such as those shown in Figure 2. Other rings 104 that provide the functionality described herein may be manufactured. For example, a ring 104 having fewer components (e.g., sensors) may be manufactured. In a particular example, a ring 104 may be manufactured having a single temperature sensor 240 (or other sensor), a power supply, and device electronics configured to read the single temperature sensor 240 (or other sensor). In another specific example, the temperature sensor 240 (or other sensor) may be attached to the user's finger (e.g., using a clamp, spring clamp, etc.). In this case, the sensor may be wired to another computing device, such as a wrist-worn computing device that reads the temperature sensor 240 (or other sensor). In other examples, a ring 104 including additional sensors and processing functions may be manufactured.

[0052] The housing 205 may include one or more housing 205 components. The housing 205 may include an outer housing 205-b component (e.g., a shell) and an inner housing 205-a component (e.g., molding). The housing 205 may include additional components (e.g., additional layers) not explicitly shown in Figure 2. For example, in some implementations, the ring 104 may include one or more insulating layers that electrically insulate the device electronics and other conductive materials (e.g., electrical traces) from the outer housing 205 (e.g., a metallic outer housing 205-b). The housing 205 may provide structural support for the device electronics, battery 210, substrate 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. The housing 205 may also protect the device electronics, battery 210 and substrate from water and / or other chemicals.

[0053] The outer housing 205-b may be manufactured from one or more materials. In some implementations, the outer housing 205-b may contain a metal such as titanium, which can be relatively lightweight and provide strength and wear resistance. The outer housing 205-b may also be manufactured from other materials such as polymers. In some implementations, the outer housing 205-b may be protective as well as decorative.

[0054] The inner housing 205-a may be configured to interface with the user's finger. The inner housing 205-a may be formed from a polymer (e.g., a medical-grade polymer) or other material. In some implementations, the 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, the inner housing 205-a may be molded on top of the outer housing 205-b. For example, the inner housing 205-a may contain a polymer that is molded (e.g., injection-molded) to fit the outer housing 205 metal shell.

[0055] The ring 104 may include one or more substrates (not shown). The device electronics and battery 210 may be contained on one or more substrates. For example, the device electronics and battery 210 may be mounted on one or more substrates. The exemplary substrates may include one or more printed circuit boards (PCBs), such as flexible PCBs (e.g., polyimide). In some implementations, the electronics / battery 210 may include surface-mount devices (e.g., surface-mount technology (SMT) devices) on the flexible PCB. In some implementations, one or more substrates (e.g., one or more flexible PCBs) may include electrical traces that provide electrical communication between the device electronics. The electrical traces may also connect the battery 210 to the device electronics.

[0056] The device electronics, battery 210, and substrate may be arranged within the ring 104 in various ways. In some implementations, one substrate containing the device electronics may be mounted along the bottom (e.g., lower half) of the ring 104 so that 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 along the top of the ring 104 (e.g., on another substrate).

[0057] The various components / modules of ring 104 represent the functionality (e.g., circuits and other components) that may be included in ring 104. A module may include any discrete and / or integrated electronic circuit components that implement analog and / or digital circuits capable of generating the functionality resulting from the module herein. For example, a module may include analog circuits (e.g., amplifiers, filtering circuits, analog-to-digital converters and / or other signal conditioning circuits). A module may also include digital circuits (e.g., combinational logic circuits or successive logic circuits, memory circuits, etc.).

[0058] The memory 215 (memory module) of ring 104 may include any volatile, non-volatile, magnetic, or electrical medium, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable and programmable ROM (EEPROM), flash memory, or any other memory device. The memory 215 may store any of the data described herein. For example, the memory 215 may be configured to store data collected by the respective sensors and PPG system 235 (e.g., exercise data, body temperature data, PPG data). Furthermore, the memory 215 may include instructions that, when executed by one or more processing circuits, cause the module to perform various functions attributed to the module herein. The device electronics of ring 104 described herein are merely examples of device electronics. Therefore, the types of electronic components used to implement the device electronics may vary based on design considerations.

[0059] The functions resulting from the modules of ring 104 described herein may be embodied as one or more processors, hardware, firmware, software, or any combination thereof. Describing different features as modules is intended to highlight different functional aspects and does not necessarily imply that such modules must be implemented by separate hardware / software components. Rather, the functionality associated with one or more modules may be performed by separate hardware / software components or integrated within a common hardware / software component.

[0060] The processing module 230-a of ring 104 may include one or more processors (e.g., processing units), microcontrollers, digital signal processors, system-on-a-chip (SoC), and / or other processing devices. The processing module 230-a communicates with modules included in ring 104. For example, the processing module 230-a may send data to and receive data from modules and other components of ring 104, such as sensors. As described herein, modules may be implemented by various circuit components. Thus, modules may also be referred to as circuits (e.g., communication circuits and power circuits).

[0061] The processing module 230-a can communicate with memory 215. When executed by the processing module 230-a, memory 215 may contain computer-readable instructions that cause the processing module 230-a to perform various functions originating from the processing module 230-a. In some implementations, the processing module 230-a (e.g., a microcontroller) may include additional features associated with other modules, such as communication functions provided by the communication module 220-a (e.g., an integrated Bluetooth® Low Energy transceiver) and / or additional onboard memory 215.

[0062] The communication module 220-a may include circuitry that provides wireless and / or wired communication with the user device 106 (e.g., the communication module 220-b of the user device 106). In some implementations, the communication modules 220-a and 220-b may include wireless communication circuits such as Bluetooth and / or Wi-Fi circuits. In some implementations, the communication modules 220-a and 220-b may include wired communication circuits such as Universal Serial Bus (USB) communication circuits. Using the communication module 220-a, the ring 104 and the user device 106 may be configured to communicate with each other. The ring's processing module 230-a may be configured to send data to / receive data from the user device 106 via the communication module 220-a. Illustrative data may include, but is not limited to, 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). The ring processing module 230-a may also be configured to receive updates (e.g., software / firmware updates) and data from the user device 106.

[0063] The ring 104 may include a battery 210 (e.g., a rechargeable battery 210). An exemplary battery 210 may include a lithium-ion or lithium-polymer type battery 210, but a variety of battery options are possible. The battery 210 may be charged wirelessly. In some implementations, the ring 104 may include a power source other than the battery 210, such as a capacitor. The power source (e.g., battery 210 or capacitor) may have a curved shape that matches the curve of the ring 104. In some embodiments, the charger or other power source may include additional sensors that can be used to collect data in addition to or supplement the data collected by the ring 104 itself. Furthermore, the charger or other power source of the ring 104 may function as a user device 106, in which case the charger or other power source of the ring 104 may be configured to receive data from the ring 104, store and / or process the data received from the ring 104, and communicate data between the ring 104 and the server 110.

[0064] In some embodiments, the ring 104 includes a power module 225 that can control the charging of the battery 210. For example, the power module 225 may interface with an external wireless charger that charges the battery 210 when it interfaces with the ring 104. The charger may include a data structure that mates with the datum structure of the ring 104 to produce a specific orientation with the ring 104 during charging. The power module 225 may also regulate the voltage of the device electronics, regulate the power output to the device electronics, and monitor the charge state of the battery 210. In some implementations, the battery 210 may include a protection circuit module (PCM) that protects the battery 210 from high-current discharge, overvoltage during charging of 104, and undervoltage during discharging of 104. The power module 225 may also include electrostatic discharge (ESD) protection.

[0065] One or more temperature sensors 240 may be electrically coupled to the processing module 230-a. The temperature sensors 240 may be configured to generate a body temperature signal (e.g., body temperature data) indicating the body temperature read or sensed by the temperature sensors 240. The processing module 230-a may determine the user's body temperature at the location of the temperature sensors 240. For example, in the ring 104, the body temperature data generated by the temperature sensors 240 may indicate the user's body temperature (e.g., skin temperature) at the user's finger. In some implementations, the temperature sensors 240 may be in contact with the user's skin. In other implementations, a portion of the housing 205 (e.g., the inner housing 205-a) may form a barrier (e.g., a thin thermally conductive barrier) between the temperature sensors 240 and the user's skin. In some implementations, the portion of the ring 104 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 the temperature sensors 240. The heat-insulating portion can insulate the ring 104 (for example, the temperature sensor 240) from the ambient temperature.

[0066] In some implementations, the temperature sensor 240 may generate a digital signal (e.g., body temperature data) that can be used by the processing module 230-a to determine body temperature. In another example, if the temperature sensor 240 includes a passive sensor, the processing module 230-a (or the temperature sensor 240 module) may measure the current / voltage generated by the temperature sensor 240 and determine body temperature based on the measured current / voltage. The exemplary temperature sensor 240 may include 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.

[0067] The processing module 230-a may sample the user's body temperature over time. For example, the processing module 230-a may sample the user's body temperature according to a sampling rate. An exemplary sampling rate may include one sample per second, but the 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. In some implementations, the processing module 230-a may continuously sample the user's body temperature throughout the day and night. Sampling at a sufficient rate throughout the day (e.g., one sample per second, one sample per minute, etc.) may provide sufficient body temperature data for the analysis described herein.

[0068] The processing module 230-a may store the sampled body temperature data in 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 the average body temperature value over a certain period of time. In one example, the processing module 230-a may determine the average body temperature value per minute by summing all the body temperature values ​​collected in one minute and dividing by the number of samples per minute. In a particular example where body temperature is sampled at one sample per second, the average body temperature may be the sum of all sampled body temperatures per minute divided by 60 seconds. Memory 215 may store average body temperature values ​​over time. In some implementations, to conserve memory, memory 215 may store average body temperatures (e.g., one per minute) instead of sampled body temperatures.

[0069] The sampling rate that can 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 change throughout the day / night. In some implementations, ring 104 may filter / reject body temperature readings that do not indicate physiological changes, such as large spikes in body temperature (e.g., temperature spikes from a hot shower). In some implementations, ring 104 may filter / reject body temperature readings that may be unreliable due to other factors, such as excessive exercise during exercise (e.g., as indicated by the exercise sensor 245).

[0070] Ring 104 (e.g., 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 transfer the sampled body temperature data and / or average body temperature data to server 110 for storage and / or further processing.

[0071] Although the ring 104 is shown to include a single temperature sensor 240, the ring 104 may include multiple temperature sensors 240 in one or more positions, such as being positioned along the inner housing 205-a near the user's finger. In some implementations, the temperature sensor 240 may be a standalone temperature sensor 240. Additionally or alternatively, one or more temperature sensors 240 may be included with other components such as an accelerometer and / or a processor (for example, they may be packaged together with the other components).

[0072] The processing module 230-a may acquire and process data from multiple temperature sensors 240 in a manner similar to that described for a single temperature sensor 240. For example, the processing module 230 may individually sample body temperature data from each of the multiple temperature sensors 240, average them, and store them. In other examples, the processing module 230-a may sample the sensors at different rates and average / store different values ​​for different sensors. In some implementations, the processing module 230-a may be configured to determine a single body temperature based on the average of two or more body temperatures determined by two or more temperature sensors 240 located at different positions on the finger.

[0073] The temperature sensor 240 on the ring 104 may acquire distal body temperature at the user's finger (e.g., any finger). For example, one or more temperature sensors 240 on the ring 104 may acquire the user's body temperature from the underside of the finger or at different locations on the finger. In some implementations, the ring 104 may acquire distal body temperature continuously (e.g., at a sampling rate). This specification describes distal temperature measured by the ring 104 on the finger, but other devices may measure body temperature at the same / different locations. In some cases, distal body temperature measured at the user's finger may differ from body temperature measured at the user's wrist or other external body location. In addition, distal body temperature measured at the user's finger (e.g., "shell" temperature) may differ from the user's core body temperature. Thus, the ring 104 may provide a useful body temperature signal that may not be acquired at other internal / external locations on the body. In some cases, continuous temperature measurement at the finger may capture body temperature fluctuations (e.g., small or large fluctuations) that may not be apparent in core body temperature. For example, continuous temperature measurement on the finger can capture minute-by-minute or hourly temperature fluctuations, providing additional insights that other temperature measurements at other locations on the body may not offer.

[0074] The ring 104 may include a PPG system 235. The PPG system 235 may include one or more light transmitters that transmit light. The PPG system 235 may also include one or more light receivers that receive light transmitted by one or more light transmitters. The light receivers may generate a signal (hereinafter referred to as the "PPG" signal) indicating the amount of light received by the light receivers. The light transmitters may illuminate an area of ​​the user's fingers. The PPG signal generated by the PPG system 235 may indicate blood perfusion in the illuminated area. For example, the PPG signal may indicate a change in blood volume in the illuminated area caused by the user's pulse pressure. The processing module 230-a may sample the PPG signal and determine the user's pulse waveform based on the PPG signal. Based on the user's pulse waveform, the processing module 230-a may determine various physiological parameters, such as the user's respiratory rate, heart rate, HRV, oxygen saturation, and other circulatory parameters.

[0075] In some implementations, the PPG system 235 may be configured as a reflective PPG system 235, in which case the optical receiver receives transmitted light reflected through the user's finger area. In some implementations, the PPG system 235 may be configured as a transmissive PPG system 235, in which the optical transmitter and optical receiver are positioned facing each other so that light is transmitted directly to the optical receiver through a portion of the user's finger.

[0076] The number and ratio of transmitters and receivers included in the PPG system 235 may vary. An exemplary optical transmitter may include a light-emitting diode (LED). The optical transmitter may transmit light in the infrared spectrum and / or other spectra. An exemplary optical receiver may include, but is 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 positions of the transmitters and receivers may vary. In addition, a single device may include reflective and / or transmissive PPG systems 235.

[0077] The PPG system 235 shown in Figure 2 may include a reflective PPG system 235 in some implementations. In these implementations, the PPG system 235 may include a centrally located optical receiver (e.g., at the bottom of ring 104) and two optical transmitters located on either side of the optical receiver. In this implementation, the PPG system 235 (e.g., the optical receiver) may generate a PPG signal based on light received from one or both of the optical transmitters. Other implementations may involve other arrangements, combinations, and / or configurations of one or more optical transmitters and / or optical receivers.

[0078] The 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, the processing module 230-a may cause the optical transmitter with a stronger received signal to transmit light while the PPG signal generated by the optical receiver is being sampled. For example, a selected optical transmitter may continuously emit light while the PPG signal is being sampled at a sampling rate (e.g., 250 Hz).

[0079] By sampling the PPG signal generated by the PPG system 235, a pulse waveform, which may be called a "PPG," can be obtained. The pulse waveform may represent blood pressure versus time for multiple cardiac cycles. The pulse waveform may include peaks indicating cardiac cycles. In addition, the pulse waveform may include respiratory-induced variation, which can be used to determine the respiratory rate. In some implementations, the processing module 230-a may store the pulse waveform in memory 215. The processing module 230-a may determine the user's physiological parameters as described herein by processing the pulse waveform when it is generated and / or from memory 215.

[0080] The processing module 230-a may determine the user's heart rate based on the pulse waveform. For example, the processing module 230-a may determine the heart rate (e.g., beats per minute) based on the time between peaks in the pulse waveform. The time between peaks is sometimes called the interbeat interval (IBI). The processing module 230-a may store the determined heart rate value and IBI value in the memory 215.

[0081] The processing module 230-a may determine the HRV over time. For example, the processing module 230-a may determine the HRV based on fluctuations in IBl. The processing module 230-a may store the HRV values ​​over time in memory 215. Furthermore, the processing module 230-a may determine the user's respiratory rate over time. For example, the processing module 230-a may determine the respiratory 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 as breaths per minute, or as another respiratory rate (e.g., breaths per 30 seconds). The processing module 230-a may store the user's respiratory rate values ​​over time in memory 215.

[0082] The ring 104 may include one or more motion sensors 245, such as one or more accelerometers (e.g., 6-D accelerometers) and / or one or more gyroscopes (gyro). The motion sensors 245 may generate motion signals indicating the motion of the sensors. For example, the ring 104 may include one or more accelerometers that generate acceleration signals indicating the acceleration of the accelerometers. As another example, the ring 104 may include one or more gyro sensors that generate gyro signals indicating angular motion (e.g., angular velocity) and / or changes in orientation. The motion sensors 245 may be contained in one or more sensor packages. An exemplary accelerometer / gyro sensor is the Bosch® BMI160 micro electro-mechanical system (MEMS) sensor, which can measure angular velocity and acceleration in three vertical axes.

[0083] The processing module 230-a may sample motion signals at a sampling rate (e.g., 50 Hz) and determine the motion of the ring 104 based on the sampled motion signals. For example, the processing module 230-a may sample acceleration signals to determine the acceleration of the ring 104. As another example, the processing module 230-a may sample gyro signals to determine angular motion. In some implementations, the processing module 230-a may store motion data in memory 215. The motion data may include sampled motion data, as well as motion data calculated based on the sampled motion signals (e.g., acceleration and angular values).

