WO2022056306A1 - Systems and methods of identifying physiologic changes utilizing ultradian rhythms - Google Patents

Systems and methods of identifying physiologic changes utilizing ultradian rhythms Download PDF

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Publication number
WO2022056306A1
WO2022056306A1 PCT/US2021/049939 US2021049939W WO2022056306A1 WO 2022056306 A1 WO2022056306 A1 WO 2022056306A1 US 2021049939 W US2021049939 W US 2021049939W WO 2022056306 A1 WO2022056306 A1 WO 2022056306A1
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Prior art keywords
vasomotor
event
ovulation
ultradian
event determination
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PCT/US2021/049939
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French (fr)
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Wade W. Webster
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Webster Wade W
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Publication of WO2022056306A1 publication Critical patent/WO2022056306A1/en

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
    • A61B10/0012—Ovulation-period determination
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
    • A61B10/0012—Ovulation-period determination
    • A61B2010/0016—Ovulation-period determination based on measurement of electric currents, e.g. conductivity tests
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
    • A61B10/0012—Ovulation-period determination
    • A61B2010/0019—Ovulation-period determination based on measurement of temperature
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
    • A61B10/0012—Ovulation-period determination
    • A61B2010/0029—Ovulation-period determination based on time measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
    • A61B10/0012—Ovulation-period determination
    • A61B2010/0032—Ovulation-period determination based on measurement of pH-value

Definitions

  • the present invention may relate to identification, prediction, and even confirmation of physiological changes which may be used for vasomotor events, ovulation, and the like.
  • Ultradian events as opposed to circadian rhythms, are short-term rhythms that have been observed since the beginning of modern biology and were quantified about a century ago. Recently, ultradian rhythms have moved to the forefront of chronobiology. Ultradian rhythms are ubiquitous in all biological systems and found in all organisms, from unicellular organisms to mammals, and from single cells to complex biological functions in multicellular animals. Their origin may be unclear but appear to be molecular in origin and could be controlled by hormonal inputs — in vertebrates, they originate from the activity of the central nervous system as patterns of neuropeptide release.
  • Ultradian rhythms may be characterized by variable periods ranging from about 20 min to a few hours, durations of a few minutes to several hours, and even amplitudes that can be quite small compared to the amplitude of other biological rhythms. Because of their aperiodic nature, specific sampling, analytic tools and procedures may be used in the analysis of time series rhythms at the population and biologic system level. Wearable technologies that can provide frequent sampling with good discriminatory power are revolutionizing digital health in unexpected ways.
  • ultradian rhythms observed in core and peripheral body temperature may be coupled with ultradian peaks in heart rate variability power, heart rate, locomotion and activity.
  • the core body temperature may be intimately linked to several endocrine axes with the reproductive system as an excellent example of ultradian rhythm coupled to circadian rhythm and even core body temperature.
  • High temporal resolution recordings of body temperature, heart rate, and even heart rate variability may demonstrate predictive power for ovarian activity and a predictor of pregnancy outcome. Coupling the synchronization of reproductive activity with environmental changes and even cycles has the potential to optimize reproductive success.
  • wearables can be useful tools to understand reproductive disorders, predicting fertility and infertility in mammals, including humans and livestock as well as predictive for physiologic monitoring and disease processes.
  • Certain physiological changes such as but not limited to ovulation, vasomotor events, hot flashes, menopause, drug reactions, post-traumatic stress, and the like can be difficult to predict, confirm, monitor, and the like.
  • levels of estrogen and progesterone start to fall in females, and perimenopause — the transition to menopause — begins.
  • perimenopause the transition to menopause — begins.
  • vasomotor symptoms may be those that occur due to the constriction or dilation of blood vessels. They can include hot flashes, night sweats, heart palpitations, changes in blood pressure, and the like. The most likely reason why these symptoms can occur during menopause is that hormonal fluctuations affect the mechanisms that control blood pressure and control.
  • a person may enter menopause about 12 months after their last period. Hot flashes and other symptoms can start during perimenopause, while menstruation is still occurring, or they may begin after a person’s periods end. Not everyone has these symptoms, and they can vary in severity among individuals. Some people may start menopause earlier in life. In some cases, this may happen naturally, but in others, it may be due to surgery, a health condition, or certain types of medical treatment. There is a need for a technology that can predict, identify, and even confirm vasomotor events.
  • vasomotor events such as vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, or the like may want to track such events perhaps for further analysis including discussions with others, research, discussions with doctors, or the like.
  • vasomotor events may be difficult to track vasomotor events unless one takes their own vitals (heart rate, temperature, or the like) and records them themselves. It is also difficult to track vasomotor events during sleep.
  • Hormone replacement therapy for those going through menopause or the like have risks associated including heart attacks, strokes, breast cancer, heart disease, and the like. However, depending on when a hormone replacement therapy begins, those risks may decrease.
  • the present invention includes a variety of aspects, which may be selected in different combinations based upon the particular application or needs to be addressed.
  • the embodiments disclosed herein may include identification of physiological changes which may be used in various applications. It is an object of some embodiments to provide prediction or even identification of a physiological change relating to a vasomotor event.
  • Another object of some embodiments may utilize a wearable device to help accurately and easily measure vasomotor events.
  • FIG. 1 shows a schematic diagram for determination of vasomotor events and prediction of ovulation in accordance with some embodiments.
  • FIG. 2 shows a non-limiting example of the minute by minute changes in heart rate variability during a hot flash in accordance with some embodiments.
  • FIG. 3 shows a non-limiting example of raw daytime distal body temperature data in accordance with some embodiments.
  • FIG. 4 shows a non-limiting example of scored ultradian power of distal body temperature to anticipate LH surge onset in accordance with some embodiments.
  • FIG. 5 shows a non-limiting example of raw nighttime heart rate variability data in accordance with some embodiments.
  • FIG. 6 shows a non -limiting example of scored ultradian power of heart rate variability to anticipate LH surge onset in accordance with some embodiments.
  • Embodiments of the application may provide determination of certain physiological events including but not limited to vasomotor events, ovulation, drug effectiveness, diagnosis, delivery, ovulation, vasomotor symptoms or the like perhaps based on information obtained from the human body.
  • Determination including identification, prediction, and even confirmation of physiological changes may be evaluated from physiology measurements such as but not limited to: ultradian rhythms of core body temperature; ultradian rhythms of distal body temperature; ultradian rhythms of heart rate; ultradian rhythms of heart rate variability; or the like; and any combination or permutation thereof.
  • Further physiology measurements may include, but is not limited to high frequency peripheral temperature measurement, actigraphy, acoustic monitoring; internal metabolic monitoring such as measuring electrical impedance of cervical mucus, pH, water content, or the like; or the like, and in any combination or permutation thereof.
  • An ultradian rhythm may be a recurrent period or even cycle repeated throughout an about 24-hour day, e.g., about 1 to about 4 hours.
  • circadian rhythms can be completed in one cycle daily, while infradian rhythms such as the human menstrual cycle may have periods longer than a day.
  • Ultradian rhythms may be identified in body temperature, heart rates, heart rate variability, and may even be coordinated with a menstrual cycle, perimenopause, and menopause or the like.
  • Patterns of neuropeptides and even hormones may be manifested in changes to autonomic central nervous system control and even metabolic systems. Hence further manifestation in change to temperature, heart rate, and even heart rate variability may be seen through their respective ultradian rhythms and may even be described mathematically. For ovulation, this phenomenon may be exhibited in parasympathetic dominance in the follicular phase and even sympathetic dominance in the luteal phase. Assessment of ultradian rhythms may identify perimenopausal transition and even menopause.
  • Amplitude and even frequency of ultradian rhythms may change over the course of a human’s life, perhaps due to treatments, perimenopause, menopause, medicine, menstrual cycle or the like perhaps that an inflection point and subsequent peak of peripheral temperature measurement, heart rate, and even heart rate variability ultradian power may anticipate vasomotor events, ovulation, or the like.
  • ultradian frequencies of temperature, heart rate, and even heart rate variability perhaps when compared to each other may strengthen along with amplitude and/or phase change leading up to ovulation and may destabilize in the luteal phase. These may be used to predict ovulation or even confirm pregnancy. This may be instead of using the traditional basal body temperature (possibly at once per day), of using time series analysis of daily body temperature patterns, or even instead of using analysis of infradian nadirs.
  • Frequency analysis of ultradian rhythm data may be performed by any number of mathematical methods including by way of non-limiting examples: wavelets, analysis of variance, spectral analysis (Fourier), and the like.
  • Timeseries analysis of measurements of temperature, heart rate, and even heart rate variability ultradian frequencies may enable endocrine status assessment. Timeseries analysis of ultradian frequencies perhaps of body temperature measured peripherally such as combined with timeseries analysis of ultradian frequencies of heart rate and even heart rate variability can be further combined with internal body temperature and even actigraphy may provide a greater understanding of humans, males, females, female reproduction, and the like such as but not limited to, physiological strain index, symptoms of menopause, sepsis prediction, sepsis confirmation, drug effect, confirmation of infectious disease processes including, but not limited to bacterial, viral or fungal acute, subacute or chronic (e.g., optimal time for phlebotomy for blood culture or identification of infectious agent), and the like. This may help to understand and even identify and predict events of physiological changes, female reproduction, ovulation, defining a window of fertility, and the like.
  • the exemplary embodiments described herein may be used as methods and systems to determine including predict and even confirm: pharmacologic effect; acute of chronic neurologic disease states (e.g., impending seizure); impending immunologic event; acute of chronic cardiogenic change; sleep disorders; endocrinologic disorders (e.g., acute or even chronic); heat illness or even heat stroke; starvation; a change in immunologic statis (e.g., confirmation of vaccine response); a window for therapeutic administration of a pharmacologic agent; and perhaps even a psychiatric condition (e.g., acute or chronic), in any combination or permutation, and the like.
  • This list is non-exhaustive and meant as exemplary only.
  • High temporal resolution data gathering may characterize “normal” physiologic change and even normal heterogeneity perhaps corresponding with physiological relevance in understanding a heterogeneity of patterns across populations such as due to age, genetic background, diet, health, environment, time of year, and the like.
  • Some embodiments of the application may provide a process for determination of vasomotor events for a user comprising the steps of periodically sensing ultradian rhythms (1) of said user (2); automatically accepting a data input (5) to a computer (4) based at least in part on said step of periodically sensing said ultradian rhythms; establishing in said computer a vasomotor event determination model automated vasomotor event computational transform program (6) with starting vasomotor event transformation parameters (7); automatically applying said vasomotor event determination model automated vasomotor event computational transform program with said starting vasomotor event transformation parameters, to at least some of said data input to automatically create a vasomotor event determination model data transform (8); generating a vasomotor event determination model completed vasomotor event determination output (9) based on said vasomotor event determination model data transform; and perhaps even providing a vasomotor event indication (10) based on said step of generating said vasomotor event determination model completed vasomotor event determination output.
  • Systems may include a computerized vasomotor events determination system comprising: a periodic capture ultradian rhythm sensor (3) for placement in contact with a user (2); a computer data input (5) from said periodic capture ultradian rhythm sensor; a computer processor operated vasomotor event determination model automated vasomotor event computational transform program (6) with starting vasomotor event transformation parameters (7) responsive to said computer data input from said periodic capture ultradian rhythm sensor; a vasomotor event determination model data transform (8) generated from said computer processor operated vasomotor event determination model automated vasomotor event computational transform program with said starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor; a vasomotor event determination model transformed completed vasomotor event determination output (9) generated from said vasomotor event determination model data transform; and perhaps even a vasomotor event indication (10) based on said vasomotor event determination model transformed completed vasomotor event determination output.
  • a computerized vasomotor events determination system comprising: a periodic capture ultradian rhythm sensor (3) for
  • Vasomotor events of a user (2) may be determined by periodically sensing ultradian rhythms (1) perhaps with a periodic capture ultradian rhythm sensor (3) which may be placed in contact with a user (2).
  • Ultradian rhythms (1) may include ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, or the like, and any combination thereof.
  • a vasomotor event (12) may include vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, or the like, and any combination thereof.
  • a computer (4) may automatically accept a data input (5) which may be based on data from at least in part the sensing of ultradian rhythms perhaps from a periodic capture ultradian rhythm sensor. Such sensing may be automatically perhaps with an automatic periodic capture ultradian rhythm sensor. In some embodiments, ultradian rhythms may be continuously sensed perhaps to provide a continuous capture ultradian rhythm sensor.
  • a computer may be any device that can be programmed to carry out sequences of arithmetic or logical operations automatically such as but not limited to a desktop, laptop, smart phone, application, or the like.
  • a computer may include or be established with a computer processor operated vasomotor event determination model automated vasomotor event computational transform program (6) perhaps with starting vasomotor event transformation parameters (7) which may be responsive to computer data input (5) from a periodic capture ultradian rhythm sensor.
  • Embodiments may provide automatically applying the vasomotor event determination model automated vasomotor event computational transform program with the starting vasomotor event transformation parameters, to at least some of the data input to automatically create a vasomotor event determination model data transform (8).
  • a vasomotor event determination model completed vasomotor event determination output 9 may be generated.
  • a vasomotor event indication (10 may be provided.
  • a user may provide user input (11) to which a vasomotor event determination model completed vasomotor event determination output or even ovulation event prediction model completed ovulation event prediction output may be responsive.
  • a user may be a male or female or the like.
  • User input (11) may include a user’s symptoms, signs of depression, vaginal dryness, libido, memory function, family history, if the user smokes, if the user consumes alcohol, or the like, and any combination or permutation thereof. This information may be valuable in the analysis of the ultradian rhythm data and even a resulting output.
  • a vasomotor event determination model completed vasomotor event determination output may be based on a plurality of vasomotor events (12) from a user.
  • An indication such as vasomotor event indication (10) may include an identification of an event such as a vasomotor event or even a prediction of a future event such as a vasomotor event.
  • a vasomotor event determination model completed vasomotor event determination output (9) may include a a vasomotor event prediction model completed vasomotor event prediction output or even a vasomotor event identification model completed vasomotor event identification output.
  • Sensing of a user’s ultradian rhythms may be made more convenient and easier with a wearable sensing system of which a user can wear a sensor (3) on or even in their body.
  • a wearable sensing system can measure user data such as but not limited to heart rate, distal body temperature, heart rate variability, sleep, or the like.
  • a sensing system may be may be any kind of sensor including but not limited to a ring perhaps worn on a user’s finger, a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, a sensor worn on a finger, or the like.
  • Signal monitoring and even data transmission may be achieved to a computer via Bluetooth or the like.
  • data input may include time series information of ultradian rhythms.
  • Starting vasomotor event transformation parameters (7) may include quantifying of data input to provide quantified ultradian rhythms.
  • Analyzing data input may include data analysis, signal processing, analysis of variance, spectral analysis, Fourier transform, time series analysis, wavelet transformations, power wavelet transformations, or the like, of computer data input and any combination thereof.
  • Embodiments of the present invention may include artificial intelligence systems such as discussed in WO 2020/013830 to Prima-Temp, Inc., hereby incorporated by reference herein. Sensing and analysis of ultradian rhythms for determination with vasomotor events or even for prediction of ovulation may utilize artificial intelligence.
  • Artificial intelligence embodiments of the application include a process for determination of vasomotor events for a user comprising the steps of periodically sensing ultradian rhythms of said user; automatically accepting a data input to a computer based at least in part on said step of periodically sensing said ultradian rhythms; establishing in said computer a first vasomotor event determination model automated vasomotor event computational transformation program with starting vasomotor event transformation parameters; automatically applying said first vasomotor event determination model automated vasomotor event computational transformation program with said starting vasomotor event transformation parameters, to at least some of said ultradian rhythms to automatically create a first vasomotor event determination model data transform; generating a first vasomotor event determination model completed vasomotor event determination output based on said first vasomotor event determination model data transform; automatically varying said starting vasomotor event transformation parameters for said first vasomotor event determination model automated vasomotor event computational transformation program to establish a second vasomotor event determination model automated vasomotor event computational transformation program (15) that differs from said first
  • a computerized vasomotor event determination system may include: a periodic capture ultradian rhythm sensor for placement in contact with a user; a computer data input from said periodic capture ultradian rhythm sensor; a computer processor operated first vasomotor event determination model automated vasomotor event computational transformation program with starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor; a first vasomotor event determination model data transform generated from said computer processor operated first vasomotor event determination model automated vasomotor event computational transformation program with said starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor; a first vasomotor event determination model transformed completed vasomotor event determination output generated from said first vasomotor event determination model data transform; a computer processor operated varied automated vasomotor event computational transformation program (21) configured to generate automated varied vasomotor event transformation parameters; a computer processor operated second vasomotor event determination model automated vasomotor event computational transformation program generated from said automated varied vasomotor event transformation parameters and which differs from said computer
  • Artificial intelligence embodiments of the application include a process for prediction of the onset of ovulation for a user comprising the steps of: periodically sensing ultradian rhythms for said user; automatically accepting a data input to a computer based at least in part on said step of periodically sensing said ultradian rhythms; establishing in said computer a first ovulation prediction model automated ovulation computational transformation program with starting ovulation transformation parameters; automatically applying said first ovulation prediction model automated ovulation computational transformation program with said starting ovulation transformation parameters, to at least some of said ultradian rhythms to automatically create a first ovulation prediction model data transform; generating a first ovulation prediction model completed ovulation prediction output based on said ovulation prediction model data transform; automatically varying said starting transformation parameters for said first ovulation prediction model automated ovulation computational transformation program to establish a second ovulation prediction model automated ovulation computational transformation program that differs from said first ovulation prediction model automated ovulation computational transformation program in the way that
  • a computerized ovulation prediction system may include: a periodic capture ultradian rhythm sensor for placement in contact with user; a computer data input from said periodic capture ultradian rhythm sensor; a computer processor operated first ovulation prediction model automated ovulation computational transformation program with starting ovulation transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor; a first ovulation prediction model data transform generated from said computer processor operated first ovulation prediction model automated ovulation computational transformation program with said starting ovulation transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor; a first ovulation prediction model transformed completed ovulation prediction output generated from said first ovulation prediction model data transform; a computer processor operated varied automated ovulation computational transformation program configured to generate automated varied ovulation transformation parameters; a computer processor operated second ovulation prediction model automated ovulation computational transformation program generated from said automated varied ovulation transformation parameters and which differs from said computer processor operated first ovulation prediction model automated ovulation computational transformation program in the way
  • Automated varied vasomotor event transformation parameters may be automatically cumulatively varied vasomotor event transformation parameters generated from previously determined vasomotor event transformation parameters.
  • a comparator may be responsive to a plurality of events such a vasomotor events or a plurality of ovulation events.
  • An ovulation event may be a preovulatory change in reproductive hormones, a luteinizing hormone surge, a release of an egg from an ovary, or the like.
  • FIG. 1 represents a schematic diagram for determination of vasomotor events and prediction of ovulation in accordance with some embodiments including a user (2), ultradian rhythms (1) of a user, vasomotor events (12), a sensor (3), user input (11), data input (5), a computer (4), an event determination model transform program (20), a transformation program (6), transformation parameters (7), data transform (8), an output (9), an indication (10), a second transformation program (15), varied transformed program (21), varied transformation parameters (16), a second data transform (17), a second output (18), stored improved parameters (19), a comparator (20), and the like.
  • FIG. 2 shows a non-limiting example of the minute by minute changes in high frequency heart rate variability during a hot flash.
  • FIG. 3 shows a non-limiting example of raw daytime distal body temperature data in Celcius at wake up, 4 hours, 8 hours, 12 hours, and 16 hours.
  • FIG. 4 shows a non-limiting example of scored ultradian power of distal body temperature versus days relative to LH surge onset to anticipate LH surge onset.
  • FIG. 5 shows a non-limiting example of raw nighttime heart rate variability data (root mean square of successive differences between normal heartbeats) at sleep, 4 hours, and 8 hours.
  • FIG. 6 shows a non-limiting example of scored ultradian power of heart rate variability (root mean square of successive differences between normal heartbeats) versus days relative to LH surge onset to anticipate LH surge onset.
  • another aspect of embodiments of the invention can be the way the system processes data to achieve its purpose. Processing of data may be by software and or firmware, and for systems can be configured in a variety of ways and at a variety of locations. Devices and capabilities can be spread throughout the system as well. For example, in some embodiments the system can involve three major components. Each of these components can be configured as a discrete processor, a programmed dedicated processor, an ASIC, firmware, a device having programmable processing capability, a smart phone, a multipurpose computer, a server, or even internet or cloud computing capability.
  • a type of computer processor perhaps considered even any app processor may be used. This processor can be quite programmable and the identification as an app processor may only distinguish its location.
  • a device can execute a program, perhaps considered an application program or app, to achieve some type of operation. This and perhaps other computer processors) can achieve data, capture, data storage, user input, or other operations. It can also transmit results, data, or other information to be able to interact with another processing capability.
  • server One of the other components of such an embodiment of the system can be termed server. It can just be a generally more capable or more available computer capability.
  • the server can also include general programmable capabilities and it may be or include a multipurpose programmable computer or processor. Communication can occur in standard fashions.
  • the programmable or configurable capabilities or components in server can include yet another computer processor which may be termed server processor to distinguish it main location as above. It can also access and interact with an internal or outside capability. This outside capability may be a memory.
  • the resource can be a cloud storage capability, a cloud computing capability or the like. Again, memory and even processing capabilities can be distributed at various locales as is known to those in the art.
  • a computer processor of a computer can be understood as coupled to data memory in a manner that uses those values to achieve its programmed purpose.
  • data processing, data pass-through, and/or data storage can occur at any location.
  • only limited activity might occur in a sensor.
  • Embodiments may provide temporary data storage, limited processing activity, and data transmission capability from a sensor. Somewhat more complex activity can occur in a sensor.
  • the most complex activity and most in-depth data storage - such as for multi users - can occur or be achieved at computer or server.
  • Data input may be relatively noisy.
  • embodiments can use event transformation parameters perhaps to smooth the data which may be a functionality that is designed to endeavor to remove fluctuations from the actual values.
  • a type of programming, firmware, ASIC, or routine could remove the fluctuations from use in achieving a data transform.
  • Literal removal of data can occur by removing activity related data.
  • a system can be configured by programming, firmware, of use of an ASIC to automatically transform data to provide a first or second transformation computation generated output.
  • a system can automatically transform perhaps (but not necessarily) the same sensor computer data input accessed values through a second transformation computation and use a second output generator to achieve, more generally, a second transformation computation generated output.
  • Two types of outputs can be compared and, depending upon the needs, one or the other can be chosen as the one that is more likely to provide a desired indication of the likely existence of an event.
  • a system can be configured to provide indications that are peculiarly helpful or desirable to different particular users or needs. From the general ability to compare and select among different models or transformations, it can be understood that a system can be extended or use different configuration to include an ability to self-improve. The aspect of having a system that can automatically self-improve is particularly useful in the context of predicting or identifying an event.
  • a system can be configured to use its data perhaps in conjunction with other users’ data or perhaps prior data for just that user to provide automatically enhanced and improved prediction routines. For example, in an instance of using a variable range of values such as for a running average process, it can be understood that the data transformation can be achieved through the application of transformation parameters.
  • Parameters can be considered perhaps the value(s) that represent the ranges of data points over which the running average could be calculated.
  • Such transformation parameters can be applied to the automatic data transform, and can be varied such as from a starting value (initially or at any reapplication of the process) for application such as by a transformation parameter vary routine.
  • a starting value initially or at any reapplication of the process
  • different results can be achieved.
  • these results can be compared automatically based on default or even user input such as by an automatic transformed comparator.
  • Some types of ultimate or intermediate outputs can be made available automatically for comparison, or inclusion through combination of multiple transformations.
  • the output can be considered an automatic transformed output regardless of the stage in the transformation or recalculation from which it is derived.
  • transformation parameters By storing transformation parameters in a transformation parameter memory, these parameters can be made available for later use, later variation, and even cumulative adjustment.
  • the system can be considered as applying varied transformation parameters such as to achieve a varied data transform. There can be a starting data transform and then as a result of varying parameters, a varied data transform and this process can happen automatically.
  • This varied data transformation can provide an output that is used by an automatic varied transformation outfit generator.
  • such a system can have an automatic transform output comparator to allow decisions to be made based on the varied parameters applied.
  • a self-improving process with a number of transformations can be considered as establishing an automated computational transform program with starting transformation parameters that may be at the beginning of any improvement process be it cumulative of just beginning initiation.
  • Such starting transformation parameters can be applied to at least some of the data values to automatically create a starting data transform.
  • This starting data transform can generate an output which can then be compared.
  • a comparison can be achieved by varying the starting transformation parameters to achieve a varied automated computational transformation which can be similarly applied to at least a portion of the body temperature values then available.
  • Such a system can be configured to automatically compare the starting transformed prediction output, perhaps such as with the varied transform prediction output, to determine which of these is likely to provide a more aligned indication of the likely existence of an event.
  • the selected parameters can then be stored and used perhaps as the next re-established starting transformation parameters so that continued system learning and improvement can build on itself by further revising and re-establishing desired parameters.
  • These parameters can be cumulatively varied so that the system builds on itself as mentioned above. Cumulative variation can be achieved by using then-available data that includes some labeling or other indication, perhaps through user input to assess what is likely most accurate. Determinations can also occur automatically even with computer determined weighting so that in some embodiments more recent data or perhaps more applicable data can be weighted heavier than other data. In general, cumulative improvement can be achieved by inclusion of a cumulative transformation parameter vary routine.
  • systems can include a neural network architecture capable of incorporating data and other indications, perhaps such as discrete user data, user determined activity or occurrence data, clinical data, test data, LH test data, or the like, to allow improved identification or predictions of likely events or to allow linkage to user preferences.
  • Processing functions can take values as input arguments and output identifications or predictions.
  • embodiments of the system can allow the computer to iteratively tune the weights in the neural network in such a way as to minimize an error function.
  • the error function may be any function of the difference between the neural network output and user information such as test results.
  • the system can automatically continue its iterative weight tuning process (called “training”) to produce preferable event indications or predictions on subsequent user events to yield a lower error as judged by the error function.
  • training iterative weight tuning process
  • the parameters utilized can be simplistic or complex. More simplistic parameters can be considered as parameters like weights, ranges, coefficients, and other, perhaps linear, parameters.
  • the parameters can be more complex and even non-linear. These can even include parameters that completely vary the entire nature of the transformation and recalculation itself. Regardless whether simplistic or complex, variation in parameters can consider or react to a user input. The system can make recommendations and even suggest alterations to or as a result of this input. Furthermore, a user can even have the option of providing a user input to which transformations, comparisons, and ovulation prediction outputs can be responsive.
  • the system may be considered as including a multiple, a plurality, and any number of computer processor operated automatic data transform calculators. Each of these can be configured to apply variable transformation parameters. Further, embodiments of the system can use the multiple transformation calculations either in the alternative or as a composite way to provide a desired output. In embodiments that combine or create a composite to provide a desired output, the system can include an automatic transform combiner. This automatic transform calculator combiner can be responsive to a plurality of automatic transform calculators which, again, may be identical capabilities that apply different parameters or may be entirely differently programmed recalculators.
  • a system can include a transformation range size vary routine, a transformation range drift vary routine, a transformation threshold inclusion vary routine, a transformation coefficient vary routine, and/or a transformation weight vary routine.
  • a transformation range size vary routine can include a transformation range drift vary routine, a transformation threshold inclusion vary routine, a transformation coefficient vary routine, and/or a transformation weight vary routine.
  • weight vary routines can show how a composite output can be used with any number of transformations and any number of parameter variations with each having its own weight assigned to a total calculation or prediction output.
  • Weighting of differing transforms can be particularly useful with consideration of the user input whereby user conditions that existed at that particular time can be applied or even removed to more appropriately achieve a prediction and perhaps even more appropriately apply then available multi user data to the situation then existing.
  • embodiments of the invention can involve a decision, identification, or prediction output that can be made on a combination of differing transforms, and a composite of various transforms.
  • an embodiment can involve a transform T, such as a first transform (Ti), a second transform (T2), up to an n th transform (T n ) which can each be fundamentally different and can apply one or many persons’ data.
  • each transform can have its own coefficient (constant, look up, function, or otherwise) to indicate any scaling such as for a particularly applicable factor (demographic, age, etc.) for that transform (ci, C2, c n so as to have ciTi, C2T2, c n T n ) its own non-linear factor (indicated as a superscript, or power but not to be limited to such a mathematical process, CnTn 1 , c n T n 2 , c n T n m ), and a weight (wi, W2, ii’i, so as to have widTi, W2C2 2, WnCnTn), and these can be used individually, in the alternative, or summed, added (widTi + W2C2 2 + .
  • weighting can even include zero weighting - meaning that that one transform is effectively removed from the process - any number of transformed recalculations, from one to many, can be included in a more general embodiment of the system.
  • Such a composite can be interactively varied and evaluated to result in a continually self-improved system such as can be considered an instance of an artificial intelligence system or even a neural network based Al system as should be readily understood or separately available to a person of ordinary skill in the art.
  • One aspect that is particularly interesting for a prediction is the aspect of determining which result is the most optimal. This can be a challenge for an aspect such as predicting or onset sensing something as complex as ovulation or the like which can be difficult to sense or know with certainty prior to its actual occurrence and which may be accompanied by discernable indicia only after the fact.
  • optimal-ness can be determined based upon a user selection or the like.
  • the aspect of being optimal such as perhaps being earliest or perhaps most accurate, etc. can be achieved by comparison to a variety of data. This data can be a user input perhaps such as, but not limited to, a user’s input of physical symptoms indicating that ovulation or another event is occurring.
  • the data can also be a variety of other types of input perhaps, a prior computer input, a luteinizing hormone test computer input, a fertility test result computer input, a user menstrual cycle computer input, a user body type computer input, a user physical condition computer input, a user medical history computer input, a user text message computer input, and even a plurality of these various inputs.
  • a process for determination of vasomotor events for a user comprising the steps of:
  • vasomotor event determination model automated vasomotor event computational transform program with starting vasomotor event transformation parameters
  • vasomotor event determination model automated vasomotor event computational transform program with said starting vasomotor event transformation parameters, to at least some of said data input to automatically create a vasomotor event determination model data transform
  • vasomotor event determination model completed vasomotor event determination output based on said vasomotor event determination model data transform
  • vasomotor event indication based on said step of generating said vasomotor event determination model completed vasomotor event determination output.
  • a process as described in clause 1 or any other clause wherein said step of generating a vasomotor event determination model completed vasomotor event determination output based on said vasomotor event determination model data transform comprises a step of generating a vasomotor event determination model completed vasomotor event determination output based on said vasomotor event determination model data transform and a plurality of vasomotor events.
  • said ultradian rhythms is chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof.
  • vasomotor event is chosen from vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, and any combination thereof.
  • vasomotor event indication is chosen from an identification of a vasomotor event and a prediction of a future vasomotor event.