[0084] Ring 104 can store various types of data as described herein. For example, ring 104 may store body temperature data such as raw sampled body temperature data and calculated body temperature data (e.g., mean temperature). As another example, ring 104 may store PPG signal data such as pulse waveforms and data calculated based on pulse waveforms (e.g., heart rate values, IBI values, HRV values, and respiratory rate values). Ring 104 may also store motion data such as sampled motion data showing linear and angular motion.

[0085] The ring 104 or other computing device may calculate and store additional values ​​based on sampled / calculated physiological data. For example, the processing module 230 may calculate and store various metrics such as sleep metrics (e.g., sleep score), activity metrics, and readiness metrics. In some implementations, these additional values / metrics may be called “derived values.” The ring 104 or other computing / wearable device may calculate various values / metrics related to exercise. Exemplary derived values ​​of exercise data may include, but are not limited to, exercise count values, regularity values, intensity values, metabolic equivalence of task values ​​(METs), and orientation values. Exercise count, regularity values, intensity values, and METs may indicate the amount of user exercise over time (e.g., velocity / acceleration). Orientation values ​​may indicate how the ring 104 is oriented relative to the user’s finger and whether the ring 104 is worn on the left or right hand.

[0086] In some implementations, motion counts and regularity values ​​may be determined by counting the number of acceleration peaks within one or more periods (e.g., one or more periods ranging from 30 seconds to 1 minute). Intensity values ​​may indicate the number of motions and the associated intensity (e.g., acceleration value) of the motions. Intensity values ​​may be classified into low, medium, and high depending on the associated threshold acceleration value. METs may be determined based on the intensity of motions during a period (e.g., 30 seconds), the regularity / irregularity of the motions, and the number of motions associated with different intensities.

[0087] In some implementations, the processing module 230-a may compress the data stored in memory 215. For example, the processing module 230-a may delete sampled data after performing calculations based on it. As another example, the processing module 230-a may average the data over a longer period to reduce the number of values ​​stored. In a specific example, if the average body temperature of a user over one minute is stored in memory 215, the processing module 230-a may calculate the average body temperature over five minutes for storage and then erase the average body temperature data for that one minute. The processing module 230-a may compress the data based on various factors such as the total amount of memory 215 used / available and / or the elapsed time since ring 104 last sent data to user device 106.

[0088] The user's physiological parameters may be measured by sensors included in the ring 104, but other devices may also measure the user's physiological parameters. For example, the user's body temperature may be measured by the temperature sensor 240 included in the ring 104, but other devices may also measure the user's body temperature. In some examples, other wearable devices (e.g., wrist devices) may include sensors that measure the user's physiological parameters. In addition, medical devices such as external medical devices (e.g., wearable medical devices) and / or implantable medical devices may measure the user's physiological parameters. The techniques described herein may be implemented using one or more sensors on any type of computing device.

[0089] Physiological measurements may be performed continuously throughout the day and / or night. In some implementations, physiological measurements may be performed between the daytime and / or nighttime portions of 104. In some implementations, physiological measurements may be performed in response to the 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 may perform physiological measurements in the resting / sleeping state to obtain a cleaner physiological signal. In one example, ring 104 or another device / system may detect when the user is resting and / or sleeping and obtain physiological parameters (e.g., body temperature) of the detected state. The device / system may use the resting / sleeping physiological data and / or other data when the user is in other states in order to implement the technology of this disclosure.

[0090] In some implementations, as described herein, the ring 104 may be configured to collect, store and / or process data, and may transfer any of the data described herein to the user device 106 for storage and / or processing. In some embodiments, the user device 106 includes a wearable application 250, an operating system (OS), a web browser application (e.g., a web browser 280), one or more additional applications, and a GUI 275. The user device 106 may further include other modules and components, including sensors, audio devices, haptic feedback devices, etc. The wearable application 250 may include examples of applications (e.g., “app”) that can be installed on the user device 106. The wearable application 250 may be configured to acquire data from the ring 104, store the acquired data, and process the acquired data, as described herein. For example, a wearable application 250 may include a user interface (UI) module 255, an acquisition module 260, a processing module 230-b, a communication module 220-b, and a storage module (e.g., a database 265) configured to store application data.

[0091] The various data processing operations described herein may be performed by the ring 104, the user device 106, the server 110, or any combination thereof. For example, in some cases, data collected by the ring 104 may be preprocessed and sent to the user device 106. In this example, the user device 106 may perform some data processing operation on the received data, send the data to the server 110 for data processing, or both. For example, in some cases, the user device 106 may perform processing operations that require relatively low processing power and / or operations that require relatively low latency, while the user device 106 may send data to the server 110 for processing processing operations that require relatively high processing power and / or operations that can tolerate relatively high latency.

[0092] In some embodiments, the ring 104, user device 106, and server 110 of system 200 may be configured to evaluate the user's sleep patterns. In particular, each component of system 200 may be used to collect data from the user via the ring 104 and to generate one or more scores (e.g., sleep score, readiness score) for the user based on the collected data. For example, as described herein, the ring 104 of system 200 may be worn by the user and may collect data from the user including body temperature, heart rate, HRV, etc. The data collected by the ring 104 may be used to evaluate the user's sleep for a given "sleep day" and to determine when the user is asleep. In some embodiments, the score may be calculated for each sleep day for the user, such that the first sleep day is associated with a first score set and the second sleep day is associated with a second score set. The score may be calculated for each sleep day based on the data collected by the ring 104 during each sleep day. The score may include, but is not limited to, a sleep score, readiness score, etc.

[0093] In some cases, a “sleep day” may be aligned with a traditional calendar day, such that a given sleep day lasts from midnight to midnight on each calendar day. In other cases, a sleep day may be offset relative to a calendar day. 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 PM may act as a “cutoff time,” in which case data collected from the user before 6:00 PM is counted for the current sleep day, and data collected from the user after 6:00 PM is counted for the next sleep day. Due to the fact that most individuals sleep most at night, by offsetting the sleep day relative to the calendar day, system 200 can assess the user’s sleep pattern in a way that matches the user’s sleep schedule. In some cases, the user can selectively adjust the timing of the sleep day relative to the calendar day (e.g., via a GUI) to align the sleep day with the length of time each user typically sleeps.

[0094] In some implementations, each user's overall score for each day (e.g., sleep score, readiness score) may be determined / calculated based on one or more “contributors,” “factors,” or “contributing factors.” 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. The sleep score may include any number of contributing factors. The “total sleep” contributing factor may refer to the sum of all sleep periods in a sleep day. The “efficiency” contributing factor may reflect the percentage of time spent sleeping compared to the time spent awake while in bed, and may be calculated using the efficiency average of the longer sleep periods in a sleep day (e.g., primary sleep period), weighted by the duration of each sleep period. The “rest” contributing factor may indicate how restful the user’s sleep is, and may be calculated using the average of all sleep periods in a sleep day, weighted by the duration of each period. Rest-contributing factors can be based on "wake-up count" (e.g., the sum of all wake-ups detected during different sleep periods (when the user woke up)), excessive movement, and "got-up count" (e.g., the sum of all got-ups detected during different sleep periods (when the user got out of bed)).

[0095] The "REM sleep" contributor may refer to the sum of REM sleep durations across all sleep periods in a sleep day that include REM sleep. Similarly, the "deep sleep" contributor may refer to the sum of deep sleep durations across all sleep periods in a sleep day that include deep sleep. The "latency" contributor may mean the time it takes for a user to fall asleep (e.g., mean, median, longest) and may be calculated using the average of the longest sleep periods throughout the sleep day, weighted by the duration of each period and the number of such periods (e.g., a given integration of sleep stages may be a contributor itself or may weight other contributors). Finally, the "timing" contributor may refer to the relative timing of sleep periods within a sleep day and / or calendar day and may be calculated using the average of all sleep periods in a sleep day, weighted by the duration of each period.

[0096] As another example, a user's overall readiness score may be calculated 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. The readiness score may include any number of contributing factors. The “sleep” contributing factor may refer to the combined sleep score for all sleep periods within a sleep day. The “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 the user whether the sleep they have received over a period of time (e.g., the past two weeks) is balanced with their needs. Typically, adults need 7-9 hours of sleep per night to be healthy, alert, and perform at their best mentally and physically. However, it is normal to have nights when you don't sleep well, so the sleep balance contributing factor takes long-term sleep patterns into account to determine whether each user's sleep needs are being met. The "resting heart rate" contributing factor may represent the lowest heart rate from the longest sleep period of the sleep day (e.g., the main sleep period) and / or the lowest heart rate from a nap that occurs after the main sleep period.

[0097] Continuing in relation to the “contributing factors” (e.g., factors, contributing factors) of the readiness score, the “HRV balance” contributing factor may represent the highest mean HRV from the main sleep period and naps occurring after the main sleep period. The HRV balance contributing factor can help users track their recovery status by comparing the user’s HRV trend over a first period (e.g., two weeks) to the mean HRV over some longer second period (e.g., three months). The “recovery index” contributing factor may be calculated based on the longest sleep period. The recovery index measures the time it takes for the 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 in the first half of the night, at least six hours before the user wakes up, leaving time for the body to recover for the next day. The “body temperature” contributing factor may be calculated based on the longest sleep period (e.g., the main sleep period), or based on naps occurring after the longest sleep period if the user’s highest body temperature during naps is at least 0.5°C higher than the highest body temperature during the longest period. In some embodiments, the ring may measure the user's body temperature while the user is sleeping, and the system 200 may display the user's average body temperature relative to the user's baseline body temperature. If the user's body temperature is outside the normal range (e.g., obviously above or below 0.0), the body temperature contributing factor may be highlighted (e.g., enter a "Pay attention" state) or an alert may be generated to the user in other ways.

[0098] In some embodiments, the system 200 may support techniques for pregnancy detection. In particular, each component of the system 200 may be used to detect indicators of pregnancy in a time series representing the user's body temperature over time. The user's pregnancy may be predicted by utilizing the temperature sensor on the ring 104 of the system 200. In some cases, pregnancy may be detected by identifying one or more morphological features, such as an increase in body temperature in a time series representing the user's body temperature over time, and detecting an indicator of pregnancy corresponding to the temperature increase in the time series. An early pregnancy indicator may be an example of detecting that the user is currently pregnant and / or already pregnant before the user's hormonal changes (e.g., increases) become detectable (e.g., via conventional home pregnancy tests).

[0099] For example, as described herein, the ring 104 of system 200 may be worn by a user and may collect data from the user, including temperature, heart rate, respiratory data, HRV data, etc. The ring 104 of system 200 may collect physiological data from the user based on temperature sensors and measurements extracted from arterial blood flow (e.g., using PPG signals). Physiological data may be collected continuously. In some implementations, the processing module 230-a may continuously sample the user's body temperature throughout the day and night. Sampling at a sufficient rate (e.g., one sample per minute) throughout the day and / or night may provide sufficient body temperature data for the analysis described herein. In some implementations, the ring 104 may continuously acquire body temperature data (e.g., at a sampling rate). In some examples, even if temperature is collected continuously, system 200 may use other information about the user collected by system 200 or otherwise derived (e.g., sleep stage, activity level, disease onset, etc.) to select a representative body temperature for a particular day that is an accurate representation of the underlying physiological phenomena.

[0100] In contrast, systems that require users to manually measure their body temperature daily, and / or systems that continuously measure body temperature but lack other contextual information about the user, may select inaccurate or inconsistent temperature values ​​for pregnancy detection, leading to inaccurate detection and a reduced user experience. In contrast, the data collected by ring 104 can be used to accurately determine when a user is pregnant. Detected early pregnancy and related technologies are further illustrated and explained in relation to Figure 3.

[0101] Figure 3 shows an example of a system 300 that supports pregnancy detection from wearable-based physiological data according to an aspect of the present disclosure. System 300 may implement system 100, system 200, or both, or may be implemented by both. In particular, system 300 shows an example of a ring 104 (e.g., wearable device 104), a user device 106, and a server 110, as described with reference to Figure 1.

[0102] The ring 305 may acquire body temperature data 320, heart rate data 325, respiratory rate data 330, and HRV data 335, among other forms of physiological data as described herein. In such cases, the ring 305 may transmit the body temperature data 320, heart rate data 325, respiratory rate data 330, and HRV data 335 to the user device 310. The body temperature data 320 may include continuous nighttime body temperature data, continuous daytime body temperature data, or both. The respiratory rate data 330 may be an example of continuous nighttime respiratory rate data. 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 acquire body temperature data 320, heart rate data 325, respiratory rate data 330, HRV data 335, or a combination thereof.

[0103] For example, ring 305 may acquire user physiological data such as user body temperature data 320, respiratory rate data 330, heart rate data 325, HRV data 335, galvanic skin response, blood oxygen saturation, actigraphy, and / or other user physiological data. For example, ring 305 may acquire raw data and convert the raw data into daily granularity features. Some implementations may use input data of different granularities. Ring 305 may send the data to another computing device, such as a mobile device (e.g., user device 310), for further processing.

[0104] In some examples, data features for processing may include body temperature detected during sleep, calculated by calculating the maximum body temperature over each half-hour of sleep after cleaning and artifact removal, calculating a second maximum from that series of 30-minute maximums, calculating a delta from the previous day's value, and optionally overlaying a weighted 3-day smoothing. In some implementations, system 300 may alternatively use body temperature deviations without a smoothing window, or this information may be statistical quantiles (e.g., 1% and 99%) or the raw second maximum and minimum values ​​of body temperature after outlier removal, or a combination thereof. In some implementations, system 300 may add additional features such as heart rate and respiratory rate. In some implementations, a multivariate time series method such as a shapelet classifier may identify the shape of pregnancy-related increases in body temperature and simultaneous or phase-independent correlated changes in other signals.

[0105] System 300 may input user data and / or feature sets (e.g., the last few months of data) into a processing pipeline. The pipeline may smooth the data (e.g., using a 7-day smoothing window or other windows). Missing values ​​may be imputed (e.g., using the forecaster Impute method from the python package sktime). The system may derive features from a multivariate matrix that can detect either cessation and / or absence of periodicity, in combination with changes or increases in sleep skin temperature values ​​against a previous within-user distribution of body temperature values ​​over the relevant baseline period. In some cases, System 300 may detect that the lowest body temperature level (the lowest or trough level of the menstrual cycle) may not reach a threshold indicating that a menstrual cycle has started. The threshold may be defined based on within-user data (e.g., when the user previously showed a cycle and it stopped) or between-user data (e.g., a statistical threshold of body temperature distribution derived from users of similar age and with similar signal characteristics). In some implementations, the system 300 may detect a drop in body temperature from the user's in-peak value as the start of a new menstrual cycle. The system may flag the observation as potentially pregnant if the user shows cessation or absence of menstruation after previously recording sufficiently regular menstruation over several cycles, as illustrated with reference to Figure 5.

[0106] For example, the user device 310 may determine pregnancy tracking data (e.g., early pregnancy detection) based on the received data. In some cases, the system 300 may determine pregnancy tracking data based on body temperature data 320, respiratory rate data 330, heart rate data 325, HRV data 335, galvanic skin response, blood oxygen saturation, activity, sleep structure, or a combination thereof. In some cases, the system 300 may determine which features are useful identifiers for early pregnancy detection. The system may be implemented by the ring 305 and the user device 310, but any combination of computing devices described herein may implement features attributed to the system 300.

[0107] The user device 310-a may include a ring application 350. The ring application 350 may include at least a module 340 and application data 345. In some cases, the application data 345 may include the user's historical body temperature pattern and other data. The other data may include body temperature data 320, heart rate data 325, respiratory rate data 330, HRV data 335, or a combination thereof.

[0108] The ring application 350 may present the detected pregnancy indicators to the user. The ring application 350 may include an application data processing module that can perform data processing. For example, the application data processing module may include a module 340 that provides functions belonging to the system 300. An exemplary module 340 may include a daily body temperature determination module, a time series processing module, a body temperature rise module, and a pregnancy detection module.

[0109] A daily temperature determination module can determine the daily temperature value (for example, by selecting a representative temperature value for the day from a series of temperature values ​​collected continuously throughout the day and / or night). A time series processing module can process time series data to detect indicators of pregnancy. A temperature rise module can identify an increase in temperature relative to the user's temperature baseline based on the processed time series data. A pregnancy detection module can detect indicators of pregnancy based on the processed time series data. In such cases, system 300 can receive the user's physiological data (for example, from ring 305) and output a daily classification of whether the user is pregnant. Ring application 350 may store application data 345 such as acquired temperature data, other physiological data, and pregnancy tracking data (e.g., event data).