  • vasomotor event determination model completed vasomotor event determination output is chosen from a vasomotor event prediction model completed vasomotor event prediction output and a vasomotor event identification model completed vasomotor event identification output.
  • said step of periodically sensing ultradian rhythms for said user comprises a step of automatically sensing ultradian rhythms for said user.
  • said step of automatically sensing ultradian rhythms for said user comprises a wearable sensing system.
  • said wearable sensing system measures user data chosen from heart rate, distal body temperature, and heart rate variability.
  • a process as described in clause 9 or any other clause wherein said wearable sensing system comprises a ring worn on said user’s finger.
  • a process as described in clause 9 or any other clause wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger.
  • said data input based at least in part on said step of periodically sensing said ultradian rhythms comprises time series information of said ultradian rhythms.
  • said starting vasomotor event transformation parameters comprises quantifying said data input to provide quantified ultradian rhythms.
  • a computerized vasomotor events determination system comprising:
  • vasomotor event determination model automated vasomotor event computational transform program with starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor
  • vasomotor event determination model data transform generated from said computer processor operated vasomotor event determination model automated vasomotor event computational transform program with said starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
  • vasomotor event determination model transformed completed vasomotor event determination output generated from said vasomotor event determination model data transform
  • vasomotor event indication based on said vasomotor event determination model transformed completed vasomotor event determination output.
  • vasomotor event determination model transformed completed vasomotor event determination output generated from said vasomotor event determination model data transform comprises vasomotor event determination model transformed completed vasomotor event determination output generated from said vasomotor event determination model data transform and based on a plurality of vasomotor events.
  • said ultradian rhythm sensor is capable of capturing ultradian rhythms chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof.
  • vasomotor event is chosen from vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, and any combination thereof.
  • vasomotor event indication is chosen from an identification of a vasomotor event and a prediction of a future vasomotor event.
  • vasomotor event determination model completed vasomotor event determination output is chosen from a vasomotor event prediction model completed vasomotor event prediction output and a vasomotor event identification model completed vasomotor event identification output.
  • a system as described in clause 24 or any other clause wherein said automatic periodic capture ultradian rhythm sensor comprises a wearable sensing system.
  • said wearable sensing system is configured to measure user data chosen from heart rate, distal body temperature, and heart rate variability.
  • said wearable sensing system comprises a ring worn on said user’s finger.
  • said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger.
  • a system as described in clause 17 or any other clause wherein said computer data input from said periodic capture ultradian rhythm sensor comprises time series information of a user’s ultradian rhythms.
  • said starting vasomotor events transformation parameters comprise quantified ultradian rhythm data.
  • said starting vasomotor events transformation parameters comprises data analysis of said computer data input, signal processing of said computer data input, variance analysis of said computer data input, spectral analysis of said computer data input, Fourier transform of said computer data input, time series analysis of said computer data input, wavelet transformations of said computer data input, power wavelet transformations of said computer data input, and any combination thereof.
  • said periodic capture ultradian rhythm sensor comprises a continuous capture ultradian rhythm sensor.
  • vasomotor event indication based on said step of automatically determining whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide said existence of said vasomotor event;
  • step of automatically varying said starting vasomotor event transformation parameters for said first vasomotor event determination model automated vasomotor event computational transformation program to establish a second vasomotor event determination model automated vasomotor event computational transformation program that differs from said first vasomotor event determination model automated vasomotor event computational transformation program in the way that it determines vasomotor event from data comprises the step of automatically cumulatively varying previously applied vasomotor event transformation parameters for said automated vasomotor event computational transformation program to establish a varied automated vasomotor event computational transformation program.
  • a process as described in clause 33 or any other clause wherein said step of automatically determining whether said first vasomotor event determination model completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide the existence of a vasomotor event comprises the step of automatically applying said first vasomotor event determination model completed vasomotor event determination output and said different, second vasomotor event determination model transformed completed vasomotor event determination output to a plurality of vasomotor events.
  • said ultradian rhythms is chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof.
  • said vasomotor event is chosen from vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, and any combination thereof.
  • vasomotor event indication is chosen from an identification of a vasomotor event and a prediction of a future vasomotor event.
  • said first vasomotor event determination model transformed completed vasomotor event determination output is chosen from a first vasomotor event prediction model completed vasomotor event prediction output and a first vasomotor event identification model transformed completed vasomotor event identification output and wherein said different, second vasomotor event determination model transformed completed vasomotor event determination output is chosen from a different, second vasomotor event prediction model transformed completed vasomotor event prediction output and a different, second vasomotor event identification model transformed completed vasomotor event identification output.
  • a process as described in clause 33 or any other clause wherein said step of periodically sensing ultradian rhythms for said user comprises a step of automatically sensing ultradian rhythms for said user.
  • a process as described in clause 41 or any other clause wherein said step of automatically sensing ultradian rhythms for said user comprises a wearable sensing system.
  • said wearable sensing measures user data chosen from heart rate, distal body temperature, and heart rate variability.
  • said wearable sensing system comprises a ring worn on said user’s finger.
  • a process as described in clause 42 or any other clause wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger.
  • said data input based at least in part on said step of periodically sensing said ultradian rhythms comprises time series information of said ultradian rhythms.
  • said starting vasomotor event transformation parameters comprises quantifying said data input to provide quantified ultradian rhythms.
  • said step of periodically sensing ultradian rhythms of said user comprises a step of continuously sensing ultradian rhythms of said user.
  • a computerized vasomotor event determination system comprising:
  • vasomotor event determination model data transform generated from said computer processor operated first vasomotor event determination model automated vasomotor event computational transformation program with said starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
  • vasomotor event transformation comparator responsive to said first vasomotor event determination model transformed completed vasomotor event determination output and said different, second vasomotor event determination model transformed completed vasomotor event determination output and configured to automatically determine whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide an existence of a vasomotor event;
  • vasomotor event indication based on said automatic determination of whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to said existence of said vasomotor event;
  • said ultradian rhythm sensor is capable of capturing ultradian rhythms chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof.
  • said vasomotor event is chosen from vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, perimenopause, menopause, drug reactions, post-traumatic stress disorder, and any combination thereof.
  • vasomotor event indication is chosen from a detection of a vasomotor event and a prediction of a future vasomotor event.
  • first vasomotor event determination model transformed completed vasomotor event determination output is chosen from a first vasomotor event prediction model transformed completed vasomotor event prediction output and a first vasomotor event identification model transformed completed vasomotor event identification output and wherein said different, second vasomotor event determination model transformed completed vasomotor event determination output is chosen from a different, second vasomotor event prediction model transformed completed vasomotor event prediction output and a different, second vasomotor event identification model transformed completed vasomotor event identification output.
  • said computer data input from said periodic capture ultradian rhythm sensor comprises time series information of a user’s ultradian rhythms.
  • said starting vasomotor event transformation parameters comprise quantified ultradian rhythm data.
  • a system as described in clause 50 or any other clause wherein said starting vasomotor event transformation parameters comprises data analysis of said computer data input, signal processing of said computer data input, variance analysis of said computer data input, spectral analysis of said computer data input, Fourier transform of said computer data input, time series analysis of said computer data input, wavelet transformations of said computer data input, power wavelet transformations of said computer data input, and any combination thereof.
  • said periodic capture ultradian rhythm sensor comprises a continuous capture ultradian rhythm sensor.
  • step of automatically varying said starting ovulation transformation parameters for said first ovulation prediction model automated ovulation computational transformation program to establish a second ovulation prediction model automated ovulation computational transformation program that differs from said first ovulation prediction model automated ovulation computational transformation program in the way that it predicts ovulation from data comprises the step of automatically cumulatively varying previously applied ovulation transformation parameters for said automated ovulation computational transformation program to establish a varied automated ovulation computational transformation program.
  • a process as described in clause 67 or any other clause wherein said step of automatically determining whether said first ovulation prediction model completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide said indication of the likely existence of an ovulation event comprises the step of automatically applying said first ovulation prediction model completed ovulation prediction output and said different, second ovulation prediction model transformed completed ovulation prediction output to a plurality of ovulation events.
  • ultradian rhythms is chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof.
  • a process as described in clause 67 or any other clause wherein said step of periodically sensing ultradian rhythms for said user comprises a step of automatically sensing ultradian rhythms for said user.
  • a process as described in clause 74 or any other clause wherein said step of automatically sensing ultradian rhythms for said user comprises a wearable sensing system.
  • said wearable sensing system measures user data chosen from heart rate, distal body temperature, and heart rate variability.
  • said wearable sensing system comprises a ring worn on said user’s finger.
  • said data input based at least in part on said step of periodically sensing said ultradian rhythms comprises time series information of said ultradian rhythms.
  • said starting ovulation transformation parameters comprises quantifying said data input to provide quantified ultradian rhythms.
  • a computerized ovulation prediction system comprising:
  • a computer processor operated automatic ovulation transformation comparator responsive to said first ovulation prediction model transformed completed ovulation prediction output and said different, second ovulation prediction model transformed completed ovulation prediction output and configured to automatically determine whether said first ovulation prediction model transformed completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide an indication of the likely existence of an ovulation event; - an ovulation indication based on said automatic determination of whether said first ovulation prediction model transformed completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide said indication of the likely existence of an ovulation event;
  • said automatic ovulation transformation comparator is responsive to a plurality of ovulation events.
  • a system as described in clause 83 or any other clause wherein said ultradian rhythm sensor is capable of capturing ultradian rhythms chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof.
  • said ovulation event is chosen from a preovulatory change in reproductive hormones and a luteinizing hormone surge.
  • said ovulation event comprises a release of an egg from an ovary.
  • said periodic capture ultradian rhythm sensor comprises an automatic periodic capture ultradian rhythm sensor.
  • said automatic periodic capture ultradian rhythm sensor comprises a wearable sensing system.
  • said wearable sensing system is configured to measure user data chosen from heart rate, distal body temperature, and heart rate variability.
  • said wearable sensing system comprises a ring worn on said user’s finger.
  • said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger.
  • said starting ovulation transformation parameters comprise quantified ultradian rhythm data.
  • said starting ovulation transformation parameters comprises data analysis of said computer data input, signal processing of said computer data input, variance analysis of said computer data input, spectral analysis of said computer data input, Fourier transform of said computer data input, time series analysis of said computer data input, wavelet transformations of said computer data input, power wavelet transformations of said computer data input, and any combination thereof.
  • said user input comprises user’s symptoms, signs of depression, vaginal dryness, libido, memory function, family history, if the user smokes, if the user consumes alcohol, and any combination thereof.
  • said user input comprises user’s symptoms, signs of depression, vaginal dryness, libido, memory function, family history, if the user smokes, if the user consumes alcohol, and any combination thereof.
  • the basic concepts of the application may be embodied in a variety of ways. It involves both ultradian rhythm determination techniques as well as devices to accomplish the appropriate ultradian rhythm determination.
  • the ultradian rhythm determination techniques are disclosed as part of the results shown to be achieved by the various devices described and as steps which are inherent to utilization. They are simply the natural result of utilizing the devices as intended and described.
  • some devices are disclosed, it should be understood that these not only accomplish certain methods but also can be varied in a number of ways.
  • all of these facets should be understood to be encompassed by this disclosure.
  • percentage values should be understood as encompassing the options of percentage values that include 99.5%, 99%, 97%, 95%, 92% or even 90% of the specified value or relative condition; correspondingly for values at the other end of the spectrum (e.g., substantially free of x, these should be understood as encompassing the options of percentage values that include not more than 0.5%, 1%, 3%, 5%, 8% or even 10% of the specified value or relative condition, all whether by volume or by weight as either may be specified.
  • these should be understood by a person of ordinary skill as being disclosed and included whether in an absolute value sense or in valuing one set of or substance as compared to the value of a second set of or substance.
  • each of the various elements of the invention and claims may also be achieved in a variety of manners.
  • an element is to be understood as encompassing individual as well as plural structures that may or may not be physically connected.
  • This disclosure should be understood to encompass each such variation, be it a variation of an embodiment of any apparatus embodiment, a method or process embodiment, or even merely a variation of any element of these.
  • the words for each element may be expressed by equivalent apparatus terms or method terms — even if only the function or result is the same. Such equivalent, broader, or even more generic terms should be considered to be encompassed in the description of each element or action.
  • each such means should be understood as encompassing all elements that can perform the given function, and all descriptions of elements that perform a described function should be understood as a non-limiting example of means for performing that function.
  • claim elements can also be expressed as any of: components that are configured to, or configured and arranged to, achieve a particular result, use, purpose, situation, function, or operation, or as components that are capable of achieving a particular result, use, purpose, situation, function, or operation,. All should be understood as within the scope of this disclosure and written description.
  • each of the measuring devices as herein disclosed and described ii) the related methods disclosed and described, iii) similar, equivalent, and even implicit variations of each of these devices and methods, iv) those alternative designs which accomplish each of the functions shown as are disclosed and described, v) those alternative designs and methods which accomplish each of the functions shown as are implicit to accomplish that which is disclosed and described, vi) each feature, component, and step shown as separate and independent inventions, vii) the applications enhanced by the various systems or components disclosed, viii) the resulting products produced by such processes, methods, systems or components, ix) each system, method, and element shown or described as now applied to any specific field or devices mentioned, x) methods and apparatuses substantially as described hereinbefore and with reference to any of the accompanying examples, xi) an apparatus for performing the methods described herein comprising means for performing the steps, xii) the various combinations and permutations of each of
  • the applicant(s) should be understood to have support to claim and make a statement of invention to at least: xv) processes performed with the aid of or on a computer, machine, or computing machine as described throughout the above discussion, xvi) a programmable apparatus as described throughout the above discussion, xvii) a computer readable memory encoded with data to direct a computer comprising means or elements which function as described throughout the above discussion, xviii) a computer, machine, or computing machine configured as herein disclosed and described, xix) individual or combined subroutines and programs as herein disclosed and described, xx) a carrier medium carrying computer readable code for control of a computer to carry out separately each and every individual and combined method described herein or in any claim, xxi) a computer program to perform separately each and every individual and combined method disclosed, xxii) a computer program containing all and each combination of means for performing each and every individual and combined step disclosed, xxiii) a storage medium storing each computer program disclosed
  • any claims set forth at any time are hereby incorporated by reference as part of this description of the invention, and the applicant expressly reserves the right to use all of or a portion of such incorporated content of such claims as additional description to support any of or all of the claims or any element or component thereof, and the applicant further expressly reserves the right to move any portion of or all of the incorporated content of such claims or any element or component thereof from the description into the claims or vice-versa as necessary to define the matter for which protection is sought by this application or by any subsequent continuation, division, or continuation-in-part application thereof, or to obtain any benefit of, reduction in fees pursuant to, or to comply with the patent laws, rules, or regulations of any country or treaty, and such content incorporated by reference shall survive during the entire pendency of this application including any subsequent continuation, division, or continuation-in-part application thereof or any reissue or extension thereon.

Abstract

Embodiments may provide determination of vasomotor events for a user including sensing ultradian rhythms (1) of a user (2), automatically accepting a data input (5) to a computer (4) based on sensed ultradian rhythms, establishing in the computer a vasomotor event determination model automated vasomotor event computational transform program (6) with starting vasomotor event transformation parameters (7); applying the vasomotor event determination model automated vasomotor event computational transform program to at least some of the data input to automatically create a vasomotor event determination model data transform (8), generating a vasomotor event determination model completed vasomotor event determination output, providing a vasomotor event indication (10), or the like. Embodiments may also include artificial intelligence using ultradian rhythms for vasomotor event determinations and even ovulation predictions.

Description

SYSTEMS AND METHODS OF IDENTIFYING PHYSIOLOGIC CHANGES UTILIZING ULTRADIAN RHYTHMS
PRIORITY CLAIM
This application is an international patent application claiming the benefit of and priority to U.S. Provisional Application No. 63/076,855 filed September 10, 2020, said application and any priority case is hereby incorporated by reference in its entirety herein.
TECHNICAL FIELD
Generally, the present invention may relate to identification, prediction, and even confirmation of physiological changes which may be used for vasomotor events, ovulation, and the like.
BACKGROUND
Ultradian events, as opposed to circadian rhythms, are short-term rhythms that have been observed since the beginning of modern biology and were quantified about a century ago. Recently, ultradian rhythms have moved to the forefront of chronobiology. Ultradian rhythms are ubiquitous in all biological systems and found in all organisms, from unicellular organisms to mammals, and from single cells to complex biological functions in multicellular animals. Their origin may be unclear but appear to be molecular in origin and could be controlled by hormonal inputs — in vertebrates, they originate from the activity of the central nervous system as patterns of neuropeptide release.
Ultradian rhythms may be characterized by variable periods ranging from about 20 min to a few hours, durations of a few minutes to several hours, and even amplitudes that can be quite small compared to the amplitude of other biological rhythms. Because of their aperiodic nature, specific sampling, analytic tools and procedures may be used in the analysis of time series rhythms at the population and biologic system level. Wearable technologies that can provide frequent sampling with good discriminatory power are revolutionizing digital health in unexpected ways.
1
RECTIFIED SHEET (RULE 91) - ISA/US While ultradian rhythms can be observed in a time series of biological events, their quantification using statistical analysis may be dependent on the characteristics of the signal, such as the shape of the events, the regularity of the intervals between events, and the underlying presence of a circadian rhythm.
Recent advances in computer power have facilitated the use of advanced time series analyses, which has been proposed as a method to detect and even quantify ultradian rhythms. Methods used in the analysis of circadian rhythms may often not be appropriate because the ultradian rhythms do not always occur at a regular period. Patterns of individual ultradian rhythms may rarely be sinusoidal or composed of independent sinusoids, and so cannot be decomposed into a set of frequencies making Fourier Transformations sometimes inappropriate. Wavelet transformations, which can analyze a time series using wavelets, or “mini-waves”, have evolved perhaps as a more appropriate manner for analysis of time-limited wavelets considering the shapes that may fit the ultradian rhythms better than a Fourier Transformation.
Importantly, ultradian rhythms observed in core and peripheral body temperature may be coupled with ultradian peaks in heart rate variability power, heart rate, locomotion and activity. Similarly, the core body temperature may be intimately linked to several endocrine axes with the reproductive system as an excellent example of ultradian rhythm coupled to circadian rhythm and even core body temperature. High temporal resolution recordings of body temperature, heart rate, and even heart rate variability may demonstrate predictive power for ovarian activity and a predictor of pregnancy outcome. Coupling the synchronization of reproductive activity with environmental changes and even cycles has the potential to optimize reproductive success. Because of the methodological limitations of obtaining frequent blood samples (e.g., about 5 min sampling frequency) that can otherwise monitor ultradian and circadian rhythms for long time periods, wearables can be useful tools to understand reproductive disorders, predicting fertility and infertility in mammals, including humans and livestock as well as predictive for physiologic monitoring and disease processes.
Certain physiological changes such as but not limited to ovulation, vasomotor events, hot flashes, menopause, drug reactions, post-traumatic stress, and the like can be difficult to predict, confirm, monitor, and the like. At about the age of 40 years, levels of estrogen and progesterone start to fall in females, and perimenopause — the transition to menopause — begins. As this transition progresses, a female may experience vasomotor symptoms. Vasomotor symptoms may be those that occur due to the constriction or dilation of blood vessels. They can include hot flashes, night sweats, heart palpitations, changes in blood pressure, and the like. The most likely reason why these symptoms can occur during menopause is that hormonal fluctuations affect the mechanisms that control blood pressure and control. A person may enter menopause about 12 months after their last period. Hot flashes and other symptoms can start during perimenopause, while menstruation is still occurring, or they may begin after a person’s periods end. Not everyone has these symptoms, and they can vary in severity among individuals. Some people may start menopause earlier in life. In some cases, this may happen naturally, but in others, it may be due to surgery, a health condition, or certain types of medical treatment. There is a need for a technology that can predict, identify, and even confirm vasomotor events.
Those experiencing vasomotor events such as vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, or the like may want to track such events perhaps for further analysis including discussions with others, research, discussions with doctors, or the like. In the past, it has been difficult to track vasomotor events unless one takes their own vitals (heart rate, temperature, or the like) and records them themselves. It is also difficult to track vasomotor events during sleep. Hormone replacement therapy for those going through menopause or the like have risks associated including heart attacks, strokes, breast cancer, heart disease, and the like. However, depending on when a hormone replacement therapy begins, those risks may decrease. Thus, it is desirable to provide systems and methods to accurately and easily measure vasomotor events.
DISCLOSURE OF INVENTION
The present invention includes a variety of aspects, which may be selected in different combinations based upon the particular application or needs to be addressed. In various embodiments, the embodiments disclosed herein may include identification of physiological changes which may be used in various applications. It is an object of some embodiments to provide prediction or even identification of a physiological change relating to a vasomotor event.
It is another object of some embodiments to utilize ultradian rhythms in prediction or even identification of a physiological change.
It is another object of some embodiments to provide artificial intelligence utilizing ultradian rhythms in prediction or even identification of a physiological change.
Another object of some embodiments may utilize a wearable device to help accurately and easily measure vasomotor events.
It is another object of some embodiments to utilize ultradian rhythms with prediction and even confirmation of ovulation.
Naturally, further objects, goals and embodiments of the application are disclosed throughout other areas of the specification, claims, and drawings.
BRIEF DESCRIPTION OF DRAWINGS
FIG. 1 shows a schematic diagram for determination of vasomotor events and prediction of ovulation in accordance with some embodiments.
FIG. 2 shows a non-limiting example of the minute by minute changes in heart rate variability during a hot flash in accordance with some embodiments.
FIG. 3 shows a non-limiting example of raw daytime distal body temperature data in accordance with some embodiments.
FIG. 4 shows a non-limiting example of scored ultradian power of distal body temperature to anticipate LH surge onset in accordance with some embodiments.
FIG. 5 shows a non-limiting example of raw nighttime heart rate variability data in accordance with some embodiments.
FIG. 6 shows a non -limiting example of scored ultradian power of heart rate variability to anticipate LH surge onset in accordance with some embodiments. MODE(S) FOR CARRYING OUT THE INVENTIONS
It should be understood that the present application includes a variety of aspects, which may be combined in different ways. The following descriptions are provided to list elements and describe some of the embodiments. These elements are listed with initial embodiments; however, it should be understood that they may be combined in any manner and in any number to create additional embodiments. The variously described examples and preferred embodiments should not be construed to limit the present application to only the explicitly described systems, techniques, and applications. The specific embodiment or embodiments shown are examples only. The specification should be understood and is intended as supporting broad claims as well as each embodiment, and even claims where other embodiments may be excluded. Importantly, disclosure of merely exemplary embodiments is not meant to limit the breadth of other more encompassing claims that may be made where such may be only one of several methods or embodiments which could be employed in a broader claim or the like. Further, this description should be understood to support and encompass descriptions and claims of all the various embodiments, systems, techniques, methods, devices, and applications with any number of the disclosed elements, with each element alone, and also with any and all various permutations and combinations of all elements in this or any subsequent application.
Embodiments of the application may provide determination of certain physiological events including but not limited to vasomotor events, ovulation, drug effectiveness, diagnosis, delivery, ovulation, vasomotor symptoms or the like perhaps based on information obtained from the human body.
Determination including identification, prediction, and even confirmation of physiological changes may be evaluated from physiology measurements such as but not limited to: ultradian rhythms of core body temperature; ultradian rhythms of distal body temperature; ultradian rhythms of heart rate; ultradian rhythms of heart rate variability; or the like; and any combination or permutation thereof. Further physiology measurements may include, but is not limited to high frequency peripheral temperature measurement, actigraphy, acoustic monitoring; internal metabolic monitoring such as measuring electrical impedance of cervical mucus, pH, water content, or the like; or the like, and in any combination or permutation thereof. An ultradian rhythm may be a recurrent period or even cycle repeated throughout an about 24-hour day, e.g., about 1 to about 4 hours. In contrast, circadian rhythms can be completed in one cycle daily, while infradian rhythms such as the human menstrual cycle may have periods longer than a day. Ultradian rhythms may be identified in body temperature, heart rates, heart rate variability, and may even be coordinated with a menstrual cycle, perimenopause, and menopause or the like.
Patterns of neuropeptides and even hormones may be manifested in changes to autonomic central nervous system control and even metabolic systems. Hence further manifestation in change to temperature, heart rate, and even heart rate variability may be seen through their respective ultradian rhythms and may even be described mathematically. For ovulation, this phenomenon may be exhibited in parasympathetic dominance in the follicular phase and even sympathetic dominance in the luteal phase. Assessment of ultradian rhythms may identify perimenopausal transition and even menopause. Amplitude and even frequency of ultradian rhythms may change over the course of a human’s life, perhaps due to treatments, perimenopause, menopause, medicine, menstrual cycle or the like perhaps that an inflection point and subsequent peak of peripheral temperature measurement, heart rate, and even heart rate variability ultradian power may anticipate vasomotor events, ovulation, or the like.
Evaluation of ultradian frequencies of temperature, heart rate, and even heart rate variability perhaps when compared to each other, may strengthen along with amplitude and/or phase change leading up to ovulation and may destabilize in the luteal phase. These may be used to predict ovulation or even confirm pregnancy. This may be instead of using the traditional basal body temperature (possibly at once per day), of using time series analysis of daily body temperature patterns, or even instead of using analysis of infradian nadirs.
Frequency analysis of ultradian rhythm data may be performed by any number of mathematical methods including by way of non-limiting examples: wavelets, analysis of variance, spectral analysis (Fourier), and the like.
Timeseries analysis of measurements of temperature, heart rate, and even heart rate variability ultradian frequencies may enable endocrine status assessment. Timeseries analysis of ultradian frequencies perhaps of body temperature measured peripherally such as combined with timeseries analysis of ultradian frequencies of heart rate and even heart rate variability can be further combined with internal body temperature and even actigraphy may provide a greater understanding of humans, males, females, female reproduction, and the like such as but not limited to, physiological strain index, symptoms of menopause, sepsis prediction, sepsis confirmation, drug effect, confirmation of infectious disease processes including, but not limited to bacterial, viral or fungal acute, subacute or chronic (e.g., optimal time for phlebotomy for blood culture or identification of infectious agent), and the like. This may help to understand and even identify and predict events of physiological changes, female reproduction, ovulation, defining a window of fertility, and the like.
The exemplary embodiments described herein may be used as methods and systems to determine including predict and even confirm: pharmacologic effect; acute of chronic neurologic disease states (e.g., impending seizure); impending immunologic event; acute of chronic cardiogenic change; sleep disorders; endocrinologic disorders (e.g., acute or even chronic); heat illness or even heat stroke; starvation; a change in immunologic statis (e.g., confirmation of vaccine response); a window for therapeutic administration of a pharmacologic agent; and perhaps even a psychiatric condition (e.g., acute or chronic), in any combination or permutation, and the like. This list is non-exhaustive and meant as exemplary only.
High temporal resolution data gathering may characterize “normal” physiologic change and even normal heterogeneity perhaps corresponding with physiological relevance in understanding a heterogeneity of patterns across populations such as due to age, genetic background, diet, health, environment, time of year, and the like.
Perhaps due to a high demand for accurate methods of fertility assessment, it may be apparent that predictive and even conformational information for reproductive status may hold significant commercial value, including, but not limited to: confirming ovulation, predicting ovulation, uterine events, and the like, which may include confirming pregnancy, predicting pregnancy, early pregnancy detection, avoiding pregnancy, and even information in solving issues of infertility, pharmacologic application, timing of drug delivery, and the like.
Some embodiments of the application may provide a process for determination of vasomotor events for a user comprising the steps of periodically sensing ultradian rhythms (1) of said user (2); automatically accepting a data input (5) to a computer (4) based at least in part on said step of periodically sensing said ultradian rhythms; establishing in said computer a vasomotor event determination model automated vasomotor event computational transform program (6) with starting vasomotor event transformation parameters (7); automatically applying said vasomotor event determination model automated vasomotor event computational transform program with said starting vasomotor event transformation parameters, to at least some of said data input to automatically create a vasomotor event determination model data transform (8); generating a vasomotor event determination model completed vasomotor event determination output (9) based on said vasomotor event determination model data transform; and perhaps even providing a vasomotor event indication (10) based on said step of generating said vasomotor event determination model completed vasomotor event determination output. Systems may include a computerized vasomotor events determination system comprising: a periodic capture ultradian rhythm sensor (3) for placement in contact with a user (2); a computer data input (5) from said periodic capture ultradian rhythm sensor; a computer processor operated vasomotor event determination model automated vasomotor event computational transform program (6) with starting vasomotor event transformation parameters (7) responsive to said computer data input from said periodic capture ultradian rhythm sensor; a vasomotor event determination model data transform (8) generated from said computer processor operated vasomotor event determination model automated vasomotor event computational transform program with said starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor; a vasomotor event determination model transformed completed vasomotor event determination output (9) generated from said vasomotor event determination model data transform; and perhaps even a vasomotor event indication (10) based on said vasomotor event determination model transformed completed vasomotor event determination output.
Vasomotor events of a user (2) may be determined by periodically sensing ultradian rhythms (1) perhaps with a periodic capture ultradian rhythm sensor (3) which may be placed in contact with a user (2). Ultradian rhythms (1) may include ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, or the like, and any combination thereof. A vasomotor event (12) may include vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, or the like, and any combination thereof. A computer (4) may automatically accept a data input (5) which may be based on data from at least in part the sensing of ultradian rhythms perhaps from a periodic capture ultradian rhythm sensor. Such sensing may be automatically perhaps with an automatic periodic capture ultradian rhythm sensor. In some embodiments, ultradian rhythms may be continuously sensed perhaps to provide a continuous capture ultradian rhythm sensor. A computer may be any device that can be programmed to carry out sequences of arithmetic or logical operations automatically such as but not limited to a desktop, laptop, smart phone, application, or the like.
A computer may include or be established with a computer processor operated vasomotor event determination model automated vasomotor event computational transform program (6) perhaps with starting vasomotor event transformation parameters (7) which may be responsive to computer data input (5) from a periodic capture ultradian rhythm sensor. Embodiments may provide automatically applying the vasomotor event determination model automated vasomotor event computational transform program with the starting vasomotor event transformation parameters, to at least some of the data input to automatically create a vasomotor event determination model data transform (8). From a vasomotor event determination model data transform, a vasomotor event determination model completed vasomotor event determination output (9) may be generated. Based on a vasomotor event determination model completed vasomotor event determination output, a vasomotor event indication (10) may be provided.
In some embodiments a user may provide user input (11) to which a vasomotor event determination model completed vasomotor event determination output or even ovulation event prediction model completed ovulation event prediction output may be responsive. A user may be a male or female or the like. User input (11) may include a user’s symptoms, signs of depression, vaginal dryness, libido, memory function, family history, if the user smokes, if the user consumes alcohol, or the like, and any combination or permutation thereof. This information may be valuable in the analysis of the ultradian rhythm data and even a resulting output. A vasomotor event determination model completed vasomotor event determination output may be based on a plurality of vasomotor events (12) from a user. An indication such as vasomotor event indication (10) may include an identification of an event such as a vasomotor event or even a prediction of a future event such as a vasomotor event. A vasomotor event determination model completed vasomotor event determination output (9) may include a a vasomotor event prediction model completed vasomotor event prediction output or even a vasomotor event identification model completed vasomotor event identification output.