[0110] In some cases, system 300 may generate pregnancy tracking data based on the user's physiological data (e.g., body temperature data 320 and / or exercise data). The pregnancy tracking data may include indicators of the user's pregnancy, which may be determined based on user body temperature data (e.g., daily body temperature data 320) acquired over an analysis period (e.g., several weeks / several months). For example, system 300 may receive physiological data associated with the user from a wearable device (e.g., a ring 305). The physiological data may include at least body temperature data 320, heart rate data 325, respiratory rate data 330, HRV data 335, or a combination thereof. For example, system 300 may acquire the user's physiological data over an analysis period (e.g., several days). In such a case, system 300 may acquire and process the user's physiological data over the analysis period to generate one or more time series of the user's physiological data.

[0111] In some cases, system 300 may acquire daily user body temperature data 320 over the 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 at night, process the acquired body temperature values, and determine a single daily body temperature value. In some implementations, system 300 may determine a time series of multiple body temperature values ​​acquired over several days based on the received body temperature data 320. System 300 may detect indicators of pregnancy in the time series of body temperature values ​​based on identified temperature increases in the time series of body temperature values, as further illustrated with reference to Figure 4.

[0112] System 300 may cause the GUI of user devices 310-a, 310-b to display indicators of detected pregnancy. Optionally, System 300 may also display a time series in the GUI. System 300 may generate pregnancy tracking data output; for example, System 300 may generate a tracking GUI that includes physiological data (e.g., at least body temperature data 320), tagged events, and / or other GUI elements as described herein with reference to Figure 7. In such a case, System 300 may render indicators of detected pregnancy in the pregnancy tracking GUI.

[0113] System 300 may generate a message 365 indicating an indicator of pregnancy for display on a GUI on user device 310-a or 310-b. For example, system 300 (e.g., user device 310-a or server 315) may send a message 365 indicating a detected indicator of pregnancy to user device 310-b. In such a case, user device 310-b may be associated with a clinician, fertility specialist, caregiver, partner, or a combination thereof. Detection of a possible pregnancy may trigger a personalized message 365 to the user that highlights patterns detected in the body temperature data 320 and provides educational links related to pregnancy.

[0114] In some implementations, the ring application 350 may notify the user of detected pregnancy indicators and / or prompt the user to perform various tasks in the activity GUI. Notifications and prompts may include text, graphics, and / or other user interface elements. Notifications and prompts may be included in the ring application 350, for example, when a pregnancy has just been detected, and the ring application 350 may display notifications and prompts. The user device 310 may display notifications and prompts in a separate window on the home screen and / or overlay them on another screen (for example, at the top of the home screen). In some cases, the user device 310 may display notifications and prompts on a mobile device, the user's watch device, or both.

[0115] In some implementations, the user device 310 may store historical user data. In some cases, the historical user data may include historical data 355. The historical data 355 may include the user's historical body temperature patterns, historical heart rate patterns, historical respiratory rate patterns, historical HRV patterns, historical menstrual cycle start events (e.g., cycle length, cycle start date, etc.) or a combination thereof. The historical data 355 may be selected from the most recent few months. The historical data 355 may be used (e.g., by the user device 310 or server 315) to determine the user's threshold (e.g., non-pregnancy baseline), to detect early pregnancy in the user, or a combination thereof. By using the historical data 355, the user device 310 and / or server 315 can personalize the GUI by taking the user's historical data 355 into consideration.

[0116] Non-pregnancy baselines (e.g., body temperature, heart rate, respiratory rate, HRV, etc.) may be adjusted specifically for the user based on historical data 355 acquired by the system 300. For example, the user's non-pregnancy baseline may be based on physiological data continuously collected by the system 300 before the user becomes pregnant. In such cases, the system 300 may determine the non-pregnancy baseline for the user (e.g., body temperature, heart rate, HRV, respiratory rate). In some cases, the non-pregnancy baseline may be relative to the user's menstrual cycle. For example, the user's baseline may be based on physiological data continuously collected by the system 300 before the user becomes pregnant and / or during the user's menstrual cycle. In such cases, the system 300 may determine the baseline for the user (e.g., body temperature, heart rate, HRV, respiratory rate) based on the values ​​of physiological data determined between different parts of the user's menstrual cycle.

[0117] In such cases, the user device 310 may send 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 cases, the history data 355 may be different from 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 the server data 360.

[0118] In some implementations, the user device 310 and / or the server 315 may also receive and store other data, which 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 features to identify ovulatory and anovulatory cycles. Server data 360 may include other data, such as user information.

[0119] In some implementations, system 300 may include one or more user devices 310 for different users. For example, system 300 may include user device 310-a for the primary user and user device 310-b for a second user 302 (e.g., a partner) associated with the primary user. User devices 310 may measure the physiological parameters of different users, provide GUIs for different users, and receive user input from different users. In some implementations, different user devices 310 may acquire physiological information and provide outputs related to women's health, such as menstrual cycle, ovarian cycle, disease, fertility, and / or pregnancy. In some implementations, user device 310-b may acquire physiological information related to the second user 302, such as male disease and fertility.

[0120] In some implementations, system 300 may provide a GUI to inform the second user 302 of relevant information. For example, the first user and the second user 302 may share their information with each other via one or more user devices 310, such as a server device, mobile device, or other device. In some implementations, the second user 302 may share one or more of the user's account (e.g., username, login information, etc.) and / or related data with each other (e.g., the first user). By sharing information between users, system 300 may help the second user 302 make health decisions related to pregnancy. In some implementations, the user may be prompted (e.g., in the GUI) to share certain information. For example, the user may choose to share her pregnancy information with the second user 302 using the GUI. In such a case, the user and the second user 302 may receive notifications related to the stage of pregnancy on their respective user devices 310. In other examples, a second user 302 may make their information (e.g., disease, fertility data, etc.) available to the user through notifications or other sharing arrangements. In such cases, the second user 302 may be a clinician, fertility specialist, caregiver, partner, or a combination thereof.

[0121] Figure 4 shows an example of a timing diagram 400 that supports pregnancy detection from wearable-based physiological data according to an aspect of this disclosure. The timing diagram 400 may implement aspects of system 100, system 200, system 300, or a combination thereof, or be implemented by them. For example, in some implementations, the timing diagram 400 may be displayed to the user via the GUI 275 of the user device 106, as shown in Figure 2.

[0122] As will be described in more detail herein, the system may be configured to detect indicators of pregnancy. In some cases, the user's body temperature pattern throughout the day and night may be an indicator that can characterize pregnancy. For example, daytime and nighttime skin temperature may identify indicators of pregnancy. Thus, the timing diagram 400 illustrates the relationship between the user's body temperature data and time (e.g., over multiple days and / or months). In this regard, the vertical bars shown in the timing diagram 400 may be understood to refer to “body temperature values ​​405”. The dark vertical bars shown in the timing diagram 400 may be understood to refer to “confirmed pregnancy 410”. The user's body temperature values ​​405 may be relative to the baseline body temperature.

[0123] In some cases, the system (e.g., ring 104, user device 106, server 110) may receive physiological data associated with the user from the wearable device. The physiological data may include at least body temperature data. Based on the received body temperature data, the system may determine a time series of multiple body temperature values ​​405 acquired over several days. The system may process the original time series body temperature data (e.g., body temperature values ​​405) to detect pregnancy indicators 415.

[0124] Body temperature values ​​of 405 can be continuously collected by a wearable device. Physiological measurements may be taken continuously throughout the day and / or night. For example, in some implementations, the ring may be configured to continuously acquire physiological data (e.g., body temperature data, sleep data, heart rate data, HRV data, respiratory rate data, MET data, etc.) according to one or more measurement cycles throughout the day / sleep cycle. In other words, the ring can continuously acquire physiological data from the user regardless of “trigger conditions” for making such measurements. In some cases, continuous body temperature measurement on the finger may capture body temperature fluctuations (e.g., small or large fluctuations) that may not be apparent in core body temperature. For example, continuous body temperature measurement on the finger may capture minute-by-minute or hourly body temperature fluctuations that may not be provided by other body temperature measurements elsewhere on the body, or if the user were manually measuring their temperature once a day.

[0125] In some implementations, the system may detect the indicator of pregnancy 415 by observing the user's relative body temperature for several days and marking temperature increases that may indicate pregnancy. Distinguishing phases of the menstrual cycle using individualized continuous physiology to detect the indicator of pregnancy 415 may provide accurate pregnancy detection. In such cases, the system may detect the indicator of pregnancy 415 based on identifying a temperature increase at temperature value 405. For example, the indicator of pregnancy 415 may occur before and / or at the time of the temperature increase at temperature value 405. In such cases, the indicator of pregnancy 415 may include a period of time (e.g., one or more days) during which pregnancy was highly likely to occur.

[0126] The system may determine each temperature value 405 in response to receiving temperature data. The temperature data may include continuous nighttime temperature data. Temperature value 405 may be an example of a nocturnal sleeping temperature value (e.g., once a day) obtained by the ring. Temperature value 405 may indicate pregnancy, detected by an increase in temperature relative to the user's non-pregnancy temperature baseline. The user's non-pregnancy temperature baseline may represent temperature value 405 prior to the pregnancy indicator 415. For example, temperature value 405 may be elevated from the user's non-pregnancy temperature baseline, thereby indicating that the user is pregnant.

[0127] Body temperature values ​​405 may be plotted over several months for users who took a positive pregnancy test (e.g., confirmed pregnancy 410) at times separated by vertical lines. A timing diagram 400 that may be included in an application, as described with reference to Figure 7, may show how body temperature data can be used to detect the onset of pregnancy up to two weeks earlier than conventional home testing methods. For example, physiological data obtained from a device (e.g., a ring device) may detect pregnancy several weeks before menstruation stops (e.g., up to two weeks before the first menstruation stops). In such cases, the pregnancy indicator 415 may be detected before confirmed pregnancy 410.

[0128] As will be described in more detail herein, the system may be configured to detect pregnancy before hormone tests confirm it (e.g., before hormonal changes detectable by the user). In some cases, the user's body temperature pattern throughout the night may be an indicator that can characterize pregnancy. For example, nighttime skin temperature may detect an indicator of pregnancy 415. Thus, the timing diagram 400 shows the relationship between the user's body temperature data and time (e.g., over several days).

[0129] Figure 5 shows an example of a timing diagram 500 that supports pregnancy detection from wearable-based physiological data according to an aspect of this disclosure. The timing diagram 500 may implement aspects of system 100, system 200, system 300, or a combination thereof, or may be implemented by them. For example, in some implementations, the timing diagram 500 may be displayed to the user via the GUI 275 of the user device 106, as shown in Figure 2.

[0130] As will be described in more detail herein, the system may be configured to detect indicators of pregnancy 510 based on deviations in body temperature, HRV, respiratory rate, heart rate, or combinations thereof. In some cases, the user's body temperature pattern, HRV pattern, respiratory rate pattern, heart rate pattern, or combinations thereof throughout the day and night may be indicators that characterize pregnancy. For example, daytime and nighttime skin temperature, HRV, respiratory rate, heart rate, or combinations thereof may detect indicators of pregnancy 510.

[0131] Thus, timing diagram 500-a shows the relationship between the user's body temperature data and time (e.g., over multiple months). In this regard, the solid curve shown in timing diagram 500 can be understood as referring to "body temperature value 505". The user's body temperature value 505 may be relative to the baseline body temperature. The dashed vertical line shown in timing diagram 500 can be understood as referring to "indicator of pregnancy 510". The short dashed vertical line shown in timing diagram 500 can be understood as referring to "menstrual period 515".

[0132] In some cases, the system (e.g., ring 104, user device 106, server 110) may receive physiological data associated with the user from the wearable device. The physiological data may include at least body temperature data. Based on the received body temperature data, the system may determine a time series of multiple body temperature values ​​505 acquired over several days. Referring to timing diagram 500-a, the several days may be, for example, seven months. For example, timing diagram 500-a may include at least two menstrual periods 515, an indicator of pregnancy 510, and body temperature values ​​505 throughout the two menstrual periods 515 and at least the first stage of pregnancy (e.g., weeks 0-14). In such a case, timing diagram 500-a may show a user having two menstrual periods 515, an indicator of pregnancy 510, and body temperature values ​​505 throughout at least the subsequent first stage of pregnancy.

[0133] The system may process the original time-series body temperature data (e.g., temperature value 505) to detect an indicator of pregnancy 510. In some cases, the time series may include multiple events tagged by the user within the system. For example, the time series may include menstrual periods 515 that may be tagged by the user. In some cases, menstrual periods 515 may be determined by the system based on physiological data collected sequentially by the system. For example, timing diagram 500-a may show a user who had several menstrual periods 515, which may be identified automatically and / or by user tags within the application. The user is pregnant (indicated, for example, via an indicator of pregnancy 510), and thereafter, menstruation has not returned within nine months of conception, thereby indicating that the user is pregnant. The user's body temperature trajectory around the time of the indicator of pregnancy 510 is generally higher than the peak around the time of menstrual period 515.

[0134] Body temperature values ​​of 50°C can be continuously collected by a wearable device. Physiological measurements may be taken continuously throughout the day and / or night. For example, in some implementations, the ring may be configured to continuously acquire physiological data (e.g., body temperature data, sleep data, MET data, etc.) according to one or more measurement cycles throughout the day / sleep cycle. In other words, the ring can continuously acquire physiological data from the user regardless of “trigger conditions” for making such measurements. In some cases, continuous body temperature measurement on the finger may capture body temperature fluctuations (e.g., small or large fluctuations) that may not be apparent in core body temperature. For example, continuous body temperature measurement on the finger may capture minute-by-minute or hourly body temperature fluctuations that may not be provided by other body temperature measurements elsewhere on the body, or if the user were manually measuring their temperature once a day.

[0135] In some implementations, the system may detect the indicator of pregnancy 510 by observing the user's relative body temperature for several days and marking an increase in temperature relative to a non-pregnancy baseline, a menstrual cycle baseline, or both, which may indicate pregnancy. For example, the system may determine that the received temperature data (e.g., temperature value 505) exceeds the user's non-pregnancy baseline temperature for at least some of several days. In such cases, the system may detect the indicator of pregnancy 510 in response to its determination that the received temperature data exceeds the user's non-pregnancy baseline temperature. For example, the system may identify that the user's temperature has risen by approximately 0.4°C above the baseline (e.g., non-pregnancy baseline, menstrual cycle baseline, or both) and lasts much longer than the temperature rise in the user's previous luteal phase (e.g., before the start of the next menstrual period 515). In some examples, the system may identify the temperature value 505 after determining the time series and identify the non-pregnancy baseline for the temperature value.

[0136] In some cases, the system may identify and / or determine a menstrual cycle baseline for physiological parameters. The menstrual cycle baseline may be an example of a trend showing how physiological parameters typically change for a user throughout their menstrual cycle, based on the received physiological data. For example, a menstrual cycle baseline for a user's body temperature may include typical daily or phase-specific body temperature values ​​for the user's menstrual cycle. Based on the menstrual cycle baseline, the system may compare the received body temperature data to the predicted body temperature for a given day in the user's menstrual cycle. The system may determine that the received body temperature data (e.g., a body temperature value of 505) is higher than the user's menstrual cycle baseline for an identified day in the user's menstrual cycle. In such a case, the system may detect a pregnancy indicator 510 in response to determining that the received body temperature data is higher than the user's baseline body temperature for their menstrual cycle.

[0137] In some cases, the system may determine that the received temperature data (e.g., a temperature value of 505) has risen earlier in the cycle or at a faster rate than is typical based on the user's menstrual cycle baseline. For example, the system may determine that the received temperature data (e.g., a temperature value of 505) is higher than the user's typical value for this day or phase of the menstrual cycle based on the menstrual cycle baseline, which may indicate that the temperature rise is something other than the normal fluctuations experienced during the menstrual cycle (e.g., the user may be pregnant). In such cases, the system may detect a pregnancy indicator 510 in response to determining that the received temperature data has risen earlier than the user's baseline temperature for the menstrual cycle.

[0138] The system may detect pregnancy in a time series of body temperature values ​​505 based on one or more positive slopes in the time series of body temperature values ​​505. For example, after determining the time series, the system may identify one or more positive slopes in multiple time series of body temperature values ​​505. In response to identifying one or more positive slopes in the time series, the system may detect pregnancy in the time series of body temperature values ​​505. The pregnancy indicator 510 is associated with the positive slope in the time series of body temperature values ​​505. For example, the pregnancy indicator 510 may occur at the end of a positive slope. In such cases, the positive slope may indicate that pregnancy has occurred.

[0139] In some cases, the system may determine or estimate the maximum and / or minimum values ​​of the user's body temperature after determining the time series of the user's body temperature values ​​505 collected via the ring. Based on having determined the maximum and / or minimum values, the system may identify one or more positive slopes of the time series of multiple body temperature values ​​505. In some cases, the 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 the time series of multiple body temperature values ​​505 may be in response to calculating the derivative of the original time series body temperature data (e.g., body temperature values ​​505).

[0140] In some implementations, the system may, in response to determining a time series, identify a halt in the periodicity of the time series of body temperature values ​​at 505. In such cases, the system may, in response to identifying the halt in periodicity, detect pregnancy. For example, the system may determine that the body temperature value at 505 may deviate from the periodicity of the time series of body temperature values ​​during a menstrual cycle (e.g., menstrual period 515). In such cases, the system may determine that the body temperature value at 505 will continue to rise rather than decrease after the user experiences menstrual period 515.

[0141] As will be described in more detail herein, the system may be configured to track the menstrual cycle, ovulation, pregnancy, etc. In some cases, the user's body temperature pattern throughout the night may serve as an indicator of pregnancy. For example, nighttime skin temperature may identify an indicator of pregnancy. Thus, timing diagram 500-a shows the relationship between the user's body temperature data and time (e.g., over several months).

[0142] Timing Chart 500-b shows the relationship between the user's HRV data and time (e.g., across multiple months). In this regard, the solid curve shown in Timing Chart 500-b may be understood to represent "HRV 520". The user's HRV 520 may be relative to the baseline HRV. The dashed vertical line shown in Timing Chart 500-b may be understood to represent "Pregnancy Indicator 510". The short dashed vertical line shown in Timing Chart 500-b may be understood to represent "Menstrual Period 515".

[0143] In some cases, the system (e.g., ring 104, user device 106, server 110) may receive physiological data associated with the user from the wearable device. The physiological data may include at least HRV data. Based on the received HRV data, the system may determine a time series of multiple HRV values ​​520 acquired over several days. Referring to timing diagram 500-b, the several days may be, for example, seven months. For example, timing diagram 500-b may include at least two menstrual periods 515, a pregnancy indicator 510, and HRV values ​​520 throughout the two menstrual periods 515 and at least the first stage of pregnancy (e.g., weeks 0-14). In such a case, timing diagram 500-b may show a user having two menstrual periods 515, a pregnancy indicator 510, and HRV values ​​520 throughout at least the first stage of pregnancy.

[0144] The system may process the original time-series HRV data (e.g., HRV value 520) to detect the pregnancy indicator 510. In some cases, the time series may include multiple events tagged by the user within the system. For example, the time series may include menstrual periods 515 that may be tagged by the user. In some cases, menstrual periods 515 may be determined by the system based on physiological data collected sequentially by the system. For example, timing diagram 500-b may show a user who had several menstrual periods 515, which may be identified automatically and / or by user tags within the application. The user is pregnant (indicated, for example, via the pregnancy indicator 510), and thereafter, the menstrual period has not returned within nine months of conception, thereby indicating that the user is pregnant. The user's HRV trajectory around the time of the pregnancy indicator 510 may generally be lower than the peak around the time of menstrual period 515.

[0145] HRV values ​​of 520 can be continuously collected by a wearable device. Physiological measurements may be taken continuously throughout the day and / or night. For example, in some implementations, the ring may be configured to continuously acquire physiological data (e.g., HRV data, sleep data, MET data, etc.) according to one or more measurement cycles throughout each day / sleep day. In other words, the ring can continuously acquire physiological data from the user regardless of any "trigger conditions" for performing such measurements.

[0146] In some implementations, the system may detect an indicator of pregnancy by observing the user's relative HRV over several days and marking a decrease in HRV relative to a non-pregnancy baseline, menstrual cycle baseline, or both, which may indicate pregnancy. For example, the system may determine that the received HRV data (e.g., HRV value 520) is below the user's non-pregnancy baseline HRV for at least some of several days. In such cases, detecting an indicator of pregnancy may be in response to the determination that the received HRV data is below the user's non-pregnancy baseline HRV. For example, the system may identify that the user's HRV has decreased below the baseline (e.g., non-pregnancy baseline, menstrual cycle baseline, or both) and that this decrease lasts much longer than the user's previous luteal phase HRV decrease. In some examples, the system may identify the HRV value 520 after determining the time series and identify the non-pregnancy baseline HRV value.

[0147] In some cases, the system may determine a menstrual cycle baseline for HRV. The menstrual cycle baseline for HRV may be an example of an HRV trend, showing how the user's HRV typically changes throughout their menstrual cycle, based on the received physiological data. Based on the menstrual cycle baseline, the system may compare the received HRV data to the predicted HRV for a given day in the user's menstrual cycle. The system may determine that the received HRV data (e.g., an HRV value of 520) is lower than the user's menstrual cycle baseline for an identified day or phase of the user's menstrual cycle. In such cases, the system may detect a pregnancy indicator 510 in response to determining that the received HRV data is lower than the baseline HRV for the user's menstrual cycle.

[0148] In some cases, the system may determine that the received HRV data (e.g., HRV value 520) is decreasing faster than the user's menstrual cycle baseline. For example, the system may determine that HRV value 520 is decreasing at a faster rate than typical based on the user's menstrual cycle baseline HRV. In such cases, the system may detect a pregnancy indicator 510 in response to its determination that the received HRV data is decreasing faster than the user's menstrual cycle baseline HRV.

[0149] The system may detect pregnancy in a time series of HRV values ​​520 based on one or more negative slopes in the time series of HRV values ​​520. For example, after determining the time series, the system may identify one or more negative slopes in multiple time series of HRV values ​​520. In response to identifying one or more negative slopes in the time series, the system may detect pregnancy in a time series of HRV values ​​520. The pregnancy indicator 510 is associated with a smaller negative slope in the time series of HRV values ​​520 compared to a negative slope in the time series during the menstrual cycle (e.g., menstrual period 515). For example, the pregnancy indicator 510 may occur at the end of a negative slope. In such cases, the negative slope may indicate that pregnancy has occurred.

[0150] In some cases, the system may determine or estimate the maximum and / or minimum values ​​of a user's HRV after determining the time series of the user's HRV values ​​520 collected via the ring. Based on having determined the maximum and / or minimum values, the system may identify one or more negative slopes of multiple HRV value 520 time series. In some cases, the negative slope may be determined by calculating the difference between the maximum and minimum values. In other examples, identifying one or more negative slopes of multiple HRV value 520 time series may be in response to calculating the derivative of the original time series HRV data (e.g., HRV value 520).

[0151] In some implementations, the system may, in response to determining the time series, identify a halt in the periodicity of the HRV value 520 time series. In such cases, the system may detect pregnancy in response to identifying the halt in periodicity. For example, the system may determine that the HRV value 520 may be deviating from the periodicity of the HRV value 520 time series during a menstrual cycle (e.g., menstrual period 515). In such cases, the system may determine that the HRV value 520 will continue to decrease rather than increase after the user experiences menstrual period 515.

[0152] In some cases, a user's HRV pattern throughout the night can serve as an indicator of pregnancy. For example, nighttime HRV may identify an indicator of pregnancy. Thus, timing diagram 500-b shows the relationship between a user's HRV data and time (e.g., across multiple months).

[0153] In some cases, one or more physiological measurements can be combined to detect pregnancy (e.g., identify a pregnancy indicator 510). In such cases, identifying the pregnancy indicator 510 may be based on one physiological measurement or a combination of physiological measurements. For example, a user's HRV pattern combined with the user's body temperature pattern may be an indicator that can characterize pregnancy. In such cases, the user's HRV pattern can be used in conjunction with the user's body temperature data 320 to confirm the pregnancy indicator 510 (e.g., provide a definitive indicator or better prediction of pregnancy). For example, if the system determines that the received heart rate variability data is below the user's non-pregnancy baseline heart rate variability and the received body temperature data 320 is higher than the user's non-pregnancy baseline body temperature, the system can verify or detect the pregnancy indicator 510 with greater accuracy and precision than if either the heart rate variability data or the body temperature data deviated from the non-pregnancy baseline.

[0154] In some cases, a combination of one or more physiological measurements may disprove or reduce the likelihood of a detected pregnancy indicator 510. In such cases, the system may identify a false positive for pregnancy indicator 510 based on one or a combination of physiological measurements. For example, if the system determines that the received body temperature data is higher than the user's non-pregnancy baseline body temperature, but the received heart rate variability data is still consistent with the user's non-pregnancy baseline heart rate variability, the system may determine that pregnancy indicator 510 is invalid or at least less likely than if both body temperature and respiratory variability were deviating from their respective pregnancy baselines. In such cases, the system may determine that the user may be experiencing illness, hormonal changes during the menstrual cycle, etc.

[0155] Timing chart 500-c shows the relationship between the user's respiratory rate data and time (e.g., over multiple months). In this regard, the solid curve shown in timing chart 500-c may be understood to represent "respiratory rate 525". The user's respiratory rate 525 may be relative to the baseline respiratory rate. The dashed vertical line shown in timing chart 500-c may be understood to represent "pregnancy indicator 510". The short dashed vertical line shown in timing chart 500-c may be understood to represent "menstrual period 515".

[0156] In some cases, the system (e.g., ring 104, user device 106, server 110) may receive physiological data associated with the user from the wearable device. The physiological data may include at least respiratory rate data. Based on the received respiratory rate data, the system may determine a time series of multiple respiratory values ​​525 acquired over several days. Referring to timing diagram 500-c, the several days may be, for example, seven months. For example, timing diagram 500-c may include at least two menstrual periods 515, an indicator of pregnancy 510, and respiratory values ​​525 throughout the two menstrual periods 515 and at least the first stage of pregnancy (e.g., weeks 0-14). In such a case, timing diagram 500-c may show a user having two menstrual periods 515, an indicator of pregnancy 510, and respiratory values ​​525 throughout at least the subsequent first stage of pregnancy.

[0157] The system may process the original time-series respiratory rate data (e.g., respiratory rate 525) to detect an indicator of pregnancy 510. In some cases, the time series may include multiple events tagged by the user within the system. For example, the time series may include menstrual periods 515 that may be tagged by the user. In some cases, menstrual periods 515 may be determined by the system based on physiological data collected sequentially by the system. For example, timing diagram 500-c may show a user who had several menstrual periods 515, which may be identified automatically and / or by user tags within the application. The user is pregnant (indicated, for example, via an indicator of pregnancy 510), and thereafter, menstruation has not returned within nine months of conception, thereby indicating that the user is pregnant. The user's respiratory rate trajectory around the time of the indicator of pregnancy 510 may generally be higher than the peak around the time of menstrual period 515.

[0158] A respiratory rate of 525 can be continuously collected by a wearable device. Physiological measurements may be taken continuously throughout the day and / or night. For example, in some implementations, the ring may be configured to continuously acquire physiological data (e.g., respiratory rate data, sleep data, MET data, etc.) according to one or more measurement cycles throughout each day / sleep day. In other words, the ring can continuously acquire physiological data from the user regardless of any “trigger conditions” for performing such measurements.

[0159] In some implementations, the system may detect the indicator of pregnancy 510 by observing the user's relative respiratory rate over several days and marking an increase in respiratory rate relative to a non-pregnancy baseline, menstrual cycle baseline, or both, which may indicate pregnancy. For example, the system may determine that the received respiratory rate data (e.g., respiratory rate 525) is greater than (e.g., exceeds) the user's non-pregnancy baseline respiratory rate for at least some of several days. In such cases, detecting the indicator of pregnancy 510 may be in response to the determination that the received respiratory rate data is greater than the user's non-pregnancy baseline respiratory rate. For example, the system may identify that the user's respiratory rate includes a 30% increase relative to the baseline (e.g., non-pregnancy baseline, menstrual cycle baseline, or both) and lasts much longer than the respiratory rate increase in the user's previous luteal phase. In some examples, the system may identify the respiratory rate 525 after determining the time series and identify the non-pregnancy baseline for the respiratory rate.

[0160] In some cases, the system may determine a menstrual cycle baseline for respiratory rate. The menstrual cycle baseline may be an example of a respiratory rate trend, based on received physiological data, showing how the user's respiratory rate typically changes throughout the user's menstrual cycle. For example, the system may identify the days and phases of the user's menstrual cycle and the corresponding baseline respiratory rate for those days or phases. The system may compare the received respiratory rate data to the predicted respiratory rate for the day of the user's menstrual cycle (e.g., the menstrual cycle baseline). The system may determine that the received respiratory rate data (e.g., respiratory rate 525) is greater than the user's menstrual cycle baseline for the identified day of the user's menstrual cycle. In such a case, the system may detect a pregnancy indicator 510 in response to determining that the received respiratory rate data is greater than the user's baseline respiratory rate for the menstrual cycle.

[0161] In some cases, the system may determine that the received respiratory rate data (e.g., respiratory rate 525) is increasing faster than is typical for the user based on their menstrual cycle baseline respiratory rate. For example, the system may determine that the respiratory rate 525 is increasing at a higher or faster rate than would be predicted based on the user's menstrual cycle baseline. In such cases, the system may detect a pregnancy indicator 510 in response to its determination that the received respiratory rate 525 is increasing faster than the user's menstrual cycle baseline respiratory rate.

[0162] The system may detect pregnancy in a time series of respiratory values ​​525 based on one or more positive slopes in the time series of respiratory values ​​525. For example, after determining the time series, the system may identify one or more positive slopes in multiple time series of respiratory values ​​525. In response to identifying one or more positive slopes in the time series, the system may detect pregnancy in the time series of respiratory values ​​525. The pregnancy indicator 510 is associated with the largest positive slope in the time series of respiratory values ​​525 compared to the positive slope in the time series of respiratory values ​​525 during the menstrual cycle (e.g., menstruation 515). For example, the pregnancy indicator 510 may occur at the end of a positive slope. In such cases, the positive slope may indicate that pregnancy has occurred.

[0163] In some cases, the system may determine or estimate the maximum and / or minimum values ​​of the user's respiratory rate after determining the time series of the user's respiratory rate 525 collected via the ring. Based on having determined the maximum and / or minimum values, the system may identify one or more positive slopes of the time series of multiple respiratory rate 525s. In some cases, the 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 the time series of multiple respiratory rate 525s may be in response to calculating the derivative of the original time series respiratory rate data (e.g., respiratory rate 525).

[0164] In some implementations, the system may, in response to determining a time series, identify a halt in the periodicity of the respiratory rate 525 time series. In such cases, the system may detect pregnancy in response to identifying the halt in periodicity. For example, the system may determine that the respiratory rate 525 may be deviating from the periodicity of the respiratory rate 525 time series during a menstrual cycle (e.g., menstrual period 515). In such cases, the system may determine that the respiratory rate 525 will continue to rise rather than decrease after the user experiences menstrual period 515.

[0165] In some cases, a user's respiratory rate pattern throughout the night can be an indicator that may characterize pregnancy. For example, nighttime respiratory rate may identify indicator 510 of pregnancy. Thus, timing diagram 500-c shows the relationship between user respiratory rate data and time (e.g., over multiple months).

[0166] In some cases, a user's respiratory rate pattern, combined with a user's body temperature pattern (or any other physiological parameter described herein), can be an indicator that may characterize early detection of pregnancy. In some cases, a user's respiratory rate pattern, combined with a user's body temperature pattern and / or HRV pattern, can be an indicator that may characterize pregnancy. In such cases, the user's respiratory rate pattern can be used in conjunction with the user's body temperature data, the user's HRV pattern, or both to confirm an indicator of pregnancy 510 (e.g., to provide a definitive indicator or better prediction of pregnancy). For example, if the system determines that the received respiratory rate data is higher than the user's menstrual cycle baseline respiratory rate and the received body temperature data 320 is higher than the user's menstrual cycle baseline body temperature, the system can verify or detect an indicator of pregnancy 510 with greater accuracy and precision than if either the respiratory rate data or the body temperature data deviated from the menstrual cycle baseline.

[0167] In some cases, the system may identify false positives for the pregnancy indicator 510 based on a single physiological measurement or a combination of physiological measurements. For example, if the system determines that the received body temperature data is higher than the user's baseline body temperature for their menstrual cycle, but the received respiratory rate data still matches the user's baseline respiratory rate for their menstrual cycle, the system may determine that the pregnancy indicator 510 is invalid (e.g., a false positive). In such cases, the system may determine, based on its determination that a single physiological measurement or combination of physiological measurements matches the baseline for the menstrual cycle, that the user may be experiencing illness, hormonal changes during the menstrual cycle, etc.

[0168] Timing diagram 500-d shows the relationship between the user's heart rate data and time (e.g., over multiple months). In this regard, the solid curve shown in timing diagram 500-d can be understood as referring to "heart rate 530". The user's heart rate 530 may be relative to the baseline heart rate. The dashed vertical line shown in timing diagram 500-d can be understood as referring to "indicator of pregnancy 510". The short dashed vertical line shown in timing diagram 500-d can be understood as referring to "menstrual period 515".