Sensing of a user’s ultradian rhythms may be made more convenient and easier with a wearable sensing system of which a user can wear a sensor (3) on or even in their body. A wearable sensing system can measure user data such as but not limited to heart rate, distal body temperature, heart rate variability, sleep, or the like. A sensing system may be may be any kind of sensor including but not limited to a ring perhaps worn on a user’s finger, a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, a sensor worn on a finger, or the like. Signal monitoring and even data transmission may be achieved to a computer via Bluetooth or the like.
As discussed herein data input may include time series information of ultradian rhythms. Starting vasomotor event transformation parameters (7) may include quantifying of data input to provide quantified ultradian rhythms. Analyzing data input may include data analysis, signal processing, analysis of variance, spectral analysis, Fourier transform, time series analysis, wavelet transformations, power wavelet transformations, or the like, of computer data input and any combination thereof.
Embodiments of the present invention may include artificial intelligence systems such as discussed in WO 2020/013830 to Prima-Temp, Inc., hereby incorporated by reference herein. Sensing and analysis of ultradian rhythms for determination with vasomotor events or even for prediction of ovulation may utilize artificial intelligence.
Artificial intelligence embodiments of the application include a process for determination of vasomotor events for a user comprising the steps of periodically sensing ultradian rhythms of said user; automatically accepting a data input to a computer based at least in part on said step of periodically sensing said ultradian rhythms; establishing in said computer a first vasomotor event determination model automated vasomotor event computational transformation program with starting vasomotor event transformation parameters; automatically applying said first vasomotor event determination model automated vasomotor event computational transformation program with said starting vasomotor event transformation parameters, to at least some of said ultradian rhythms to automatically create a first vasomotor event determination model data transform; generating a first vasomotor event determination model completed vasomotor event determination output based on said first vasomotor event determination model data transform; automatically varying said starting vasomotor event transformation parameters for said first vasomotor event determination model automated vasomotor event computational transformation program to establish a second vasomotor event determination model automated vasomotor event computational transformation program (15) that differs from said first vasomotor event determination model automated vasomotor event computational transformation program in the way that it determines vasomotor events from said data input; automatically applying said second vasomotor event determination model automated vasomotor event computational transformation program with said automatically varied vasomotor event transformation parameters (16), to at least some of said ultradian rhythms to automatically create a second vasomotor event determination model data transform (17); generating a different, second vasomotor event determination model transformed completed vasomotor event determination output based on said second vasomotor event determination model data transform; automatically comparing said first vasomotor event determination model transformed completed vasomotor event determination output with said different, second vasomotor event determination model transformed completed vasomotor event determination output (18); automatically determining whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide an existence of a vasomotor event; providing a vasomotor event indication (10) based on said step of automatically determining whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide said existence of said vasomotor event; and perhaps even storing automatically improved vasomotor event transformation parameters (19) that are determined to provide said likely existence of said vasomotor event for future use to automatically self improve vasomotor event determination models (20).
A computerized vasomotor event determination system may include: a periodic capture ultradian rhythm sensor for placement in contact with a user; a computer data input from said periodic capture ultradian rhythm sensor; a computer processor operated first vasomotor event determination model automated vasomotor event computational transformation program with starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor; a first vasomotor event determination model data transform generated from said computer processor operated first vasomotor event determination model automated vasomotor event computational transformation program with said starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor; a first vasomotor event determination model transformed completed vasomotor event determination output generated from said first vasomotor event determination model data transform; a computer processor operated varied automated vasomotor event computational transformation program (21) configured to generate automated varied vasomotor event transformation parameters; a computer processor operated second vasomotor event determination model automated vasomotor event computational transformation program generated from said automated varied vasomotor event transformation parameters and which differs from said computer processor operated first vasomotor event determination model automated vasomotor event computational transformation program in the way it determines a vasomotor event from said computer data input; a computer processor operated second vasomotor event determination model data transform generated from said computer processor operated second vasomotor event determination model automated vasomotor event computational transformation program with said automated varied vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor; a different, second vasomotor event determination model transformed completed vasomotor event determination output generated from said computer processor operated second vasomotor event determination model data transform; a computer processor operated automatic vasomotor event transformation comparator (22) responsive to said first vasomotor event determination model transformed completed vasomotor event determination output and said different, second vasomotor event determination model transformed completed vasomotor event determination output and configured to automatically determine whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide an existence of a vasomotor event; a vasomotor event indication based on said automatic determination of whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to said existence of said vasomotor event; automatically improved vasomotor event transformation parameters based on said determined vasomotor event determination model transformed completed vasomotor event determination output that is likely to provide said existence of a vasomotor event; and perhaps even a computer processor operated automatic vasomotor event determination model self improvement program using said automatically improved vasomotor event transformation parameters for future use.
Artificial intelligence embodiments of the application include a process for prediction of the onset of ovulation for a user comprising the steps of: periodically sensing ultradian rhythms for said user; automatically accepting a data input to a computer based at least in part on said step of periodically sensing said ultradian rhythms; establishing in said computer a first ovulation prediction model automated ovulation computational transformation program with starting ovulation transformation parameters; automatically applying said first ovulation prediction model automated ovulation computational transformation program with said starting ovulation transformation parameters, to at least some of said ultradian rhythms to automatically create a first ovulation prediction model data transform; generating a first ovulation prediction model completed ovulation prediction output based on said ovulation prediction model data transform; automatically varying said starting transformation parameters for said first ovulation prediction model automated ovulation computational transformation program to establish a second ovulation prediction model automated ovulation computational transformation program that differs from said first ovulation prediction model automated ovulation computational transformation program in the way that it predicts ovulation from said data input; automatically applying said second ovulation prediction model automated ovulation computational transformation program with said automatically varied ovulation transformation parameters, to at least some of said ultradian rhythms to automatically create a second ovulation prediction model data transform; generating a different, second ovulation prediction model transformed completed ovulation prediction output based on a data transform of said second ovulation prediction model; automatically comparing said first ovulation prediction model completed ovulation prediction output with said different, second ovulation prediction model transformed completed ovulation prediction output; automatically determining whether said first ovulation prediction model completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide an indication of the likely existence of an ovulation event; providing an ovulation indication based on said step of automatically determining whether said first ovulation prediction model completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide said indication of the likely existence of an ovulation event; and perhaps even storing automatically improved ovulation transformation parameters that are determined to provide said desired selection criterion indication of the likely existence of an ovulation event for future use to automatically self improve said ovulation prediction models.
A computerized ovulation prediction system may include: a periodic capture ultradian rhythm sensor for placement in contact with user; a computer data input from said periodic capture ultradian rhythm sensor; a computer processor operated first ovulation prediction model automated ovulation computational transformation program with starting ovulation transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor; a first ovulation prediction model data transform generated from said computer processor operated first ovulation prediction model automated ovulation computational transformation program with said starting ovulation transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor; a first ovulation prediction model transformed completed ovulation prediction output generated from said first ovulation prediction model data transform; a computer processor operated varied automated ovulation computational transformation program configured to generate automated varied ovulation transformation parameters; a computer processor operated second ovulation prediction model automated ovulation computational transformation program generated from said automated varied ovulation transformation parameters and which differs from said computer processor operated first ovulation prediction model automated ovulation computational transformation program in the way it predicts ovulation from said computer data input; a computer processor operated second ovulation prediction model data transform generated from said computer processor operated second ovulation prediction model automated ovulation computational transformation program with said automated varied ovulation transformation parameters responsive to computer data input from said periodic capture ultradian rhythm sensor; a different, second ovulation prediction model transformed completed ovulation prediction output generated from a data transform of said second prediction model; a computer processor operated automatic ovulation transformation comparator responsive to said first ovulation prediction model transformed completed ovulation prediction output and said different, second ovulation prediction model transformed completed ovulation prediction output and configured to automatically determine whether said first ovulation prediction model transformed completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide an indication of the likely existence of an ovulation event; an ovulation indication based on said automatic determination of whether said first ovulation prediction model transformed completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide said indication of the likely existence of an ovulation event; automatically improved ovulation transformation parameters based on said determined ovulation prediction model transformed completed ovulation prediction output that is likely to provide said indication of the likely existence of an ovulation event; and perhaps even a computer processor operated automatic ovulation prediction model self improvement program using said automatically improved ovulation transformation parameters for future use.
Automated varied vasomotor event transformation parameters may be automatically cumulatively varied vasomotor event transformation parameters generated from previously determined vasomotor event transformation parameters. A comparator may be responsive to a plurality of events such a vasomotor events or a plurality of ovulation events. An ovulation event may be a preovulatory change in reproductive hormones, a luteinizing hormone surge, a release of an egg from an ovary, or the like.
FIG. 1 represents a schematic diagram for determination of vasomotor events and prediction of ovulation in accordance with some embodiments including a user (2), ultradian rhythms (1) of a user, vasomotor events (12), a sensor (3), user input (11), data input (5), a computer (4), an event determination model transform program (20), a transformation program (6), transformation parameters (7), data transform (8), an output (9), an indication (10), a second transformation program (15), varied transformed program (21), varied transformation parameters (16), a second data transform (17), a second output (18), stored improved parameters (19), a comparator (20), and the like.
FIG. 2 shows a non-limiting example of the minute by minute changes in high frequency heart rate variability during a hot flash. FIG. 3 shows a non-limiting example of raw daytime distal body temperature data in Celcius at wake up, 4 hours, 8 hours, 12 hours, and 16 hours. FIG. 4 shows a non-limiting example of scored ultradian power of distal body temperature versus days relative to LH surge onset to anticipate LH surge onset. FIG. 5 shows a non-limiting example of raw nighttime heart rate variability data (root mean square of successive differences between normal heartbeats) at sleep, 4 hours, and 8 hours. FIG. 6 shows a non-limiting example of scored ultradian power of heart rate variability (root mean square of successive differences between normal heartbeats) versus days relative to LH surge onset to anticipate LH surge onset.
As can be appreciated, another aspect of embodiments of the invention can be the way the system processes data to achieve its purpose. Processing of data may be by software and or firmware, and for systems can be configured in a variety of ways and at a variety of locations. Devices and capabilities can be spread throughout the system as well. For example, in some embodiments the system can involve three major components. Each of these components can be configured as a discrete processor, a programmed dedicated processor, an ASIC, firmware, a device having programmable processing capability, a smart phone, a multipurpose computer, a server, or even internet or cloud computing capability.
A type of computer processor, perhaps considered even any app processor may be used. This processor can be quite programmable and the identification as an app processor may only distinguish its location. As an app processor, a device can execute a program, perhaps considered an application program or app, to achieve some type of operation. This and perhaps other computer processors) can achieve data, capture, data storage, user input, or other operations. It can also transmit results, data, or other information to be able to interact with another processing capability. One of the other components of such an embodiment of the system can be termed server. It can just be a generally more capable or more available computer capability. The server can also include general programmable capabilities and it may be or include a multipurpose programmable computer or processor. Communication can occur in standard fashions. The programmable or configurable capabilities or components in server can include yet another computer processor which may be termed server processor to distinguish it main location as above. It can also access and interact with an internal or outside capability. This outside capability may be a memory. The resource can be a cloud storage capability, a cloud computing capability or the like. Again, memory and even processing capabilities can be distributed at various locales as is known to those in the art.
A computer processor of a computer can be understood as coupled to data memory in a manner that uses those values to achieve its programmed purpose. As should be appreciated, data processing, data pass-through, and/or data storage can occur at any location. In some embodiments, only limited activity might occur in a sensor. Embodiments may provide temporary data storage, limited processing activity, and data transmission capability from a sensor. Somewhat more complex activity can occur in a sensor. In some embodiments, the most complex activity and most in-depth data storage - such as for multi users - can occur or be achieved at computer or server.
Data input may be relatively noisy. To assist in achieving useful data, embodiments can use event transformation parameters perhaps to smooth the data which may be a functionality that is designed to endeavor to remove fluctuations from the actual values. A type of programming, firmware, ASIC, or routine could remove the fluctuations from use in achieving a data transform. Literal removal of data can occur by removing activity related data.
A system can be configured by programming, firmware, of use of an ASIC to automatically transform data to provide a first or second transformation computation generated output. A system can automatically transform perhaps (but not necessarily) the same sensor computer data input accessed values through a second transformation computation and use a second output generator to achieve, more generally, a second transformation computation generated output. Two types of outputs can be compared and, depending upon the needs, one or the other can be chosen as the one that is more likely to provide a desired indication of the likely existence of an event.
In general a system can be configured to provide indications that are peculiarly helpful or desirable to different particular users or needs. From the general ability to compare and select among different models or transformations, it can be understood that a system can be extended or use different configuration to include an ability to self-improve. The aspect of having a system that can automatically self-improve is particularly useful in the context of predicting or identifying an event. A system can be configured to use its data perhaps in conjunction with other users’ data or perhaps prior data for just that user to provide automatically enhanced and improved prediction routines. For example, in an instance of using a variable range of values such as for a running average process, it can be understood that the data transformation can be achieved through the application of transformation parameters. Parameters can be considered perhaps the value(s) that represent the ranges of data points over which the running average could be calculated. Such transformation parameters can be applied to the automatic data transform, and can be varied such as from a starting value (initially or at any reapplication of the process) for application such as by a transformation parameter vary routine. Of course, by varying parameters, different results can be achieved. And these results can be compared automatically based on default or even user input such as by an automatic transformed comparator. Some types of ultimate or intermediate outputs can be made available automatically for comparison, or inclusion through combination of multiple transformations. The output can be considered an automatic transformed output regardless of the stage in the transformation or recalculation from which it is derived.
By storing transformation parameters in a transformation parameter memory, these parameters can be made available for later use, later variation, and even cumulative adjustment. When varied either initially or after cumulative improvement or the like, the system can be considered as applying varied transformation parameters such as to achieve a varied data transform. There can be a starting data transform and then as a result of varying parameters, a varied data transform and this process can happen automatically. This varied data transformation can provide an output that is used by an automatic varied transformation outfit generator. As can be understood from the above, such a system can have an automatic transform output comparator to allow decisions to be made based on the varied parameters applied.
A self-improving process with a number of transformations can be considered as establishing an automated computational transform program with starting transformation parameters that may be at the beginning of any improvement process be it cumulative of just beginning initiation. Such starting transformation parameters can be applied to at least some of the data values to automatically create a starting data transform. This starting data transform can generate an output which can then be compared. Importantly, a comparison can be achieved by varying the starting transformation parameters to achieve a varied automated computational transformation which can be similarly applied to at least a portion of the body temperature values then available. Such a system can be configured to automatically compare the starting transformed prediction output, perhaps such as with the varied transform prediction output, to determine which of these is likely to provide a more aligned indication of the likely existence of an event. Once this decision is made, the selected parameters can then be stored and used perhaps as the next re-established starting transformation parameters so that continued system learning and improvement can build on itself by further revising and re-establishing desired parameters. These parameters can be cumulatively varied so that the system builds on itself as mentioned above. Cumulative variation can be achieved by using then-available data that includes some labeling or other indication, perhaps through user input to assess what is likely most accurate. Determinations can also occur automatically even with computer determined weighting so that in some embodiments more recent data or perhaps more applicable data can be weighted heavier than other data. In general, cumulative improvement can be achieved by inclusion of a cumulative transformation parameter vary routine. Further, systems can include a neural network architecture capable of incorporating data and other indications, perhaps such as discrete user data, user determined activity or occurrence data, clinical data, test data, LH test data, or the like, to allow improved identification or predictions of likely events or to allow linkage to user preferences. Processing functions can take values as input arguments and output identifications or predictions. By using re data that is correlated with indications, embodiments of the system can allow the computer to iteratively tune the weights in the neural network in such a way as to minimize an error function. Further, the error function may be any function of the difference between the neural network output and user information such as test results. As more data paired with results or other indicia (call this "labeled data") becomes available, the system can automatically continue its iterative weight tuning process (called "training") to produce preferable event indications or predictions on subsequent user events to yield a lower error as judged by the error function. There may also be several neural networks with different internal architectures and different error functions if desired. These can be re-trained whenever new data is available, and then re-compared with each other to see which is best, however that may be defined.
It should be understood that the parameters utilized can be simplistic or complex. More simplistic parameters can be considered as parameters like weights, ranges, coefficients, and other, perhaps linear, parameters. In addition, the parameters can be more complex and even non-linear. These can even include parameters that completely vary the entire nature of the transformation and recalculation itself. Regardless whether simplistic or complex, variation in parameters can consider or react to a user input. The system can make recommendations and even suggest alterations to or as a result of this input. Furthermore, a user can even have the option of providing a user input to which transformations, comparisons, and ovulation prediction outputs can be responsive.
Computer programming-wise, it can be understood that the system may be considered as including a multiple, a plurality, and any number of computer processor operated automatic data transform calculators. Each of these can be configured to apply variable transformation parameters. Further, embodiments of the system can use the multiple transformation calculations either in the alternative or as a composite way to provide a desired output. In embodiments that combine or create a composite to provide a desired output, the system can include an automatic transform combiner. This automatic transform calculator combiner can be responsive to a plurality of automatic transform calculators which, again, may be identical capabilities that apply different parameters or may be entirely differently programmed recalculators.
A further understanding of the way in which parameters can be varied can be understood by the more simplistic application of the running average transformation routine and the like. Using this as but one example, a system can include a transformation range size vary routine, a transformation range drift vary routine, a transformation threshold inclusion vary routine, a transformation coefficient vary routine, and/or a transformation weight vary routine. Of course, other variations are possible, and by inclusion of these types of processes and others, decisions and even some type of determination can be made based upon any desired optimization. Furthermore, the use of weight vary routines can show how a composite output can be used with any number of transformations and any number of parameter variations with each having its own weight assigned to a total calculation or prediction output. Weighting of differing transforms can be particularly useful with consideration of the user input whereby user conditions that existed at that particular time can be applied or even removed to more appropriately achieve a prediction and perhaps even more appropriately apply then available multi user data to the situation then existing. In its general sense, embodiments of the invention can involve a decision, identification, or prediction output that can be made on a combination of differing transforms, and a composite of various transforms. In just one general sense, an embodiment can involve a transform T, such as a first transform (Ti), a second transform (T2), up to an nth transform (Tn) which can each be fundamentally different and can apply one or many persons’ data. Further, each transform can have its own coefficient (constant, look up, function, or otherwise) to indicate any scaling such as for a particularly applicable factor (demographic, age, etc.) for that transform (ci, C2, cn so as to have ciTi, C2T2, cnTn) its own non-linear factor (indicated as a superscript, or power but not to be limited to such a mathematical process, CnTn1, cnTn 2, cnTn m), and a weight (wi, W2, ii’i, so as to have widTi, W2C2 2, WnCnTn), and these can be used individually, in the alternative, or summed, added (widTi + W2C2 2 + . . . WnCnTn) or otherwise combined as a composite to give an enhanced ovulation prediction output, p, perhaps such as p = S WnCnTn. As can be appreciated, by understanding that weighting can even include zero weighting - meaning that that one transform is effectively removed from the process - any number of transformed recalculations, from one to many, can be included in a more general embodiment of the system. Such a composite can be interactively varied and evaluated to result in a continually self-improved system such as can be considered an instance of an artificial intelligence system or even a neural network based Al system as should be readily understood or separately available to a person of ordinary skill in the art.
One aspect that is particularly interesting for a prediction is the aspect of determining which result is the most optimal. This can be a challenge for an aspect such as predicting or onset sensing something as complex as ovulation or the like which can be difficult to sense or know with certainty prior to its actual occurrence and which may be accompanied by discernable indicia only after the fact. As mentioned above, optimal-ness can be determined based upon a user selection or the like. In addition, the aspect of being optimal such as perhaps being earliest or perhaps most accurate, etc. can be achieved by comparison to a variety of data. This data can be a user input perhaps such as, but not limited to, a user’s input of physical symptoms indicating that ovulation or another event is occurring. The data can also be a variety of other types of input perhaps, a prior computer input, a luteinizing hormone test computer input, a fertility test result computer input, a user menstrual cycle computer input, a user body type computer input, a user physical condition computer input, a user medical history computer input, a user text message computer input, and even a plurality of these various inputs.
While the invention has been described in connection with some preferred embodiments, it is not intended to limit the scope of the invention to the particular form set forth, but on the contrary, it is intended to cover such alternatives, modifications, and equivalents as may be included within the spirit and scope of the invention as defined by the statements of inventions. Examples of alternative claims may include:
1. A process for determination of vasomotor events for a user comprising the steps of:
- periodically sensing ultradian rhythms of said user;
- automatically accepting a data input to a computer based at least in part on said step of periodically sensing said ultradian rhythms;
- establishing in said computer a vasomotor event determination model automated vasomotor event computational transform program with starting vasomotor event transformation parameters;
- automatically applying said vasomotor event determination model automated vasomotor event computational transform program with said starting vasomotor event transformation parameters, to at least some of said data input to automatically create a vasomotor event determination model data transform;
- generating a vasomotor event determination model completed vasomotor event determination output based on said vasomotor event determination model data transform; and
- providing a vasomotor event indication based on said step of generating said vasomotor event determination model completed vasomotor event determination output.
2. A process as described in clause 1 or any other clause and further comprising the step of providing a user input to which said step of generating a vasomotor event determination model completed vasomotor event determination output is responsive.
3. A process as described in clause 1 or any other clause wherein said step of generating a vasomotor event determination model completed vasomotor event determination output based on said vasomotor event determination model data transform comprises a step of generating a vasomotor event determination model completed vasomotor event determination output based on said vasomotor event determination model data transform and a plurality of vasomotor events. A process as described in clause 1 or any other clause wherein said ultradian rhythms is chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof. A process as described in clause 1 or any other clause wherein said vasomotor event is chosen from vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, and any combination thereof. A process as described in clause 1 or 5 or any other clause wherein said vasomotor event indication is chosen from an identification of a vasomotor event and a prediction of a future vasomotor event. A process as described in clause 1 or any other clause wherein said vasomotor event determination model completed vasomotor event determination output is chosen from a vasomotor event prediction model completed vasomotor event prediction output and a vasomotor event identification model completed vasomotor event identification output. A process as described in clause 1 or any other clause wherein said step of periodically sensing ultradian rhythms for said user comprises a step of automatically sensing ultradian rhythms for said user. A process as described in clause 8 or any other clause wherein said step of automatically sensing ultradian rhythms for said user comprises a wearable sensing system. A process as described in clause 9 or any other clause wherein said wearable sensing system measures user data chosen from heart rate, distal body temperature, and heart rate variability. A process as described in clause 9 or any other clause wherein said wearable sensing system comprises a ring worn on said user’s finger. A process as described in clause 9 or any other clause wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger. A process as described in clause 1 or any other clause wherein said data input based at least in part on said step of periodically sensing said ultradian rhythms comprises time series information of said ultradian rhythms. A process as described in clause 1 or any other clause wherein said starting vasomotor event transformation parameters comprises quantifying said data input to provide quantified ultradian rhythms. A process as described in clause 14 or any other clause and further comprising analyzing said data input with an analysis chosen from data analysis, signal processing, analysis of variance, spectral analysis, Fourier transform, time series analysis, wavelet transformations, power wavelet transformations, and any combination thereof. A process as described in clause 1 or any other clause wherein said step of periodically sensing ultradian rhythms of said user comprises a step of continuously sensing ultradian rhythms of said user. A computerized vasomotor events determination system comprising:
- a periodic capture ultradian rhythm sensor for placement in contact with a user;
- a computer data input from said periodic capture ultradian rhythm sensor;
- a computer processor operated vasomotor event determination model automated vasomotor event computational transform program with starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a vasomotor event determination model data transform generated from said computer processor operated vasomotor event determination model automated vasomotor event computational transform program with said starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a vasomotor event determination model transformed completed vasomotor event determination output generated from said vasomotor event determination model data transform; and
- a vasomotor event indication based on said vasomotor event determination model transformed completed vasomotor event determination output. 18. A system as described in clause 17 or any other clause and further comprising a user input to which said vasomotor event determination model completed vasomotor events determination output is responsive.
19. A system as described in clause 17 or any other clause wherein said vasomotor event determination model transformed completed vasomotor event determination output generated from said vasomotor event determination model data transform comprises vasomotor event determination model transformed completed vasomotor event determination output generated from said vasomotor event determination model data transform and based on a plurality of vasomotor events.
20. A system as described in clause 17 or any other clause wherein said ultradian rhythm sensor is capable of capturing ultradian rhythms chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof.
21. A system as described in clause 17 or any other clause wherein said vasomotor event is chosen from vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, and any combination thereof.
22. A system as described in clause 17 or 21 or any other clause wherein said vasomotor event indication is chosen from an identification of a vasomotor event and a prediction of a future vasomotor event.
23. A system as described in clause 17 or any other clause wherein said vasomotor event determination model completed vasomotor event determination output is chosen from a vasomotor event prediction model completed vasomotor event prediction output and a vasomotor event identification model completed vasomotor event identification output.
24. A system as described in clause 17 or any other clause wherein said periodic capture ultradian rhythm sensor comprises an automatic periodic capture ultradian rhythm sensor.
25. A system as described in clause 24 or any other clause wherein said automatic periodic capture ultradian rhythm sensor comprises a wearable sensing system. A system as described in clause 25 or any other clause wherein said wearable sensing system is configured to measure user data chosen from heart rate, distal body temperature, and heart rate variability. A system as described in clause 9 or any other clause wherein said wearable sensing system comprises a ring worn on said user’s finger. A system as described in clause 9 or any other clause wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger. A system as described in clause 17 or any other clause wherein said computer data input from said periodic capture ultradian rhythm sensor comprises time series information of a user’s ultradian rhythms. A system as described in clause 17 or any other clause wherein said starting vasomotor events transformation parameters comprise quantified ultradian rhythm data. A system as described in clause 17 or any other clause wherein said starting vasomotor events transformation parameters comprises data analysis of said computer data input, signal processing of said computer data input, variance analysis of said computer data input, spectral analysis of said computer data input, Fourier transform of said computer data input, time series analysis of said computer data input, wavelet transformations of said computer data input, power wavelet transformations of said computer data input, and any combination thereof. A system as described in clause 1 or any other clause wherein said periodic capture ultradian rhythm sensor comprises a continuous capture ultradian rhythm sensor. A process for determination of vasomotor events for a user comprising the steps of
- periodically sensing ultradian rhythms of said user;
- automatically accepting a data input to a computer based at least in part on said step of periodically sensing said ultradian rhythms;
- establishing in said computer a first vasomotor event determination model automated vasomotor event computational transformation program with starting vasomotor event transformation parameters;
- automatically applying said first vasomotor event determination model automated vasomotor event computational transformation program with said starting vasomotor event transformation parameters, to at least some of said ultradian rhythms to automatically create a first vasomotor event determination model data transform;
- generating a first vasomotor event determination model completed vasomotor event determination output based on said first vasomotor event determination model data transform;
- automatically varying said starting vasomotor event transformation parameters for said first vasomotor event determination model automated vasomotor event computational transformation program to establish a second vasomotor event determination model automated vasomotor event computational transformation program that differs from said first vasomotor event determination model automated vasomotor event computational transformation program in the way that it determines vasomotor events from said data input;
- automatically applying said second vasomotor event determination model automated vasomotor event computational transformation program with said automatically varied vasomotor event transformation parameters, to at least some of said ultradian rhythms to automatically create a second vasomotor event determination model data transform;
- generating a different, second vasomotor event determination model transformed completed vasomotor event determination output based on said second vasomotor event determination model data transform;
- automatically comparing said first vasomotor event determination model transformed completed vasomotor event determination output with said different, second vasomotor event determination model transformed completed vasomotor event determination output;
- automatically determining whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide an existence of a vasomotor event;
- providing a vasomotor event indication based on said step of automatically determining whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide said existence of said vasomotor event; and
- storing automatically improved vasomotor event transformation parameters that are determined to provide said likely existence of said vasomotor event for future use to automatically self improve vasomotor event determination models. A process as described in clause 33 or any other clause wherein said step of automatically varying said starting vasomotor event transformation parameters for said first vasomotor event determination model automated vasomotor event computational transformation program to establish a second vasomotor event determination model automated vasomotor event computational transformation program that differs from said first vasomotor event determination model automated vasomotor event computational transformation program in the way that it determines vasomotor event from data comprises the step of automatically cumulatively varying previously applied vasomotor event transformation parameters for said automated vasomotor event computational transformation program to establish a varied automated vasomotor event computational transformation program. A process as described in clause 33 or any other clause and further comprising the step of providing a user input to which said step of automatically determining whether said transformed vasomotor event determination output or said varied transform vasomotor event determination output is likely to provide the existence of a vasomotor event is responsive. A process as described in clause 33 or any other clause wherein said step of automatically determining whether said first vasomotor event determination model completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide the existence of a vasomotor event comprises the step of automatically applying said first vasomotor event determination model completed vasomotor event determination output and said different, second vasomotor event determination model transformed completed vasomotor event determination output to a plurality of vasomotor events. A process as described in clause 33 or any other clause wherein said ultradian rhythms is chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof. A process as described in clause 33 or any other clause wherein said vasomotor event is chosen from vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, and any combination thereof. A process as described in clause 33 or 38 or any other clause wherein said vasomotor event indication is chosen from an identification of a vasomotor event and a prediction of a future vasomotor event. A process as described in clause 33 or any other clause wherein said first vasomotor event determination model transformed completed vasomotor event determination output is chosen from a first vasomotor event prediction model completed vasomotor event prediction output and a first vasomotor event identification model transformed completed vasomotor event identification output and wherein said different, second vasomotor event determination model transformed completed vasomotor event determination output is chosen from a different, second vasomotor event prediction model transformed completed vasomotor event prediction output and a different, second vasomotor event identification model transformed completed vasomotor event identification output. A process as described in clause 33 or any other clause wherein said step of periodically sensing ultradian rhythms for said user comprises a step of automatically sensing ultradian rhythms for said user. A process as described in clause 41 or any other clause wherein said step of automatically sensing ultradian rhythms for said user comprises a wearable sensing system. A process as described in clause 42 or any other clause wherein said wearable sensing measures user data chosen from heart rate, distal body temperature, and heart rate variability. A process as described in clause 42 or any other clause wherein said wearable sensing system comprises a ring worn on said user’s finger. A process as described in clause 42 or any other clause wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger. A process as described in clause 33 or any other clause wherein said data input based at least in part on said step of periodically sensing said ultradian rhythms comprises time series information of said ultradian rhythms. A process as described in clause 33 or any other clause wherein said starting vasomotor event transformation parameters comprises quantifying said data input to provide quantified ultradian rhythms. A process as described in clause 33 or any other clause wherein said step of periodically sensing ultradian rhythms of said user comprises a step of continuously sensing ultradian rhythms of said user. A process as described in clause 47 or any other clause and further comprising analyzing said data input with an analysis chosen from data analysis, signal processing, analysis of variance, spectral analysis, Fourier transform, time series analysis, wavelet transformations, power wavelet transformations, and any combination thereof. A computerized vasomotor event determination system comprising:
- a periodic capture ultradian rhythm sensor for placement in contact with a user;
- a computer data input from said periodic capture ultradian rhythm sensor;
- a computer processor operated first vasomotor event determination model automated vasomotor event computational transformation program with starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a first vasomotor event determination model data transform generated from said computer processor operated first vasomotor event determination model automated vasomotor event computational transformation program with said starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a first vasomotor event determination model transformed completed vasomotor event determination output generated from said first vasomotor event determination model data transform;
- a computer processor operated varied automated vasomotor event computational transformation program configured to generate automated varied vasomotor event transformation parameters;
- a computer processor operated second vasomotor event determination model automated vasomotor event computational transformation program generated from said automated varied vasomotor event transformation parameters and which differs from said computer processor operated first vasomotor event determination model automated vasomotor event computational transformation program in the way it determines a vasomotor event from said computer data input;
- a computer processor operated second vasomotor event determination model data transform generated from said computer processor operated second vasomotor event determination model automated vasomotor event computational transformation program with said automated varied vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a different, second vasomotor event determination model transformed completed vasomotor event determination output generated from said computer processor operated second vasomotor event determination model data transform;
- a computer processor operated automatic vasomotor event transformation comparator responsive to said first vasomotor event determination model transformed completed vasomotor event determination output and said different, second vasomotor event determination model transformed completed vasomotor event determination output and configured to automatically determine whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide an existence of a vasomotor event;
- a vasomotor event indication based on said automatic determination of whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to said existence of said vasomotor event;
- automatically improved vasomotor event transformation parameters based on said determined vasomotor event determination model transformed completed vasomotor event determination output that is likely to provide said existence of a vasomotor event; and
- a computer processor operated automatic vasomotor event determination model self improvement program using said automatically improved vasomotor event transformation parameters for future use.