[0169] In some cases, the system (e.g., ring 104, user device 106, server 110) may receive physiological data associated with the user from the wearable device. The physiological data may include at least heart rate data. Based on the received heart rate data, the system may determine a time series of multiple heart rate values ​​530 acquired over several days. Referring to timing diagram 500-d, several days may be a 7-month example. For example, timing diagram 500-d may include at least two menstrual periods 515, an indicator of pregnancy 510, and heart rate values ​​530 throughout the two menstrual periods 515 and at least the first stage of pregnancy (e.g., weeks 0-14). In such a case, timing diagram 500-d may show a user having two menstrual periods 515, an indicator of pregnancy 510, and heart rate values ​​530 throughout at least the first stage of pregnancy.

[0170] The system may process the original time-series heart rate data (e.g., heart rate value 530) to detect an indicator of pregnancy 510. In some cases, the time series may include multiple events tagged by the user within the system. For example, the time series may include menstrual periods 515 that may be tagged by the user. In some cases, menstrual periods 515 may be determined by the system based on physiological data collected sequentially by the system. For example, timing diagram 500-d may show a user who had several menstrual periods 515, which may be identified automatically and / or by user tags within the application. The user is pregnant (e.g., indicated via an indicator of pregnancy 510), and thereafter, menstruation has not returned within nine months of conception, thereby indicating that the user is pregnant. The user's heart rate trajectory around the time of the indicator of pregnancy 510 is generally higher than the peak around the time of menstrual period 515.

[0171] A heart rate value of 530 can be continuously collected by a wearable device. Physiological measurements may be taken continuously throughout the day and / or night. For example, in some implementations, the ring may be configured to continuously acquire physiological data (e.g., heart rate data, sleep data, MET data, etc.) according to one or more measurement cycles throughout the day / sleep cycle. In other words, the ring can continuously acquire physiological data from the user regardless of any “trigger conditions” for performing such measurements.

[0172] In some implementations, the system may detect the pregnancy indicator 510 by observing the user's relative heart rate over several days and marking an increase in heart rate relative to a non-pregnancy baseline, a menstrual cycle baseline, or both, which may indicate pregnancy. For example, the system may determine that the received heart rate data (e.g., heart rate value 530) is greater than (e.g., exceeds) the user's non-pregnancy baseline respiratory rate for at least some of several days. In such cases, detecting the pregnancy indicator 510 may be in response to determining that the received heart rate data is below the user's non-pregnancy baseline respiratory rate. For example, the system may identify that the user's heart rate is increasing relative to a baseline (e.g., a non-pregnancy baseline, a menstrual cycle baseline, or both) and that this increase lasts much longer than the user's previous luteal phase heart rate increase. In some examples, the system may identify the heart rate value 530 after determining the time series and identify the non-pregnancy baseline for the heart rate value.

[0173] In some cases, the system may determine a menstrual cycle baseline for heart rate. The menstrual cycle baseline heart rate may be an example of a heart rate trend, based on received physiological data, showing how the user's daily or average heart rate changes throughout the user's menstrual cycle. For example, the system may identify a day or phase of the user's menstrual cycle and identify a corresponding heart rate value typical for that day or phase. The system may compare the received heart rate data to the predicted heart rate for that day of the user's menstrual cycle (e.g., the menstrual cycle baseline). The system may determine that the received heart rate data (e.g., heart rate value 530) is greater than (e.g., exceeds) the user's menstrual cycle baseline for the identified day of the user's menstrual cycle. In such a case, the system may detect a pregnancy indicator 510 in response to determining that the received heart rate data is greater than the user's menstrual cycle baseline heart rate.

[0174] In some cases, the system may determine that the received heart rate data (e.g., heart rate value 530) is increasing earlier than predicted or typical based on the baseline heart rate of the menstrual cycle. For example, the system may determine that the heart rate value 530 is higher than typical or is increasing earlier in the cycle. In such cases, the system may detect a pregnancy indicator 510 in response to its determination that the received heart rate data is increasing earlier than the user's baseline heart rate of the menstrual cycle.

[0175] The system may detect pregnancy in a time series of heart rate values ​​520 based on one or more positive slopes in a time series of heart rate values ​​530. For example, after determining the time series, the system may identify one or more positive slopes in multiple time series of HRV values ​​520. In response to identifying one or more positive slopes in the time series, the system may detect pregnancy in a time series of heart rate values ​​530. The pregnancy indicator 510 is associated with the positive slope in the time series of heart rate values ​​530. For example, the pregnancy indicator 510 may occur at the end of a positive slope. In such cases, the positive slope may indicate that pregnancy has occurred.

[0176] In some cases, the system may determine or estimate the maximum and / or minimum values ​​of the user's heart rate after determining the time series of the user's heart rate values ​​530 collected via the ring. Based on having determined the maximum and / or minimum values, the system may identify one or more positive slopes of the time series of heart rate values ​​530. In some cases, the 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 time series of multiple heart rate values ​​530 may be in response to calculating the derivative of the original time series heart rate data (e.g., heart rate value 530).

[0177] In some implementations, the system may, in response to determining a time series, identify a halt in the periodicity of the time series of heart rate values ​​530. In such cases, the system may, in response to identifying the halt in periodicity, detect pregnancy. For example, the system may determine that the heart rate value 530 may deviate from the periodicity of the time series of heart rate values ​​530 during the menstrual cycle (e.g., menstrual period 515). In such cases, the system may determine that the heart rate value 530 will continue to rise rather than decrease after the user experiences menstrual period 515.

[0178] In some cases, a user's heart rate pattern throughout the day and / or night can serve as an indicator of pregnancy. For example, daytime and / or nighttime heart rates may identify indicators of pregnancy. Thus, timing diagram 500-d shows the relationship between user heart rate data and time (e.g., across multiple months).

[0179] In some cases, a user's heart rate pattern, combined with a user's body temperature pattern (or any other physiological parameter described herein), can serve as an indicator that may characterize early detection of pregnancy. In some cases, a user's heart rate pattern, combined with a user's body temperature pattern, HRV pattern, and / or respiratory rate pattern, can serve as an indicator that may characterize pregnancy. In such cases, the user's heart rate pattern can confirm an indicator of pregnancy 510 (e.g., provide a definitive indicator or better prediction of pregnancy) in light of the user's body temperature data, user's HRV pattern, user's respiratory rate pattern, or a combination thereof. For example, if the system identifies a cessation of the periodicity in the time series of the heart rate value 530 and a cessation of the periodicity in the time series of the body temperature value 505, the system can verify or detect an indicator of pregnancy 510 with greater accuracy and precision than if either the heart rate data or the body temperature data deviated from the periodicity in the time series.

[0180] In some cases, the system may identify a false positive for the pregnancy indicator 510 based on a single physiological measurement or a combination of physiological measurements. For example, if the system identifies a cessation of the time-series periodicity of a body temperature value of 505, but the received heart rate data does not deviate from the time-series periodicity of a heart rate value of 530, the system may determine that the detected pregnancy indicator 510 is invalid (e.g., a false positive). In such cases, the system may determine, based on its determination that a single physiological measurement or combination of physiological measurements does not deviate from the time-series periodicity, that the user may be experiencing illness, hormonal changes in the menstrual cycle, etc.

[0181] In some implementations, the system may identify the absence of a menstrual cycle (e.g., menstrual period 515) based on the determination of the time series. In such cases, the detection of pregnancy indicators may occur before the absence of menstrual period 515 is identified. For example, the system may detect pregnancy (e.g., pregnancy indicator 510) within a time period following menstrual period 515 based on the determination of the time series. In such cases, pregnancy indicators may be detected before the absence of a menstrual cycle (e.g., menstrual period 515) within that time period is identified. For example, the system may detect pregnancy indicator 510 and then identify the absence of a menstrual period 515 (e.g., its absence).

[0182] Figure 6 shows an example of a timing diagram 600 that supports pregnancy detection from wearable-based physiological data according to an aspect of this disclosure. The timing diagram 600 may implement aspects of system 100, system 200, system 300, or a combination thereof, or may be implemented by such aspects. For example, in some implementations, the timing diagram 600 may be displayed to the user via the GUI 275 of the user device 106, as shown in Figure 2.

[0183] As will be described in more detail herein, the system may be configured to detect indicators of pregnancy. In some cases, the user's temperature pattern throughout the day and night may be an indicator that can characterize pregnancy. For example, daytime and nighttime skin temperature may identify indicators of pregnancy. Thus, the timing diagram 600 illustrates the relationship between the user's temperature data and time (e.g., over several weeks and / or months). In this regard, the solid line shown in the timing diagram 600 may be understood to refer to “temperature value 605”. The dashed vertical line shown in the timing diagram 600 may be understood to refer to “onset of pregnancy 610”. The user's temperature value 605 may be relative to the baseline temperature.

[0184] In some cases, the system (e.g., ring 104, user device 106, server 110) may receive physiological data associated with the user from the wearable device. The physiological data may include at least body temperature data. Based on the received body temperature data, the system may determine a time series of multiple body temperature values ​​605 acquired over several days. The system may process the original time series body temperature data (e.g., body temperature values ​​605) to detect indicators of pregnancy.

[0185] Body temperature values ​​605 can be continuously collected by a wearable device. Physiological measurements may be taken continuously throughout the day and / or night. For example, in some implementations, the ring may be configured to continuously acquire physiological data (e.g., body temperature data, sleep data, heart rate, HRV data, respiratory rate data, MET data, etc.) according to one or more measurement cycles throughout the day / sleep cycle. In other words, the ring can continuously acquire physiological data from the user regardless of “trigger conditions” for making such measurements. In some cases, continuous finger temperature measurement may capture body temperature fluctuations (e.g., small or large fluctuations) that may not be apparent in core body temperature. For example, continuous finger temperature measurement may capture minute-by-minute or hourly body temperature fluctuations that may not be provided by other temperature measurements elsewhere on the body or if the user were manually measuring their temperature once a day.

[0186] The timing diagram 600 may show the body temperature trajectory of a user whose pregnancy reached full term (e.g., 40 weeks). For example, the timing diagram 600 may show that the body temperature value 605 at the start of pregnancy 610 may be higher than the body temperature value 605 before the start of pregnancy 610. In some cases, the body temperature value 605 at the start of pregnancy 610 may be higher than the body temperature value 605 after the start of pregnancy 610. In such cases, the body temperature value 605 at the start of pregnancy may represent a local maximum value 620.

[0187] Based on the determination of the time series, the system may identify one or more maximal values ​​615 in the first part of the time series of body temperature value 605. The first part of the time series occurs before the onset of pregnancy 610 and may represent the user's menstrual cycle. For example, the first part may correspond to one or more of the user's menstrual cycles. Based on the determination of the time series, the system may identify one or more maximal values ​​620 in the second part following the first part of the time series of body temperature value 605. The second part may include the time period in which the onset of pregnancy 610 occurred. For example, the second part may correspond to the time period corresponding to the onset of pregnancy 610. In such a case, identifying the rise in body temperature in the time series may respond to identifying one or more maximal values ​​615 in the first part and one or more maximal values ​​620 in the second part.

[0188] The system may compare one or more identified maximum values ​​615 in the first part with one or more identified maximum values ​​620 in the second part. In some cases, based on the comparison, the system may determine that one or more identified maximum values ​​620 in the second part are greater than one or more identified maximum values ​​614 in the first part. In such cases, the system may detect an indicator of pregnancy in response to the decision. For example, the timing chart 600 may show that the body temperature level (e.g., body temperature value 605) around the time of conception (e.g., at one or more maximum values ​​620 in the second part) is higher than the body temperature peak (e.g., body temperature value 605) in a previous menstrual cycle (e.g., at one or more maximum values ​​615 in the first part).

[0189] Figure 7 shows an example of a GUI 700 that supports pregnancy detection from wearable-based physiological data according to an aspect of the present disclosure. The GUI 700 may implement, or be implemented by, an aspect of system 100, system 200, system 300, timing diagram 400, timing diagram 500, timing diagram 600, or any combination thereof. For example, the GUI 700 may be an example of a GUI 275 for a user device 106 (e.g., user devices 106-a, 106-b, 106-c) corresponding to user 102.

[0190] In some examples, GUI 700 may present a set of application pages 705 that can be displayed to the user via GUI 700 (e.g., GUI 275 shown in Figure 2). The system's server may display a query on the GUI 700 of the user device (e.g., a mobile device) asking whether the user wants to activate period mode and track their menstrual cycle (e.g., via application page 705). In such a case, the system may generate a personalized cycle tracking experience on the GUI 700 of the user device to detect indicators of pregnancy based on contextual tags or user questions.

[0191] Continuing the above example, before detecting an indicator of pregnancy, opening the wearable application may present the user with an application page. The application page 705 may display a request to activate menstrual mode, allowing the system to track the menstrual cycle (for example, thereby enabling pregnancy detection). In such a case, the application page 705 may display an invitation card inviting the user to register for the menstrual cycle tracking application. The application page 705 may prompt the user to confirm whether they want to track the menstrual cycle, or to close the message if they do not want to track the menstrual cycle. The system may receive instructions from the user to choose to opt in to or opt out of menstrual cycle tracking. For example, the application page 705 may prompt the user to confirm whether they want to close the message if an early pregnancy can or cannot be detected. The system may receive instructions from the user to opt in to or opt out of early pregnancy detection.

[0192] If the user selects "Yes" to track the menstrual cycle and / or detect early pregnancy, the user may be presented with application page 705. Application page 705 may prompt the user to confirm the primary reason for tracking the cycle and / or detecting pregnancy (e.g., menstruation, ovulation, pregnancy, etc.). In such cases, application page 705 may prompt the user to confirm their intention to track the menstrual cycle and / or detect pregnancy. For example, the system may receive confirmation of the intended use of the tracking system via the user device.

[0193] In some cases, after confirming intent, the user may be presented with application page 705. Application page 705 may prompt the user to determine their average cycle length (for example, the period between the first day of the first menstrual cycle and the first day of the second menstrual cycle). In some cases, application page 705 may prompt the user to indicate whether they are experiencing an irregular cycle in which the average cycle length may not be determined. For example, the system may receive confirmation of the average cycle length via the user device.

[0194] Upon inputting the average cycle length or irregular cycle, the user may be presented with application page 705. Application page 705 may prompt the user to confirm the last cycle start date (e.g., the first day of the most recent menstrual cycle). Application page 705 may also prompt the user to indicate whether the user may be unable to identify the last cycle start date. For example, the system may receive confirmation of the last cycle start date via the user device.

[0195] In some cases, after confirming the start date of the last cycle, the user may be presented with application page 705. The application page may prompt the user to confirm whether they are using hormonal contraceptives. For example, the system may receive confirmation via the user device whether hormonal contraceptives are being used. If it is confirmed that hormonal contraceptives are not being used, the user may be presented with GUI 700, which may be further shown and explained with reference to application page 705.

[0196] The system's server may display pregnancy indicators on the user device's (e.g., mobile device) GUI 700 (e.g., via application page 705). In such a case, the system may output the detected pregnancy indicators on the user device's GUI 700 to indicate that the user is pregnant and / or experiencing a first-day pregnancy.

[0197] Continuing the above example, when an indicator of pregnancy is detected, the user may be presented with application page 705 when opening the wearable application. As shown in Figure 7, application page 705 may display an indication that pregnancy has been detected via message 720. In such a case, application page 705 may include message 720 on its homepage. If the user's pregnancy can be identified, the server may send message 720 to the user, as described herein, where message 720 is associated with the detected indicator of pregnancy of the user. In some cases, the server may send message 720 to a clinician, fertility specialist, caregiver, the user's partner, or a combination thereof. In such a case, the system may present application page 705 on the user device associated with the clinician, fertility specialist, caregiver, partner, or a combination thereof.

[0198] For example, a user device may receive a message 720 which may include the time interval in which the pregnancy detection occurred, a request to enter symptoms associated with the detected pregnancy, educational content associated with the detected pregnancy, a coordinated set of activity goals, etc. For example, message 720 may indicate the date of the indicator of the detected pregnancy, the possible date of conception (e.g., the estimated date of conception is April 28), the range of possible dates of conception (e.g., the estimated date of conception is April 27-29), the range of predicted due date dates (e.g., the estimated due date is January 21-29), or a combination thereof. In such a case, the range may include the date of the estimated date and the dates before and after the estimated date. Message 720 may be configurable / customizable so that the user can receive different messages 720 based on the indicator of the detected pregnancy, as described herein.

[0199] As shown in Figure 7, application page 705 may display pregnancy indicators via alert 710. The user may receive alert 710, which may prompt the user to confirm whether a detected pregnancy indicator occurred, or to dismiss alert 710 if a detected pregnancy indicator did not occur. In such cases, application page 705 may prompt the user to confirm or dismiss the pregnancy (e.g., confirm / deny whether the system correctly detected a pregnancy indicator). For example, the system may receive confirmation of a detected pregnancy indicator via the user device. In some cases, the system may receive confirmation of pregnancy via the user device in response to detecting a pregnancy indicator. In such cases, detecting a pregnancy indicator occurs before confirming the pregnancy. In addition, in some implementations, application page 705 may display one or more scores for the user for each day (e.g., sleep score, readiness score, etc.).