51. A system as described in clause 50 or any other clause wherein said automated varied vasomotor event transformation parameters comprises automatically cumulatively varied vasomotor event transformation parameters generated from previously determined vasomotor event transformation parameters.
52. A system as described in clause 50 or any other clause and further comprising a user input to which said first vasomotor event determination model transformed completed vasomotor event determination output and said different, second vasomotor event determination model transformed completed vasomotor event determination output is responsive.
53. A system as described in clause 50 or any other clause wherein said automatic ovulation transformation comparator is responsive to a plurality of vasomotor events.
54. A system as described in clause 50 or any other clause wherein said ultradian rhythm sensor is capable of capturing ultradian rhythms chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof. 55. A system as described in clause 50 or any other clause wherein said vasomotor event is chosen from vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, perimenopause, menopause, drug reactions, post-traumatic stress disorder, and any combination thereof.
56. A process as described in clause 50 or 55 or any other clause wherein said vasomotor event indication is chosen from a detection of a vasomotor event and a prediction of a future vasomotor event.
57. A process as described in clause 50 or any other clause wherein said first vasomotor event determination model transformed completed vasomotor event determination output is chosen from a first vasomotor event prediction model transformed completed vasomotor event prediction output and a first vasomotor event identification model transformed completed vasomotor event identification output and wherein said different, second vasomotor event determination model transformed completed vasomotor event determination output is chosen from a different, second vasomotor event prediction model transformed completed vasomotor event prediction output and a different, second vasomotor event identification model transformed completed vasomotor event identification output.
58. A system as described in clause 50 or any other clause wherein said periodic capture ultradian rhythm sensor comprises an automatic periodic capture ultradian rhythm sensor.
59. A system as described in clause 58 or any other clause wherein said automatic periodic capture ultradian rhythm sensor comprises a wearable sensing system.
60. A system as described in clause 59 or any other clause wherein said wearable sensing system is configured to measure user data chosen from heart rate, distal body temperature, and heart rate variability.
61. A system as described in clause 59 or any other clause wherein said wearable sensing system comprises a ring worn on said user’s finger.
62. A system as described in clause 59 or any other clause wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger. A system as described in clause 50 or any other clause wherein said computer data input from said periodic capture ultradian rhythm sensor comprises time series information of a user’s ultradian rhythms. A system as described in clause 50 or any other clause wherein said starting vasomotor event transformation parameters comprise quantified ultradian rhythm data. A system as described in clause 50 or any other clause wherein said starting vasomotor event transformation parameters comprises data analysis of said computer data input, signal processing of said computer data input, variance analysis of said computer data input, spectral analysis of said computer data input, Fourier transform of said computer data input, time series analysis of said computer data input, wavelet transformations of said computer data input, power wavelet transformations of said computer data input, and any combination thereof. A system as described in clause 50 or any other clause wherein said periodic capture ultradian rhythm sensor comprises a continuous capture ultradian rhythm sensor. A process for prediction of the onset of ovulation for a user comprising the steps of:
- periodically sensing ultradian rhythms for said user;
- automatically accepting a data input to a computer based at least in part on said step of periodically sensing said ultradian rhythms;
- establishing in said computer a first ovulation prediction model automated ovulation computational transformation program with starting ovulation transformation parameters;
- automatically applying said first ovulation prediction model automated ovulation computational transformation program with said starting ovulation transformation parameters, to at least some of said ultradian rhythms to automatically create a first ovulation prediction model data transform;
- generating a first ovulation prediction model completed ovulation prediction output based on said ovulation prediction model data transform;
- automatically varying said starting transformation parameters for said first ovulation prediction model automated ovulation computational transformation program to establish a second ovulation prediction model automated ovulation computational transformation program that differs from said first ovulation prediction model automated ovulation computational transformation program in the way that it predicts ovulation from said data input;
- automatically applying said second ovulation prediction model automated ovulation computational transformation program with said automatically varied ovulation transformation parameters, to at least some of said ultradian rhythms to automatically create a second ovulation prediction model data transform;
- generating a different, second ovulation prediction model transformed completed ovulation prediction output based on a data transform of said second ovulation prediction model;
- automatically comparing said first ovulation prediction model completed ovulation prediction output with said different, second ovulation prediction model transformed completed ovulation prediction output;
- automatically determining whether said first ovulation prediction model completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide an indication of the likely existence of an ovulation event;
- providing an ovulation indication based on said step of automatically determining whether said first ovulation prediction model completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide said indication of the likely existence of an ovulation event; and
- storing automatically improved ovulation transformation parameters that are determined to provide said desired selection criterion indication of the likely existence of an ovulation event for future use to automatically self improve said ovulation prediction models. A process as described in clause 67 or any other clause wherein said step of automatically varying said starting ovulation transformation parameters for said first ovulation prediction model automated ovulation computational transformation program to establish a second ovulation prediction model automated ovulation computational transformation program that differs from said first ovulation prediction model automated ovulation computational transformation program in the way that it predicts ovulation from data comprises the step of automatically cumulatively varying previously applied ovulation transformation parameters for said automated ovulation computational transformation program to establish a varied automated ovulation computational transformation program.
69. A process as described in clause 67 or any other clause and further comprising the step of providing a user input to which said step of automatically determining whether said transformed ovulation prediction output or said varied transform ovulation prediction output is likely to provide said indication of the likely existence of an ovulation event is responsive.
70. A process as described in clause 67 or any other clause wherein said step of automatically determining whether said first ovulation prediction model completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide said indication of the likely existence of an ovulation event comprises the step of automatically applying said first ovulation prediction model completed ovulation prediction output and said different, second ovulation prediction model transformed completed ovulation prediction output to a plurality of ovulation events.
71. A process as described in clause 67 or any other clause wherein said ultradian rhythms is chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof.
72. A process as described in clause 67 or any other clause wherein said ovulation event is chosen from a preovulatory change in reproductive hormones and a luteinizing hormone surge.
73. A process as described in clause 67 or any other clause wherein said ovulation event comprises a release of an egg from an ovary.
74. A process as described in clause 67 or any other clause wherein said step of periodically sensing ultradian rhythms for said user comprises a step of automatically sensing ultradian rhythms for said user. A process as described in clause 74 or any other clause wherein said step of automatically sensing ultradian rhythms for said user comprises a wearable sensing system. A process as described in clause 75 or any other clause wherein said wearable sensing system measures user data chosen from heart rate, distal body temperature, and heart rate variability. A process as described in clause 75 or any other clause wherein said wearable sensing system comprises a ring worn on said user’s finger. A process as described in clause 75 or any other clause wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger. A process as described in clause 67 or any other clause wherein said data input based at least in part on said step of periodically sensing said ultradian rhythms comprises time series information of said ultradian rhythms. A process as described in clause 67 or any other clause wherein said starting ovulation transformation parameters comprises quantifying said data input to provide quantified ultradian rhythms. A process as described in clause 80 or any other clause and further comprising analyzing said data input with an analysis chosen from data analysis, signal processing, analysis of variance, spectral analysis, Fourier transform, time series analysis, power wavelet transformations, wavelet transformations, and any combination thereof. A process as described in clause 67 or any other clause wherein said step of periodically sensing ultradian rhythms of said user comprises a step of continuously sensing ultradian rhythms of said user. A computerized ovulation prediction system comprising:
- a periodic capture ultradian rhythm sensor for placement in contact with user;
- a computer data input from said periodic capture ultradian rhythm sensor;
- a computer processor operated first ovulation prediction model automated ovulation computational transformation program with starting ovulation transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor; - a first ovulation prediction model data transform generated from said computer processor operated first ovulation prediction model automated ovulation computational transformation program with said starting ovulation transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a first ovulation prediction model transformed completed ovulation prediction output generated from said first ovulation prediction model data transform;
- a computer processor operated varied automated ovulation computational transformation program configured to generate automated varied ovulation transformation parameters;
- a computer processor operated second ovulation prediction model automated ovulation computational transformation program generated from said automated varied ovulation transformation parameters and which differs from said computer processor operated first ovulation prediction model automated ovulation computational transformation program in the way it predicts ovulation from said computer data input;
- a computer processor operated second ovulation prediction model data transform generated from said computer processor operated second ovulation prediction model automated ovulation computational transformation program with said automated varied ovulation transformation parameters responsive to computer data input from said periodic capture ultradian rhythm sensor;
- a different, second ovulation prediction model transformed completed ovulation prediction output generated from a data transform of said second prediction model;
- a computer processor operated automatic ovulation transformation comparator responsive to said first ovulation prediction model transformed completed ovulation prediction output and said different, second ovulation prediction model transformed completed ovulation prediction output and configured to automatically determine whether said first ovulation prediction model transformed completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide an indication of the likely existence of an ovulation event; - an ovulation indication based on said automatic determination of whether said first ovulation prediction model transformed completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide said indication of the likely existence of an ovulation event;
- automatically improved ovulation transformation parameters based on said determined ovulation prediction model transformed completed ovulation prediction output that is likely to provide said indication of the likely existence of an ovulation event; and
- a computer processor operated automatic ovulation prediction model self improvement program using said automatically improved ovulation transformation parameters for future use. A system as described in clause 83 or any other clause wherein said automated varied ovulation transformation parameters comprises automatically cumulatively varied ovulation transformation parameters generated from previously determined ovulation transformation parameters. A system as described in clause 83 or any other clause and further comprising a user input to which said an automatic ovulation transformation comparator is responsive. A system as described in clause 83 or any other clause wherein said automatic ovulation transformation comparator is responsive to a plurality of ovulation events. A system as described in clause 83 or any other clause wherein said ultradian rhythm sensor is capable of capturing ultradian rhythms chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof. A system as described in clause 83 or any other clause wherein said ovulation event is chosen from a preovulatory change in reproductive hormones and a luteinizing hormone surge. A system as described in clause 83 or any other clause wherein said ovulation event comprises a release of an egg from an ovary. A system as described in clause 83 or any other clause wherein said periodic capture ultradian rhythm sensor comprises an automatic periodic capture ultradian rhythm sensor. A system as described in clause 90 or any other clause wherein said automatic periodic capture ultradian rhythm sensor comprises a wearable sensing system. A system as described in clause 91 or any other clause wherein said wearable sensing system is configured to measure user data chosen from heart rate, distal body temperature, and heart rate variability. A system as described in clause 91 or any other clause wherein said wearable sensing system comprises a ring worn on said user’s finger. A system as described in clause 91 or any other clause wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger. A system as described in clause 83 or any other clause wherein said computer data input from said periodic capture ultradian rhythm sensor comprises time series information of a user’s ultradian rhythms. A system as described in clause 83 or any other clause wherein said starting ovulation transformation parameters comprise quantified ultradian rhythm data. A system as described in clause 83 or any other clause wherein said starting ovulation transformation parameters comprises data analysis of said computer data input, signal processing of said computer data input, variance analysis of said computer data input, spectral analysis of said computer data input, Fourier transform of said computer data input, time series analysis of said computer data input, wavelet transformations of said computer data input, power wavelet transformations of said computer data input, and any combination thereof. A system as described in clause 83 or any other clause wherein said periodic capture ultradian rhythm sensor comprises a continuous capture ultradian rhythm sensor. A system as described in clause 18, 52, or 85 or any other clause wherein said user input comprises user’s symptoms, signs of depression, vaginal dryness, libido, memory function, family history, if the user smokes, if the user consumes alcohol, and any combination thereof. 100. A system as described in clause 1, 35, or 69 or any other clause wherein said user input comprises user’s symptoms, signs of depression, vaginal dryness, libido, memory function, family history, if the user smokes, if the user consumes alcohol, and any combination thereof.
As can be easily understood from the foregoing, the basic concepts of the application may be embodied in a variety of ways. It involves both ultradian rhythm determination techniques as well as devices to accomplish the appropriate ultradian rhythm determination. In this application, the ultradian rhythm determination techniques are disclosed as part of the results shown to be achieved by the various devices described and as steps which are inherent to utilization. They are simply the natural result of utilizing the devices as intended and described. In addition, while some devices are disclosed, it should be understood that these not only accomplish certain methods but also can be varied in a number of ways. Importantly, as to all of the foregoing, all of these facets should be understood to be encompassed by this disclosure.
The discussion included in this application is intended to serve as a basic description. The reader should be aware that the specific discussion may not explicitly describe all embodiments possible; many alternatives are implicit. It also may not fully explain the generic nature of the invention and may not explicitly show how each feature or element can actually be representative of a broader function or of a great variety of alternative or equivalent elements. As one example, terms of degree, terms of approximation, and/or relative terms may be used. These may include terms such as the words: substantially, about, only, and the like. These words and types of words are to be understood in a dictionary sense as terms that encompass an ample or considerable amount, quantity, size, etc. as well as terms that encompass largely but not wholly that which is specified. Further, for this application if or when used, terms of degree, terms of approximation, and/or relative terms should be understood as also encompassing more precise and even quantitative values that include various levels of precision and the possibility of claims that address a number of quantitative options and alternatives. For example, to the extent ultimately used, the existence or non-existence of a substance or condition in a particular input, output, or at a particular stage can be specified as substantially only x or substantially free of x, as a value of about x, or such other similar language. Using percentage values as one example, these types of terms should be understood as encompassing the options of percentage values that include 99.5%, 99%, 97%, 95%, 92% or even 90% of the specified value or relative condition; correspondingly for values at the other end of the spectrum (e.g., substantially free of x, these should be understood as encompassing the options of percentage values that include not more than 0.5%, 1%, 3%, 5%, 8% or even 10% of the specified value or relative condition, all whether by volume or by weight as either may be specified. In context, these should be understood by a person of ordinary skill as being disclosed and included whether in an absolute value sense or in valuing one set of or substance as compared to the value of a second set of or substance. Again, these are implicitly included in this disclosure and should (and, it is believed, would) be understood to a person of ordinary skill in this field. Where the invention is described in device-oriented terminology, each element of the device implicitly performs a function. Apparatus claims may not only be included for the device described, but also method or process claims may be included to address the functions the invention and each element performs. Neither the description nor the terminology is intended to limit the scope of the claims that will be included in any subsequent patent application.