[0200] Application page 705 may display a pregnancy card, such as a "Detected Pregnancy Indicator Confirmation Card," indicating that the detected pregnancy indicator has been recorded. In some implementations, after confirming that the detected pregnancy indicator is valid, the pregnancy may be recorded / logged for the user for each calendar day. Furthermore, in some cases, the pregnancy may be used to update (e.g., correct) one or more scores associated with the user (e.g., sleep score, readiness score, etc.). That is, the user's score may be updated for the next calendar day after the detected pregnancy indicator has been confirmed, using data associated with the detected pregnancy indicator.

[0201] In some cases, the readiness score may be updated based on detected indicators of pregnancy. For example, an elevated body temperature relative to the user's body temperature baseline may prompt the system to alert the user via alert 710 regarding the user's physical signals (e.g., elevated body temperature). In such cases, the readiness score may indicate "Caution" to the user based on the elevated body temperature. If the readiness score changes for a user, the system may implement a recovery mode for users who may have more severe symptoms, allowing them to benefit from several days of adjusted activity and readiness guidance. In other examples, the readiness score may be updated based on the sleep score and elevated body temperature. However, the system may determine that the user is pregnant and adjust (e.g., increase) the readiness score and / or sleep score to offset the effects of pregnancy.

[0202] In some cases, a message 720 displayed to the user via the GUI 700 on the user device may indicate how the detected pregnancy indicators affected the overall score (e.g., overall readiness score, sleep score, activity score, etc.) and / or individual contributing factors. For example, the message might say, "It looks like 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" or "From your recovery metrics it looks like your body is still doing ok, so some light activity can help relieve the symptoms."

[0203] If an indicator of pregnancy is detected, message 720 may offer suggestions to the user to improve their overall health. For example, the message might say, "If you feel really low on energy, why not switch to rest mode for today?" or "Since you are feeling fatigued and nauseous, devote today for rest." In such cases, message 720 displayed to the user can provide targeted insights to help the user adjust their lifestyle during a part of their pregnancy. For users whose bodily signals (e.g., body temperature, heart rate, HRV, etc.) may respond to the phase of pregnancy, the system may display lower activity targets before and after the onset of pregnancy. In such cases, accurately detecting an indicator of pregnancy can improve the accuracy and efficiency of the readiness score and activity score.

[0204] If the user dismisses a prompt on application page 705-a (e.g., alert 710), the prompt may disappear, and the user may later enter pregnancy indicators via user input 725. In some cases, the system may prompt the user via message 720 to ask whether the user is pregnant or suggest switching to an alternative mode (e.g., pregnancy mode, rest mode) or deactivating menstrual mode. In such cases, the system may recommend switching from menstrual mode to pregnancy mode or rest mode based on the detection of pregnancy indicators.

[0205] Application page 705 may display one or more parameters of the detected pregnancy, including body temperature, heart rate, HRV, etc., experienced by the user, via graph display 715. Graph display 715 may be an example of timing diagram 400, as described with reference to Figure 4.

[0206] In some cases, the user may record symptoms via user input 725. For example, the system may receive user input (e.g., tags) to record symptoms associated with pregnancy (e.g., nausea, fatigue, tiredness, headache, migraine, pain, etc.). The system may recommend tags to the user based on the user history and detected pregnancy indicators. In some cases, the system may display pregnancy symptom tags on the GUI 700 of the user device based on the detection of pregnancy indicators.

[0207] In some cases, a user's recorded symptoms (e.g., tags), combined with the user's physiological data (e.g., body temperature patterns, HRV patterns, respiratory rate patterns, heart rate patterns, or combinations thereof), can be indicators that characterize early detection of pregnancy. In such cases, the user's recorded symptoms can be used in conjunction with the user's physiological data to confirm indicators of pregnancy (e.g., provide a definitive indicator or better prediction of pregnancy). For example, if the system determines that the received body temperature data is higher than the user's menstrual cycle baseline body temperature, and the system receives user input associated with pregnancy (e.g., nausea, fatigue, tiredness, headache, migraine, pain, etc.), the system can verify or detect indicators of pregnancy with greater accuracy and precision than if one of the body temperature data points deviated from the menstrual cycle baseline or if the user recorded pregnancy symptoms.

[0208] In some cases, the system may identify false positives for identifying pregnancy indicators based on user input, a single physiological measurement, a combination of physiological measurements, or a combination thereof. For example, if the system determines that the received temperature data is higher than the user's baseline temperature for their menstrual cycle, but the user input indicates symptoms associated with stress, illness, etc., the system may determine that the detected pregnancy indicator is invalid (e.g., a false positive). In such cases, the system may determine, based on the received user input, that the user may be experiencing illness, stress, hormonal changes during their menstrual cycle, etc.

[0209] Application page 705 may also include messages 720 containing insights and recommendations associated with the detected pregnancy indicators. The system's server may cause the GUI 700 on the user device to display messages 720 associated with the detected pregnancy indicators. The user device may view recommendations and / or information associated with pregnancy via messages 720. As previously stated herein, accurately detected pregnancy indicators may be beneficial to the user's overall health. In some implementations, the user device and / or server may generate pregnancy-related alerts 710 which may be displayed to the user via the GUI 700 (e.g., application page 705). In particular, messages 720 generated via the GUI 700 and displayed to the user may be associated with one or more characteristics (e.g., timing) of the detected pregnancy indicators.

[0210] In some cases, message 720 may represent recommendations on how the user may adjust their lifestyle in the days following and / or on the day of the detected pregnancy indicator. In some examples, if the user tags "fatigue" on the day of the detected pregnancy indicator, the system may display a prompt via message 720 suggesting that the user record "fatigue" via user input 725 on the days after tagging "fatigue". In other examples, the system may recommend an amount of time (e.g., calendar days) for the user to be active or estimate a recovery time following the detected pregnancy indicator.

[0211] In some implementations, the system may provide additional insights into the user's detected pregnancy indicators. For example, application page 705 may show one or more physiological parameters (e.g., contributing factors) that resulted in the user's detected pregnancy indicator, such as an increased body temperature. In other words, the system may be configured to provide some information or other insights into the detected pregnancy indicators. Personalized insights may indicate aspects of the collected physiological data (e.g., contributing factors within the physiological data) that were used to generate messages associated with the detected pregnancy indicators.

[0212] In some implementations, the system may be configured to receive user inputs 725 regarding indicators of detected pregnancy in order to train a classifier (e.g., supervised learning for a machine learning classifier) ​​and improve the user pregnancy detection technique. For example, the user may receive user inputs 725 such as the onset of symptoms, confirmation of detected pregnancy indicators, etc. These user inputs 725 may then be fed into the classifier to train it. In other words, user inputs 725 may be used to verify or confirm indicators of detected pregnancy.

[0213] When an indicator of pregnancy is detected on application page 705, GUI 700 may display a calendar view that shows the current date the user is viewing application page 705, a date range containing the day the pregnancy is detected, a date range containing the day the pregnancy is estimated, a date range containing the estimated due date, or a combination thereof. For example, the date range may enclose calendar days using a dashed line configuration, the current date may enclose calendar days, and the detected pregnancy day and / or estimated conception / due date may enclose them. The calendar view may also include a message that includes the current calendar day and an indicator of the user's pregnancy day (e.g., that the user is 8 weeks pregnant).

[0214] Figure 8 shows a block diagram 800 of a device 805 that supports pregnancy detection from wearable-based physiological data according to an aspect of the present disclosure. Device 805 may include an input module 810, an output module 815, and a wearable application 820. Device 805 may also include a processor. Each of these components may communicate with one another (for example, via one or more buses).

[0215] The input module 810 may provide means for receiving information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to disease detection technology). The information may be passed to other components of device 805. The input module 810 may utilize a single antenna or a set of multiple antennas.

[0216] The output module 815 may provide means for transmitting signals generated by other components of device 805. For example, the output module 815 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. In some examples, the output module 815 may be located in the same place as the input module 810 within the transceiver module. The output module 815 may utilize a single antenna or a set of antennas.

[0217] For example, the wearable application 820 may include a data acquisition component 825, a body temperature data component 830, a calculation component 835, a pregnancy component 840, a user interface component 845, or any combination thereof. In some examples, the wearable application 820 or its various components may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or in other ways in cooperation with the input module 810, the output module 815, or both. For example, the wearable application 820 may receive information from the input module 810 and transmit information to the output module 815, or be integrated in combination with the input module 810, the output module 815, or both to receive information, transmit information, or perform various other operations as described herein.

[0218] The data acquisition component 825 may be configured as a means for receiving physiological data associated with the user from a wearable device, or may support this in other ways, the physiological data including at least body temperature data. The body temperature data component 830 may be configured as a means for determining a time series of multiple body temperature values ​​acquired over multiple days, at least in part, based on the received body temperature data, or may support this in other ways. The calculation component 835 may be configured as a means for identifying an increase in body temperature in a time series of multiple body temperature values ​​relative to the user's body temperature baseline, at least in part, based on the determination of the time series, or may support this in other ways. The pregnancy component 840 may be configured as a means for detecting an indicator of pregnancy in the user, at least in part, based on the identified increase in body temperature, where the indicator of pregnancy in the user is detectable from the identified increase in body temperature before it is detectable from a threshold increase in hormone elevation relative to the user's hormone baseline. The user interface component 845 may be configured as a means for displaying the detected indicator of pregnancy on the graphical user interface of the user device, or may support this in other ways.

[0219] Figure 9 shows a block diagram 900 of a wearable application 920 that supports pregnancy detection from wearable-based physiological data according to an aspect of this disclosure. The wearable application 920 may be an example of an aspect of the wearable application or wearable application 820, or both, as described herein. The wearable application 920 or its various components may be examples of means for performing various aspects of pregnancy detection from wearable-based physiological data as described herein. For example, the wearable application 920 may include a data acquisition component 925, a body temperature data component 930, a calculation component 935, a pregnancy component 940, a user interface component 945, or any combination thereof. Each of these components may communicate with one another directly or indirectly (e.g., via one or more buses).

[0220] The data acquisition component 925 may be configured as a means for receiving physiological data associated with the user from a wearable device, or may support this in other ways, the physiological data including at least body temperature data. The body temperature data component 930 may be configured as a means for determining a time series of multiple body temperature values ​​acquired over multiple days, at least in part, based on the received body temperature data, or may support this in other ways. The calculation component 935 may be configured as a means for identifying an increase in body temperature in a time series of multiple body temperature values ​​relative to the user's body temperature baseline, at least in part, based on determining the time series, or may support this in other ways. The pregnancy component 940 may be configured as a means for detecting an indicator of pregnancy in the user, at least in part, based on the identified increase in body temperature, where the indicator of pregnancy in the user is detectable from the identified increase in body temperature before it is detectable from a threshold increase in hormone levels relative to the user's hormone baseline. The user interface component 945 may be configured as a means for displaying the detected indicator of pregnancy on the graphical user interface of the user device, or may support this in other ways.

[0221] In some examples, the body temperature data component 930 may be configured as a means for identifying one or more local maxima of a first part of a time series of multiple body temperature values, at least in part on determining the time series, or it may support this in other ways. In some examples, the body temperature data component 930 may be configured as a means for identifying one or more local maxima of a second part following a first part of a time series of multiple body temperature values, at least in part on determining the time series, or it may support this in other ways, and the identification of a rise in body temperature in the time series is at least in part on identifying one or more local maxima of the first part and the second part.

[0222] In some examples, the body temperature data component 930 may be configured as a means for comparing one or more identified maximum values ​​of a first part with one or more identified maximum values ​​of a second part, or may support this in other ways, where the first part corresponds to multiple menstrual cycles of the user, and the second part corresponds to a time period corresponding to pregnancy. In some examples, the body temperature data component 930 may be configured as a means for determining, at least partially based on comparison, that one or more identified maximum values ​​of the second part are greater than one or more identified maximum values ​​of the first part, or may support this in other ways, so that pregnancy detection is at least partially based on determination.

[0223] In some examples, the physiological data further include heart rate data, and the data acquisition component 925 may be configured as a means for determining, or otherwise supporting, that the received heart rate data exceeds the user's non-pregnancy baseline heart rate during at least part of several days, where detecting an indicator of pregnancy is at least in part based on determining that the received heart rate data meets the user's non-pregnancy baseline heart rate.

[0224] In some examples, the physiological data further include heart rate variability data, and the data acquisition component 925 may be configured, or otherwise support, for determining that the received heart rate variability data is smaller than the user's non-pregnancy baseline heart rate variability over at least a portion of several days, where detecting an indicator of pregnancy is at least in part based on determining that the received heart rate variability data is smaller than the non-pregnancy baseline heart rate variability.

[0225] In some examples, the physiological data further include respiratory rate data, and the data acquisition component 925 may be configured, or otherwise supported, for determining whether the received respiratory rate data exceeds the user's non-pregnancy baseline respiratory rate during at least part of several days, where detecting an indicator of pregnancy is at least in part based on determining whether the received respiratory rate data exceeds the user's non-pregnancy baseline respiratory rate.

[0226] In some examples, the body temperature data component 930 may be configured, or otherwise support, a means of identifying a cessation of periodicity in a time series of multiple body temperature values, at least in part, based on determining the time series, and detecting pregnancy is at least in part based on identifying a cessation of periodicity.

[0227] In some examples, the pregnancy component 940 may be configured as a means for identifying the absence of a menstrual cycle, at least in part on determining the time series, or may support this in other ways, and the indicator of pregnancy may be at least in part on identifying the absence of periodicity.

[0228] In some examples, the user interface component 945 may be configured as a means for receiving a pregnancy confirmation in response to the detection of an indicator of pregnancy via the user device, or may support this in other ways, where the detection of an indicator of pregnancy occurs before the pregnancy confirmation.

[0229] In some examples, the body temperature data component 930 may be configured as a means for determining each of a plurality of body temperature values ​​based at least in part on receiving body temperature data, or may support this in other ways, where the body temperature data includes continuous nighttime body temperature data.

[0230] In some examples, the calculation component 935 may be configured as a means for updating a user-associated readiness score, a user-associated activity score, a user-associated sleep score, or a combination thereof, at least in part, based on the detection of pregnancy indicators, or may support this in other ways.

[0231] In some examples, the user interface component 945 may be configured as a means for displaying pregnancy symptom tags on the graphical user interface of a user device associated with the user, or may support this in other ways, at least in part, based on the detection of pregnancy indicators.

[0232] In some examples, the user interface component 945 may be configured as a means for displaying a message associated with the detected pregnancy indicator on the graphical user interface of a user device associated with the user, or it may support this in other ways.

[0233] In some examples, the message may further include a time interval during which the pregnancy was detected, a request to enter symptoms associated with the detected pregnancy, educational content related to the detected pregnancy, and a coordinated set of activity goals or a combination thereof.

[0234] In some examples, the computational component 935 may be configured as a means for inputting physiological data into a machine learning classifier, or may support this in other ways, where detecting indicators of pregnancy is at least partially based on inputting physiological data into a machine learning classifier.

[0235] In some examples, wearable devices include wearable ring devices.

[0236] In some cases, wearable devices collect physiological data from users based on arterial blood flow.

[0237] Figure 10 shows a diagram of a system 1000 including a device 1005 that supports pregnancy detection from wearable-based physiological data, according to an aspect of this disclosure. Device 1005 may be an example of a component of device 805 as described herein, or may include such components. Device 1005 may include an example of a user device 106 as described herein. Device 1005 may include components for bidirectional communication, including components for sending and receiving communications with a wearable device 104 and a server 110, such as a wearable application 1020, a communication module 1010, an antenna 1015, a user interface component 1025, a database (application data) 1030, a memory 1035, and a processor 1040. These components may communicate electronically or be coupled in other ways via one or more buses (e.g., bus 1045) (e.g., operationally, communicatively, functionally, electronically, electrically).

[0238] The communication module 1010 may manage input and output signals for device 1005 via antenna 1015. The communication module 1010 may include an example of the communication module 220-b of user device 106 as shown in Figure 2. In this regard, the communication module 1010 may manage communication with ring 104 and server 110, as shown in Figure 2. The communication module 1010 may also manage peripherals not integrated into device 1005. In some cases, the communication module 1010 may represent a physical connection or port to an external peripheral. In some cases, the communication module 1010 may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS / 2®, UNIX®, LINUX®, or another known operating system. In other cases, the communication module 1010 may represent or interact with a wearable device (e.g., a ring 104), a modem, a keyboard, a mouse, a touchscreen, or a similar device. In some cases, the communication module 1010 may be implemented as part of the processor 1040. In some examples, the user may interact with the device 1005 via the communication module 1010, the user interface component 1025, or via a hardware component controlled by the communication module 1010.

[0239] In some cases, device 1005 may include a single antenna 1015. However, in some other cases, device 1005 may have two or more antennas 1015 that can transmit or receive multiple wireless transmissions simultaneously. Communication module 1010 may communicate bidirectionally via one or more antennas 1015, as described herein, via a wired or wireless link. For example, communication module 1010 may represent a wireless transceiver and communicate bidirectionally with another wireless transceiver. Communication module 1010 may also include a modem for modulating packets, providing the modulated packets to one or more antennas 1015 for transmission, and demodulating packets received from one or more antennas 1015.

[0240] The user interface component 1025 can manage data storage and processing within the database 1030. In some cases, the user may interact with the user interface component 1025. In other cases, the user interface component 1025 may operate automatically without user interaction. The database 1030 may be a single database, a distributed database, multiple distributed databases, a data store, a data lake, or an emergency backup database.

[0241] Memory 1035 may include RAM and ROM. Memory 1035 may store computer-readable and computer-executable software, which, when executed, includes instructions that cause the processor 1040 to perform various functions described herein. In some cases, memory 1035 may include a BIOS that can control basic hardware or software operations, such as interaction with peripheral components or devices, among other things.

[0242] The processor 1040 may include intelligent hardware devices (e.g., general-purpose processors, DSPs, CPUs, microcontrollers, ASICs, FPGAs, programmable logic devices, discrete gates or transistor logic components, discrete hardware components, or any combination thereof). In some cases, the processor 1040 may be configured to operate a memory array using a memory controller. In other cases, the memory controller may be integrated into the processor 1040. The processor 1040 may be configured to execute computer-readable instructions stored in memory 1035 to perform various functions (e.g., functions or tasks supporting methods and systems for sleep phase algorithms).

[0243] For example, the wearable application 1020 may be configured as a means for receiving physiological data associated with a user from a wearable device, or may otherwise support such a means, the physiological data including at least body temperature data. The wearable application 1020 may be configured as a means for determining a time series of multiple body temperature values ​​acquired over multiple days, at least in part on the received body temperature data, or may otherwise support such a means. The wearable application 1020 may be configured as a means for identifying an increase in body temperature in a time series of multiple body temperature values ​​relative to the user's body temperature baseline, at least in part on the determination of the time series, or may otherwise support this. The wearable application 1020 may be configured as a means for detecting an indicator of pregnancy in the user, at least in part on the identified increase in body temperature, where the indicator of pregnancy in the user is detectable from the identified increase in body temperature before it is detectable from a threshold increase in hormone levels relative to the user's hormone baseline. The wearable application 1020 may be configured as a means for displaying detected pregnancy indicators on the graphical user interface of the user device, or may support such means in other ways.

[0244] By including or configuring a wearable application 1020 according to the embodiments described herein, device 1005 may support technologies for improved communication reliability, reduced latency, improved user experience related to reduced processing, reduced power consumption, more efficient use of communication resources, improved coordination between devices, longer battery life, and improved utilization of processing power.

[0245] The wearable application 1020 may include an application (e.g., "app"), a program, software, or other components, which are configured to facilitate communication with the ring 104, the server 110, other user devices 106, etc. For example, the wearable application 1020 may include an application executable on the user device 106 that is configured to receive data (e.g., physiological data) from the ring 104, perform processing operations on the received data, send and receive data with the server 110, and present the data to the user 102.

[0246] Figure 11 is a flowchart of a method 1100 supporting pregnancy detection from wearable-based physiological data according to an aspect of this disclosure. The operation of method 1100 may be implemented by a user device or its components, as described herein. For example, the operation of method 1100 may be performed by a user device as described with reference to Figures 1 to 10. In some examples, the user device may execute a set of instructions that control the functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may use dedicated hardware to perform aspects of the described functions.

[0247] In 1105, the method may include the step of receiving physiological data associated with a user from a wearable device, the physiological data including at least body temperature data. The operation of 1105 may be performed according to the examples disclosed herein. In some examples, the operation of 1105 may be performed by a data acquisition component 925, as described with reference to Figure 9.

[0248] In 1110, the method may include the step of determining a time series of multiple body temperature values ​​acquired over several days, at least in part, based on the received body temperature data. The operation of 1110 may be performed according to the examples disclosed herein. In some examples, the operation of 1110 may be performed by the body temperature data component 930, as described with reference to Figure 9.

[0249] In 1115, the method may include, at least in part, the step of identifying a rise in body temperature over a time series of multiple body temperature values ​​relative to the user's body temperature baseline, based on the determination of the time series. The operation of 1115 may be performed according to the examples disclosed herein. In some examples, the operation of 1115 may be performed by the computational component 935, as described with reference to Figure 9.

[0250] In 1120, the method may include a step of detecting an indicator of pregnancy in the user, at least in part, based on an identified rise in body temperature, where the indicator of pregnancy in the user is detectable from the identified rise in body temperature before it is detectable from a threshold increase in hormone elevation relative to the user's hormone baseline. The operation of 1120 may be performed according to the examples disclosed herein. In some examples, the operation of 1120 may be performed by the pregnancy component 940, as described with reference to Figure 9.

[0251] In 1125, the method may include the step of displaying the detected pregnancy indicator on the graphical user interface of the user device. The operation of 1125 may be performed according to the examples disclosed herein. In some examples, the operation of 1125 may be performed by the user interface component 945, as described with reference to Figure 9.

[0252] Figure 12 is a flowchart of Method 1200, which supports pregnancy detection from wearable-based physiological data, according to an aspect of this disclosure. The operation of Method 1200 may be implemented by a user device or its components, as described herein. For example, the operation of Method 1200 may be performed by a user device, such as those described with reference to Figures 1 to 10. In some examples, the user device may execute a set of instructions that control the functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may use dedicated hardware to perform aspects of the described functions.

[0253] In 1205, the method may include the step of receiving physiological data associated with a user from a wearable device, the physiological data including at least body temperature data. The operation of 1205 may be performed according to the examples disclosed herein. In some examples, the operation of 1205 may be performed by a data acquisition component 925, as described with reference to Figure 9.

[0254] In 1210, the method may include the step of determining a time series of multiple body temperature values ​​acquired over several days, at least in part, based on the received body temperature data. The operation of 1210 may be performed according to the examples disclosed herein. In some examples, the operation of 1210 may be performed by the body temperature data component 930, as described with reference to Figure 9.

[0255] In 1215, the method may include the step of identifying one or more local maxima of a first portion of a time series of multiple body temperature values, at least in part, based on the determination of the time series. The operation of 1215 may be performed according to the examples disclosed herein. In some examples, the operation of 1215 may be performed by the body temperature data component 930, as described with reference to Figure 9.

[0256] In 1220, the method may include the step of identifying one or more local maxima of a second part following a first part of a time series of multiple body temperature values, at least in part on determining the time series, wherein identifying the rise in body temperature in the time series is at least in part on identifying one or more local maxima of the first and second parts. The operation of 1220 may be performed according to the examples disclosed herein. In some examples, the operation of 1220 may be performed by a body temperature data component 930, as described with reference to Figure 9.

[0257] In 1225, the method may include, at least in part, the step of identifying a rise in body temperature over a time series of multiple body temperature values ​​relative to the user's body temperature baseline, based on the determination of the time series. The operation of 1225 may be performed according to the examples disclosed herein. In some examples, the operation of 1225 may be performed by the computational component 935, as described with reference to Figure 9.

[0258] In 1230, the method may include a step of detecting an indicator of pregnancy in the user, at least in part, based on an identified rise in body temperature, where the indicator of pregnancy in the user is detectable from the identified rise in body temperature before it is detectable from a threshold increase in the rise in hormones relative to the user's hormone baseline. The operation of 1230 may be performed according to the examples disclosed herein. In some examples, the operation of 1230 may be performed by the pregnancy component 940, as described with reference to Figure 9.

[0259] In 1235, the method may include the step of displaying the detected pregnancy indicator on the graphical user interface of the user device. The operation of 1235 may be performed according to the examples disclosed herein. In some examples, the operation of 1235 may be performed by the user interface component 945, as described with reference to Figure 9.

[0260] Figure 13 is a flowchart of Method 1300, according to an aspect of this disclosure, which supports pregnancy detection from wearable-based physiological data. The operation of Method 1300 may be implemented by a user device or its components, as described herein. For example, the operation of Method 1300 may be performed by a user device, such as those described with reference to Figures 1 to 10. In some examples, the user device may execute a set of instructions that control the functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may use dedicated hardware to perform aspects of the described functions.

[0261] In 1305, the method may include the step of receiving physiological data associated with a user from a wearable device, the physiological data including at least body temperature data. The operation of 1305 may be performed according to the examples disclosed herein. In some examples, the operation of 1305 may be performed by a data acquisition component 925, as described with reference to Figure 9.

[0262] In 1310, the method may include the step of determining a time series of multiple body temperature values ​​acquired over several days, at least in part, based on the received body temperature data. The operation of 1310 may be performed according to the examples disclosed herein. In some examples, the operation of 1310 may be performed by the body temperature data component 930, as described with reference to Figure 9.

[0263] In 1315, the method may include, at least in part, the step of identifying an increase in body temperature in a time series of multiple body temperature values ​​relative to a user's body temperature baseline, based on the determination of the time series. The operation of 1315 may be performed according to the examples disclosed herein. In some examples, the operation of 1315 may be performed by the computational component 935, as described with reference to Figure 9.

[0264] In 1320, the method may include a step of identifying a cessation of periodicity in a time series of body temperature values, at least in part on determining the time series, where detecting pregnancy is at least in part on identifying the cessation of periodicity. The operation of 1320 may be performed according to the examples disclosed herein. In some examples, the operation of 1320 may be performed by a body temperature data component 930, as described with reference to Figure 9.

[0265] In 1325, the method may include a step of detecting an indicator of pregnancy in the user, at least in part, based on an identified rise in body temperature, where the indicator of pregnancy in the user is detectable from the identified rise in body temperature before it is detectable from a threshold increase in the rise in hormones relative to the user's hormone baseline. The operation of 1325 may be performed according to the examples disclosed herein. In some examples, the operation of 1325 may be performed by the pregnancy component 940, as described with reference to Figure 9.

[0266] In 1330, the method may include the step of displaying the detected pregnancy indicator on the graphical user interface of the user device. The operation of 1330 may be performed according to the examples disclosed herein. In some examples, the operation of 1330 may be performed by the user interface component 945, as described with reference to Figure 9.

[0267] The methods described above illustrate possible implementations, and it should be noted that the operations and steps may be rearranged or modified in other ways, and other implementations are possible. Furthermore, two or more aspects of the methods described above may be combined.

[0268] The method is described below. The method may include 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 multiple body temperature values ​​taken over multiple days, at least in part on the received body temperature data; identifying an increase in body temperature in the time series of multiple body temperature values ​​relative to the user's body temperature baseline, at least in part on the determination of the time series; detecting an indicator of pregnancy in the user, at least in part on the identified increase in body temperature, wherein the indicator of pregnancy in the user is detectable from the identified increase in body temperature before it is detectable from a threshold increase in hormone levels relative to the user's hormone baseline; and displaying the detected indicator of pregnancy in the graphical user interface of the user device.

[0269] The device is described below. The device may include a processor, memory coupled to the processor, and instructions stored in the memory. The instructions are executable by the processor and cause the device to receive physiological data associated with the user from a wearable device, the physiological data including at least body temperature data, and at least in part on the received body temperature data, the device determines a time series of multiple body temperature values ​​taken over multiple days, at least in part on determining the time series, the device identifies an increase in body temperature in the time series of multiple body temperature values ​​relative to the user's body temperature baseline, and at least in part on the identified increase in body temperature, the device detects an indicator of pregnancy in the user, the indicator of pregnancy in the user is detectable from the identified increase in body temperature before it is detectable from a threshold increase in hormone levels relative to the user's hormone baseline, and the detected indicator of pregnancy may be displayed on the graphical user interface of the user device.

[0270] Another device is described. This device is a means for receiving physiological data associated with a user from a wearable device, wherein the physiological data includes means for including at least body temperature data; means for determining a time series of multiple body temperature values ​​acquired over multiple days, at least in part on the received body temperature data; means for identifying an increase in body temperature in the time series of multiple body temperature values ​​relative to the user's body temperature baseline, at least in part on the determination of the time series; and means for detecting an indicator of pregnancy in the user, at least in part on the identified increase in body temperature, wherein the indicator of pregnancy in the user is detectable from the identified increase in body temperature before it is detectable from a threshold increase in hormone elevation relative to the user's hormone baseline; and means for displaying the detected indicator of pregnancy on the graphical user interface of the user device.

[0271] The description concerns a non-temporary computer-readable medium. The code is an instruction executable by a processor, which receives physiological data associated with a user from a wearable device, the physiological data including at least body temperature data, and which determines a time series of multiple body temperature values ​​taken over multiple days, at least in part on the received body temperature data, and which identifies an increase in body temperature in the time series of multiple body temperature values ​​relative to the user's body temperature baseline, at least in part on the identified increase in body temperature, and which detects an indicator of pregnancy in the user, the indicator of pregnancy in the user being detectable from the identified increase in body temperature before it is detectable from a threshold increase in hormone levels relative to the user's hormone baseline, and may include an instruction to display the detected indicator of pregnancy on the graphical user interface of the user device.

[0272] Some examples of methods, apparatus and non-temporal computer-readable media described herein may further include operations, features, means or instructions for identifying one or more maximal values ​​in a first part of a time series of multiple body temperature values, at least in part on determining the time series, and for identifying one or more maximal values ​​in a second part following the first part of the time series of multiple body temperature values, and for identifying a rise in body temperature in the time series, at least in part on determining the time series, and for identifying one or more maximal values ​​in the first and second parts.

[0273] Some examples of methods, apparatus and non-temporary computer-readable media described herein involve comparing one or more identified maximum values ​​in a first part with one or more identified maximum values ​​in a second part, where the first part corresponds to multiple menstrual cycles of a user, and the second part corresponds to a period of time corresponding to pregnancy, and further include operations, features, means or instructions for determining, at least in part, that one or more identified maximum values ​​in the second part are greater than one or more identified maximum values ​​in the first part, and detecting pregnancy may be based at least in part on such determination.

[0274] In some examples of methods, apparatus and non-temporary computer-readable media described herein, physiological data further include heart rate data, and the methods, apparatus and non-temporary computer-readable media may further include actions, features, means or instructions for determining that received heart rate data exceeds the user's non-pregnancy baseline heart rate during at least a portion of several days, and detecting an indicator of pregnancy may be at least in part based on determining that received heart rate data exceeds the user's non-pregnancy baseline heart rate.

[0275] In some examples of the methods, apparatus and non-temporary computer-readable media described herein, physiological data further include heart rate variability data, and the methods, apparatus and non-temporary computer-readable media may further include operations, features, means or instructions for determining that the received heart rate variability data is below the user's non-pregnancy baseline heart rate variability for at least a portion of several days, and detecting an indicator of pregnancy may be at least in part based on determining that the received heart rate variability data is below the user's non-pregnancy baseline variability.

[0276] In some examples of methods, apparatus and non-temporary computer-readable media described herein, physiological data further include respiratory rate data, and the methods, apparatus and non-temporary computer-readable media may further include operations, features, means or instructions for determining that received respiratory rate data exceeds the user's non-pregnancy baseline respiratory rate during at least a portion of several days, and detecting an indicator of pregnancy may be at least in part based on determining that received respiratory rate data exceeds the user's non-pregnancy baseline respiratory rate.

[0277] Some examples of methods, apparatus and non-temporary computer-readable media described herein may further include actions, features, means or instructions for identifying a cessation of periodicity in a time series of multiple body temperature values, at least in part on determining the time series, and detecting pregnancy may be at least in part on identifying a cessation of periodicity.

[0278] Some examples of methods, apparatus and non-temporary computer-readable media described herein may further include actions, features, means or instructions for identifying the absence of a menstrual cycle, at least in part on determining a sequence, and the detection of indicators of pregnancy is performed prior to identifying the absence of a menstrual cycle.

[0279] Some examples of methods, apparatus and non-temporary computer-readable media described herein may further include actions, features, means or instructions for receiving a pregnancy confirmation in response to the detection of an indicator of pregnancy via a user device, wherein the detection of an indicator of pregnancy is performed prior to the pregnancy confirmation.

[0280] Some examples of methods, apparatus and non-temporary computer-readable media described herein may further include operations, features, means or instructions for determining each of a plurality of body temperature values, at least in part, based on receiving body temperature data, the body temperature data including continuous nighttime body temperature data.

[0281] Some examples of methods, apparatus and non-temporary computer-readable media described herein may further include actions, features, means or instructions for updating a user-associated readiness score, a user-associated activity score, a user-associated sleep score, or a combination thereof, at least in part, based on the detection of indicators of pregnancy.

[0282] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include actions, features, means, or instructions for causing a pregnancy symptom tag to be displayed on a graphical user interface of a user device associated with a user, at least in part, based on the detection of an indicator of pregnancy.

[0283] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include actions, features, means, or instructions for causing a message associated with a detected indicator of pregnancy to be displayed on the graphical user interface of a user device associated with the user.

[0284] In some examples of the methods, apparatuses, and non-transitory computer-readable media described herein, the message may further include a time interval at which pregnancy detection occurs, a request to input symptoms associated with the detected pregnancy, educational content associated with the detected pregnancy, an adjusted set of activity goals, or a combination thereof.

[0285] Some examples of the methods, apparatuses, 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, and detecting an indicator of pregnancy may be based at least in part on inputting the physiological data into the machine learning classifier.

[0286] In some examples of the methods, apparatuses, and non-transitory computer-readable media described herein, the wearable device includes a wearable ring device.

[0287] In some examples of the methods, apparatuses, and non-transitory computer-readable media described herein, the wearable device collects physiological data from the user based on arterial blood flow.

[0288] The following provides a summary of aspects of the present disclosure:

[0289] Aspect 1: A method comprising: receiving physiological data associated with a user from a wearable device, wherein the physiological data includes at least body temperature data; determining a time series of a plurality of body temperature values obtained over a plurality of days based at least in part on the received body temperature data; identifying an increase in body temperature in the time series of the plurality of body temperature values relative to the user's body temperature baseline based at least in part on the determination of the time series; detecting an indication of pregnancy of the user based at least in part on the identified increase in body temperature, wherein the indication of pregnancy of the user is detectable from the identified increase in body temperature before it can be detected from a threshold increase in a hormone elevation relative to the user's hormone baseline; and causing the detected indication of pregnancy to be displayed on a graphical user interface of a user device.

[0290] Aspect 2: The method of Aspect 1, further comprising: identifying one or more local maxima in a first portion of the time series of the plurality of body temperature values based at least in part on the determination of the time series; and identifying one or more local maxima in a second portion of the time series of the plurality of body temperature values subsequent to the first portion based at least in part on the determination of the time series, wherein the step of identifying the increase in body temperature in the time series is based at least in part on identifying the one or more local maxima in the first portion and the second portion.

[0291] Aspect 3: The method of Aspect 1 or 2, comprising: comparing the one or more identified local maxima of the first portion with the one or more identified local maxima of the second portion, wherein the first portion corresponds to a plurality of menstrual cycles of the user, and the second portion corresponds to a time period corresponding to pregnancy; and determining, based at least in part on the comparison, that the one or more identified local maxima of the second portion are larger than the one or more identified local maxima of the first portion, wherein detecting the pregnancy is based at least in part on the determination.

[0292] Embodiment 4: The method according to any one of Embodiments 1 to 3, wherein the physiological data further includes heart rate data, the method further includes the step of determining that, during at least a portion of the multiple days, the received heart rate data exceeds the user's non-pregnancy baseline heart rate, and the step of detecting the indicator of pregnancy is at least in part based on determining that the received heart rate data exceeds the user's non-pregnancy baseline heart rate.

[0293] Embodiment 5: The physiological data further includes heart rate variability data, and the method is, The method according to any one of embodiments 1 to 4, further comprising the step of determining that, during at least a portion of the aforementioned multiple days, the received heart rate variability data is below the user's non-pregnancy baseline heart rate variability, and the step of detecting the indicator of pregnancy is at least in part based on determining that the received heart rate variability data is below the user's non-pregnancy baseline variability.

[0294] Embodiment 6: The physiological data further includes respiratory rate data, and the method is The method according to any one of embodiments 1 to 5, further comprising the step of determining that during at least a portion of the aforementioned multiple days, the received respiratory rate data exceeds the user's non-pregnancy baseline respiratory rate, and the step of detecting the indicator of pregnancy is at least in part based on determining that the received respiratory rate data exceeds the user's non-pregnancy baseline respiratory rate.

[0295] Embodiment 7: The method of any one of Embodiments 1 to 6, further comprising the step of identifying a cessation of the periodicity of the time series of the plurality of body temperature values, at least in part on determining the time series, wherein detecting pregnancy is at least in part on identifying the cessation of the periodicity.

[0296] Embodiment 8: The method of any one of embodiments 1 to 7, further comprising the step of identifying a absence of a menstrual cycle, at least in part on determining the time series, wherein the step of detecting an indicator of pregnancy is performed before identifying the absence of a menstrual cycle.

[0297] Embodiment 9: The method according to any one of Embodiments 1 to 8, further comprising the step of receiving confirmation of pregnancy in response to the detection of the pregnancy indicator via the user device, wherein the step of detecting the pregnancy indicator is performed prior to the confirmation of pregnancy.

[0298] Embodiment 10: The method according to any one of Embodiments 1 to 9, further comprising the step of determining each of the plurality of body temperature values, at least in part, based on receiving the body temperature data, wherein the body temperature data includes continuous nighttime body temperature data.

[0299] Embodiment 11: The method according to any one of Embodiments 1 to 10, further comprising the step of updating a readiness score associated with the user, an activity score associated with the user, a sleep score associated with the user, or a combination thereof, at least in part on the detection of the indicator of pregnancy.

[0300] Embodiment 12: The method according to any one of embodiments 1 to 11, further comprising the step of displaying a pregnancy symptom tag on the graphical user interface of a user device associated with the user, at least in part on the detection of the pregnancy indicator.

[0301] Embodiment 13: The method according to any one of Embodiments 1 to 12, further comprising the step of displaying a message associated with the detected pregnancy indicator on the graphical user interface of a user device associated with the user.

[0302] Embodiment 14: The method according to Embodiment 13, wherein the message further includes a time interval during which the pregnancy was detected, a request to input symptoms associated with the detected pregnancy, educational content associated with the detected pregnancy, a coordinated set of activity goals, or a combination thereof.

[0303] Embodiment 15: The method according to any one of Embodiments 1 to 14, further comprising the step of inputting the physiological data into a machine learning classifier, wherein the step of detecting the indicator of pregnancy is at least in part based on inputting the physiological data into the machine learning classifier.

[0304] Embodiment 16: The method according to any one of Embodiments 1 to 15, wherein the wearable device includes a wearable ring device.

[0305] Embodiment 17: The method according to any one of Embodiments 1 to 16, wherein the wearable device collects the physiological data from the user based on arterial blood flow.

[0306] Embodiment 18: A device comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor, which cause the device to perform the method described in any one of Embodiments 1 to 17.

[0307] Embodiment 19: An apparatus comprising at least one means for performing the method described in any one of Embodiments 1 to 17.

[0308] Embodiment 20: A non-temporary computer-readable medium for storing code, wherein the code comprises instructions that can be executed by a processor to perform the method described in any one of Embodiments 1 to 17.

[0309] In connection with the accompanying drawings, the description set forth herein describes exemplary configurations and is not intended to represent all examples that may be implemented or fall within the scope of the claims. As used herein, the term "exemplary" means "serving as an example, instance, or illustration" and does not mean "preferred" or "advantaged over other examples". 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.

[0310] In the accompanying drawings, similar components or features may have the same reference label. Furthermore, various components of the same type may be distinguished by a second label that follows the reference label with a hyphen to distinguish similar components. Where only the 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.

[0311] Information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, 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.

[0312] The various illustrative blocks and modules described in connection with the disclosure herein may be implemented or run on general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller or state machine. The processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, a DSP core and one or more microprocessors associated with it, or any other such configuration).

[0313] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. When implemented in software executed by a processor, the functions may be stored or transmitted as one or more instructions or codes on a computer-readable medium. Other examples and implementations are within the scope of this disclosure and the appended claims. For example, depending on the nature of the software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwiring, or any combination thereof. The features implementing the functions may also be physically located in various locations, including being distributed so that some of the functions are implemented in different physical locations. Also, when used herein, including in the claims, "or" in a list of items (e.g., a list of items preceded by a phrase such as "at least one of" or "one or more of") means an inclusive list such as, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, when 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, when used herein, the phrase “based on” shall be construed in the same manner as the phrase “based at least in part.”

[0314] Computer-readable media include both non-temporary computer storage media and communication media, including any media that facilitate the transfer of computer programs from one location to another. Non-temporary storage media may be any available media that can be accessed by a general-purpose or dedicated computer. By example, and not by limitation, non-temporary computer-readable media may include RAM, ROM, electrically erasable programmable ROM (EEPROM), compact disk (CD)ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-temporary media 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 dedicated computer or general-purpose or dedicated processor. Any connection is also appropriately referred to as computer-readable media. For example, if software is 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, then that coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technology such as infrared, radio, or microwave is included in the definition of media. As used herein, the term "disk" or "disc" includes CDs, laserdiscs, optical discs, digital multipurpose discs (DVDs), floppy disks, and Blu-ray® discs, where a "disk" typically reproduces data magnetically, while a "disc" reproduces data optically using a laser. Any combination of these is also included within the scope of computer-readable media.

[0315] The descriptions herein are provided to enable those skilled in the art to create or use this 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 modifications without departing from the scope of this disclosure. Accordingly, this disclosure is not limited to the examples and designs described herein, and should be given the broadest scope that is consistent with the principles and novel features disclosed herein.

Claims

1. A method for operating a device, wherein the method for operating the device is: The steps include: at least one processor of the device receiving physiological data associated with a user, wherein the physiological data is acquired via one or more sensors of a wearable device, and the physiological data includes at least body temperature data; The steps include: the at least one processor determining a time series of multiple body temperature values ​​acquired over multiple days, at least partially based on the body temperature data; A step in which the at least one processor determines the time series and identifies the temperature increase in the time series of the plurality of temperature values ​​relative to the user's temperature baseline, wherein the step of identifying the temperature increase in the time series is: Identifying one or more local maximums in a first portion of the time series, wherein the first portion includes a first subset of the plurality of body temperature values ​​corresponding to one or more menstrual cycles of the user, Identifying one or more local maxima of the second portion of the time series, wherein the second portion follows the first portion of the time series and includes a second subset of the plurality of body temperature values ​​corresponding to the onset of pregnancy. Comparing the one or more local maximums of the first part with the one or more local maximums of the second part, Based at least partially on the comparison, it is determined that the one or more local maximums of the second portion are greater than the one or more local maximums of the first portion. Steps including, A step of detecting an indicator of pregnancy for the user, at least in part on the fact that the at least one processor has determined that the one or more maximum values ​​of the second portion are greater than the one or more maximum values ​​of the first portion, wherein the indicator of pregnancy for the user is prior to a threshold increase in hormone elevation relative to the user's hormone baseline. The steps include: the at least one processor causing the graphical user interface of the user device to display the detected indicator of pregnancy; A method of operation, including the method of operation.

2. The said physiological data further includes heart rate data, and the said operating method is The step of the at least one processor further includes determining that during at least a portion of the multiple days, the heart rate data exceeds the user's non-pregnancy baseline heart rate, and the step of detecting the indicator of pregnancy is at least in part based on determining that the heart rate data exceeds the user's non-pregnancy baseline heart rate. The operating method according to claim 1.

3. The said physiological data further includes heart rate variability data, and the said operating method is, The step of the at least one processor further includes determining that during at least a portion of the multiple days, the heart rate variability data is below the user's non-pregnancy baseline heart rate variability, and the step of detecting the indicator of pregnancy is at least in part based on determining that the heart rate variability data is below the user's non-pregnancy baseline heart rate variability. The operating method according to claim 1.

4. The said physiological data further includes respiratory rate data, and the operating method is, The step of the at least one processor further includes determining that during at least a portion of the multiple days, the respiratory rate data exceeds the user's non-pregnancy baseline respiratory rate, and the step of detecting the indicator of pregnancy is at least in part based on determining that the respiratory rate data exceeds the user's non-pregnancy baseline respiratory rate. The operating method according to claim 1.

5. The step of at least one processor further includes identifying a cessation of the periodicity of the time series of the plurality of body temperature values, at least in part on determining the time series, and detecting the indicator of pregnancy, at least in part on identifying the cessation of the periodicity The operating method according to claim 1.

6. The at least one processor further includes the step of identifying a absence of a menstrual cycle, at least in part on determining the time series, wherein the step of detecting the indicator of pregnancy is performed prior to identifying the absence of the menstrual cycle. The operating method according to claim 1.

7. The step of receiving confirmation of pregnancy in response to the detection of the indicator of pregnancy by the user device by the at least one processor, wherein the step of detecting the indicator of pregnancy is performed prior to the confirmation of pregnancy. The operating method according to claim 1.

8. The at least one processor further includes the step of determining each of the plurality of body temperature values, at least in part, based on receiving the body temperature data, wherein the body temperature data includes continuous nighttime body temperature data. The operating method according to claim 1.

9. The step of the at least one processor updating a readiness score associated with the user, an activity score associated with the user, a sleep score associated with the user, or a combination thereof, at least in part on the detection of the indicators of pregnancy, The operating method according to claim 1.

10. The step of causing the at least one processor to display one or more pregnancy symptom tags on the graphical user interface of the user device associated with the user, at least in part on the detection of the indicators of pregnancy by the at least one processor, The operating method according to claim 1.

11. The at least one processor further includes the step of causing the graphical user interface of the user device associated with the user to display a message associated with the detected indicator of pregnancy, The operating method according to claim 10.

12. The message further includes the time interval in which the indicator of pregnancy was detected, a request to input one or more symptoms associated with the pregnancy, educational content associated with the pregnancy, a coordinated set of activity goals or a combination thereof, The operating method according to claim 11.

13. The at least one processor further includes the step of inputting the physiological data into a machine learning classifier, and the step of detecting the indicator of pregnancy is at least in part based on inputting the physiological data into the machine learning classifier. The operating method according to claim 1.

14. The wearable device includes a wearable ring device. The operating method according to claim 1.

15. The wearable device collects the physiological data from the user based on arterial blood flow. The operating method according to claim 1.

16. It is a device, Processor and The memory coupled to the aforementioned processor, Instructions stored in the memory and executable by the processor, the device The system receives physiological data associated with the user, the physiological data being acquired via one or more sensors of a wearable device, and the physiological data including at least body temperature data. Based at least partially on the aforementioned body temperature data, a time series of multiple body temperature values ​​acquired over several days is determined. Based at least in part on determining the aforementioned time series, the temperature increase in the aforementioned time series of the plurality of temperature values ​​relative to the user's temperature baseline is identified, where identifying the temperature increase in the aforementioned time series means Identifying one or more local maximums in a first portion of the time series, wherein the first portion includes a first subset of the plurality of body temperature values ​​corresponding to one or more menstrual cycles of the user, Identifying one or more local maxima of the second portion of the time series, wherein the second portion follows the first portion of the time series and includes a second subset of the plurality of body temperature values ​​corresponding to the onset of pregnancy. Comparing the one or more local maximums of the first part with the one or more local maximums of the second part, Based at least partially on the comparison, it is determined that the one or more local maximums of the second portion are greater than the one or more local maximums of the first portion. Includes, Based at least in part on the determination that the one or more maximum values ​​of the second portion are greater than the one or more maximum values ​​of the first portion, the system detects the user's indicator of pregnancy, and the user's indicator of pregnancy is prior to a threshold increase in the increase of hormones relative to the user's hormone baseline. The graphical user interface of the user device displays the detected indicators of pregnancy. Commands and, A device equipped with the following features.

17. A non-temporary computer-readable medium for storing code, wherein the code is an instruction executable by a processor, The system receives physiological data associated with the user, the physiological data being acquired via one or more sensors of a wearable device, and the physiological data including at least body temperature data. Based at least partially on the received body temperature data, a time series of multiple body temperature values ​​acquired over several days is determined. Based at least in part on determining the aforementioned time series, the temperature increase in the time series of the plurality of temperature values ​​relative to the user's temperature baseline is identified, where identifying the temperature increase in the time series means Identifying one or more local maximums in a first portion of the time series, wherein the first portion includes a first subset of the plurality of body temperature values ​​corresponding to one or more menstrual cycles of the user, Identifying one or more local maxima of the second portion of the time series, wherein the second portion follows the first portion of the time series and includes a second subset of the plurality of body temperature values ​​corresponding to the onset of pregnancy. Comparing the one or more local maximums of the first part with the one or more local maximums of the second part, Based at least partially on the comparison, it is determined that the one or more local maximums of the second portion are greater than the one or more local maximums of the first portion. Includes, Based at least in part on the determination that the one or more maximum values ​​of the second portion are greater than the one or more maximum values ​​of the first portion, an indicator of pregnancy for the user is detected, and the indicator of pregnancy for the user is prior to a threshold increase in the increase of hormones relative to the user's hormone baseline. The graphical user interface of the user device displays the detected indicators of pregnancy. A non-temporary computer-readable medium containing instructions for use.

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