It should also be understood that a variety of changes may be made without departing from the essence of the invention. Such changes are also implicitly included in the description. They still fall within the scope of this invention. A broad disclosure encompassing both the explicit embodiment(s) shown, the great variety of implicit alternative embodiments, and the broad methods or processes and the like are encompassed by this disclosure and may be relied upon when drafting the claims for any subsequent patent application. It should be understood that such language changes and broader or more detailed claiming may be accomplished at a later date (such as by any required deadline) or in the event the applicant subsequently seeks a patent filing based on this filing. With this understanding, the reader should be aware that this disclosure is to be understood to support any subsequently filed patent application that may seek examination of as broad a base of claims as deemed within the applicant's right and may be designed to yield a patent covering numerous aspects of the invention both independently and as an overall system.
Further, each of the various elements of the invention and claims may also be achieved in a variety of manners. Additionally, when used or implied, an element is to be understood as encompassing individual as well as plural structures that may or may not be physically connected. This disclosure should be understood to encompass each such variation, be it a variation of an embodiment of any apparatus embodiment, a method or process embodiment, or even merely a variation of any element of these. Particularly, it should be understood that as the disclosure relates to elements of the invention, the words for each element may be expressed by equivalent apparatus terms or method terms — even if only the function or result is the same. Such equivalent, broader, or even more generic terms should be considered to be encompassed in the description of each element or action. Such terms can be substituted where desired to make explicit the implicitly broad coverage to which this invention is entitled. As but one example, it should be understood that all actions may be expressed as a means for taking that action or as an element which causes that action. Similarly, each physical element disclosed should be understood to encompass a disclosure of the action which that physical element facilitates. Regarding this last aspect, as but one example, the disclosure of a “measurement” should be understood to encompass disclosure of the act of “measuring” — whether explicitly discussed or not — and, conversely, were there effectively disclosure of the act of “measuring”, such a disclosure should be understood to encompass disclosure of a “measurement” and even a “means for measuring.” Such changes and alternative terms are to be understood to be explicitly included in the description. Further, each such means (whether explicitly so described or not) should be understood as encompassing all elements that can perform the given function, and all descriptions of elements that perform a described function should be understood as a non-limiting example of means for performing that function. As other nonlimiting examples, it should be understood that claim elements can also be expressed as any of: components that are configured to, or configured and arranged to, achieve a particular result, use, purpose, situation, function, or operation, or as components that are capable of achieving a particular result, use, purpose, situation, function, or operation,. All should be understood as within the scope of this disclosure and written description.
Any patents, publications, or other references mentioned in this application for patent are hereby incorporated by reference. Any priority case(s) claimed by this application is hereby appended and hereby incorporated by reference. In addition, as to each term used it should be understood that unless its utilization in this application is inconsistent with a broadly supporting interpretation, common dictionary definitions should be understood as incorporated for each term and all definitions, alternative terms, and synonyms such as contained in the Random House Webster’s Unabridged Dictionary, second edition are hereby incorporated by reference. Finally, all references listed in the list of reference below or other information statement filed with the application are hereby appended and hereby incorporated by reference, however, as to each of the above, to the extent that such information or statements incorporated by reference might be considered inconsistent with the patenting of this/these invention(s) such statements are expressly not to be considered as made by the applicant(s).
US PATENT APPLICATION PUBLICATIONS
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Thus, the applicant(s) should be understood to have support to claim and make a statement of invention to at least: i) each of the measuring devices as herein disclosed and described, ii) the related methods disclosed and described, iii) similar, equivalent, and even implicit variations of each of these devices and methods, iv) those alternative designs which accomplish each of the functions shown as are disclosed and described, v) those alternative designs and methods which accomplish each of the functions shown as are implicit to accomplish that which is disclosed and described, vi) each feature, component, and step shown as separate and independent inventions, vii) the applications enhanced by the various systems or components disclosed, viii) the resulting products produced by such processes, methods, systems or components, ix) each system, method, and element shown or described as now applied to any specific field or devices mentioned, x) methods and apparatuses substantially as described hereinbefore and with reference to any of the accompanying examples, xi) an apparatus for performing the methods described herein comprising means for performing the steps, xii) the various combinations and permutations of each of the elements disclosed, xiii) each potentially dependent claim or concept as a dependency on each and every one of the independent claims or concepts presented, and xiv) all inventions described herein.
In addition and as to computer aspects and each aspect amenable to programming or other electronic automation, it should be understood that in characterizing these and all other aspects of the invention - whether characterized as a device, a capability, an element, or otherwise, because all of these can be implemented via software, hardware, or even firmware structures as set up for a general purpose computer, a programmed chip or chipset, an ASIC, application specific controller, subroutine, or other known programmable or circuit specific structure — it should be understood that all such aspects are at least defined by structures including, as person of ordinary skill in the art would well recognize: hardware circuitry, firmware, programmed application specific components, and even a general purpose computer programmed to accomplish the identified aspect. For such items implemented by programmable features, the applicant(s) should be understood to have support to claim and make a statement of invention to at least: xv) processes performed with the aid of or on a computer, machine, or computing machine as described throughout the above discussion, xvi) a programmable apparatus as described throughout the above discussion, xvii) a computer readable memory encoded with data to direct a computer comprising means or elements which function as described throughout the above discussion, xviii) a computer, machine, or computing machine configured as herein disclosed and described, xix) individual or combined subroutines and programs as herein disclosed and described, xx) a carrier medium carrying computer readable code for control of a computer to carry out separately each and every individual and combined method described herein or in any claim, xxi) a computer program to perform separately each and every individual and combined method disclosed, xxii) a computer program containing all and each combination of means for performing each and every individual and combined step disclosed, xxiii) a storage medium storing each computer program disclosed, xxiv) a signal carrying a computer program disclosed, xxv) a processor executing instructions that act to achieve the steps and activities detailed, xxvi) circuitry configurations (including configurations of transistors, gates, and the like) that act to sequence and/or cause actions as detailed, xxvii) computer readable medium(s) storing instructions to execute the steps and cause activities detailed, xxviii) the related methods disclosed and described, xxix) similar, equivalent, and even implicit variations of each of these systems and methods, xxx) those alternative designs which accomplish each of the functions shown as are disclosed and described, xxxi) those alternative designs and methods which accomplish each of the functions shown as are implicit to accomplish that which is disclosed and described, xxxii) each feature, component, and step shown as separate and independent inventions, and xxxiii) the various combinations of each of the above and of any aspect, all without limiting other aspects in addition. With regard to claims whether now or later presented for examination, it should be understood that for practical reasons and so as to avoid great expansion of the examination burden, the applicant may at any time present only initial claims or perhaps only initial claims with only initial dependencies. The office and any third persons interested in potential scope of this or subsequent applications should understand that broader claims may be presented at a later date in this case, in a case claiming the benefit of this case, or in any continuation in spite of any preliminary amendments, other amendments, claim language, or arguments presented, thus throughout the pendency of any case there is no intention to disclaim or surrender any potential subject matter. It should be understood that if or when broader claims are presented, such may require that any relevant prior art that may have been considered at any prior time may need to be re-visited since it is possible that to the extent any amendments, claim language, or arguments presented in this or any subsequent application are considered as made to avoid such prior art, such reasons may be eliminated by later presented claims or the like. Both the examiner and any person otherwise interested in existing or later potential coverage, or considering if there has at any time been any possibility of an indication of disclaimer or surrender of potential coverage, should be aware that no such surrender or disclaimer is ever intended or ever exists in this or any subsequent application. Limitations such as arose in Hakim v. Cannon Avent Group, PLC, 479 F.3d 1313 (Fed. Cir 2007), or the like are expressly not intended in this or any subsequent related matter. In addition, support should be understood to exist to the degree required under new matter laws — including but not limited to European Patent Convention Article 123(2) and United States Patent Law 35 USC 132 or other such laws- - to permit the addition of any of the various dependencies or other elements presented under one independent claim or concept as dependencies or elements under any other independent claim or concept. In drafting any claims at any time whether in this application or in any subsequent application, it should also be understood that the applicant has intended to capture as full and broad a scope of coverage as legally available. To the extent that insubstantial substitutes are made, to the extent that the applicant did not in fact draft any claim so as to literally encompass any particular embodiment, and to the extent otherwise applicable, the applicant should not be understood to have in any way intended to or actually relinquished such coverage as the applicant simply may not have been able to anticipate all eventualities; one skilled in the art, should not be reasonably expected to have drafted a claim that would have literally encompassed such alternative embodiments.
Further, if or when used, the use of the transitional phrase “comprising” is used to maintain the “open-end” claims herein, according to traditional claim interpretation. Thus, unless the context requires otherwise, it should be understood that the term “comprise” or variations such as “comprises” or “comprising”, are intended to imply the inclusion of a stated element or step or group of elements or steps but not the exclusion of any other element or step or group of elements or steps. Such terms should be interpreted in their most expansive form so as to afford the applicant the broadest coverage legally permissible. The use of the phrase, “or any other claim” is used to provide support for any claim to be dependent on any other claim, such as another dependent claim, another independent claim, a previously listed claim, a subsequently listed claim, and the like. As one clarifying example, if a claim were dependent “on claim 20 or any other claim” or the like, it could be re-drafted as dependent on claim 1, claim 15, or even claim 25 (if such were to exist) if desired and still fall with the disclosure. It should be understood that this phrase also provides support for any combination of elements in the claims and even incorporates any desired proper antecedent basis for certain claim combinations such as with combinations of method, apparatus, process, and the like claims.
Finally, any claims set forth at any time are hereby incorporated by reference as part of this description of the invention, and the applicant expressly reserves the right to use all of or a portion of such incorporated content of such claims as additional description to support any of or all of the claims or any element or component thereof, and the applicant further expressly reserves the right to move any portion of or all of the incorporated content of such claims or any element or component thereof from the description into the claims or vice-versa as necessary to define the matter for which protection is sought by this application or by any subsequent continuation, division, or continuation-in-part application thereof, or to obtain any benefit of, reduction in fees pursuant to, or to comply with the patent laws, rules, or regulations of any country or treaty, and such content incorporated by reference shall survive during the entire pendency of this application including any subsequent continuation, division, or continuation-in-part application thereof or any reissue or extension thereon.

Claims

What is claimed is:
1. A process for determination of vasomotor events for a user comprising the steps of:
- periodically sensing ultradian rhythms of said user;
- automatically accepting a data input to a computer based at least in part on said step of periodically sensing said ultradian rhythms;
- establishing in said computer a vasomotor event determination model automated vasomotor event computational transform program with starting vasomotor event transformation parameters;
- automatically applying said vasomotor event determination model automated vasomotor event computational transform program with said starting vasomotor event transformation parameters, to at least some of said data input to automatically create a vasomotor event determination model data transform;
- generating a vasomotor event determination model completed vasomotor event determination output based on said vasomotor event determination model data transform; and
- providing a vasomotor event indication based on said step of generating said vasomotor event determination model completed vasomotor event determination output.
2. A process as described in claim 1 and further comprising the step of providing a user input to which said step of generating a vasomotor event determination model completed vasomotor event determination output is responsive.
3. A process as described in claim 1 wherein said step of generating a vasomotor event determination model completed vasomotor event determination output based on said vasomotor event determination model data transform comprises a step of generating a vasomotor event determination model completed vasomotor event determination output based on said vasomotor event determination model data transform and a plurality of vasomotor events.
4. A process as described in claim 1 wherein said ultradian rhythms is chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof.
5. A process as described in claim 1 wherein said vasomotor event is chosen from vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, and any combination thereof.
6. A process as described in claim 1 or 5 wherein said vasomotor event indication is chosen from an identification of a vasomotor event and a prediction of a future vasomotor event.
7. A process as described in claim 1 wherein said vasomotor event determination model completed vasomotor event determination output is chosen from a vasomotor event prediction model completed vasomotor event prediction output and a vasomotor event identification model completed vasomotor event identification output.
8. A process as described in claim 1 wherein said step of periodically sensing ultradian rhythms for said user comprises a step of automatically sensing ultradian rhythms for said user.
9. A process as described in claim 8 wherein said step of automatically sensing ultradian rhythms for said user comprises a wearable sensing system.
10. A process as described in claim 9 wherein said wearable sensing system measures user data chosen from heart rate, distal body temperature, and heart rate variability.
11. A process as described in claim 9 wherein said wearable sensing system comprises a ring worn on said user’s finger.
12. A process as described in claim 9 wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger.
13. A process as described in claim 1 wherein said data input based at least in part on said step of periodically sensing said ultradian rhythms comprises time series information of said ultradian rhythms.
14. A process as described in claim 1 wherein said starting vasomotor event transformation parameters comprises quantifying said data input to provide quantified ultradian rhythms.
15. A process as described in claim 14 and further comprising analyzing said data input with an analysis chosen from data analysis, signal processing, analysis of variance, spectral analysis, Fourier transform, time series analysis, wavelet transformations, power wavelet transformations, and any combination thereof.
16. A process as described in claim 1 wherein said step of periodically sensing ultradian rhythms of said user comprises a step of continuously sensing ultradian rhythms of said user.
17. A computerized vasomotor events determination system comprising:
- a periodic capture ultradian rhythm sensor for placement in contact with a user;
- a computer data input from said periodic capture ultradian rhythm sensor;
- a computer processor operated vasomotor event determination model automated vasomotor event computational transform program with starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a vasomotor event determination model data transform generated from said computer processor operated vasomotor event determination model automated vasomotor event computational transform program with said starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a vasomotor event determination model transformed completed vasomotor event determination output generated from said vasomotor event determination model data transform; and
- a vasomotor event indication based on said vasomotor event determination model transformed completed vasomotor event determination output.
18. A system as described in claim 17 and further comprising a user input to which said vasomotor event determination model completed vasomotor events determination output is responsive.
19. A system as described in claim 17 wherein said vasomotor event determination model transformed completed vasomotor event determination output generated from said vasomotor event determination model data transform comprises vasomotor event determination model transformed completed vasomotor event determination output generated from said vasomotor event determination model data transform and based on a plurality of vasomotor events. A system as described in claim 17 wherein said ultradian rhythm sensor is capable of capturing ultradian rhythms chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof. A system as described in claim 17 wherein said vasomotor event is chosen from vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, and any combination thereof. A system as described in claim 17 or 21 wherein said vasomotor event indication is chosen from an identification of a vasomotor event and a prediction of a future vasomotor event. A system as described in claim 17 wherein said vasomotor event determination model completed vasomotor event determination output is chosen from a vasomotor event prediction model completed vasomotor event prediction output and a vasomotor event identification model completed vasomotor event identification output. A system as described in claim 17 wherein said periodic capture ultradian rhythm sensor comprises an automatic periodic capture ultradian rhythm sensor. A system as described in claim 24 wherein said automatic periodic capture ultradian rhythm sensor comprises a wearable sensing system. A system as described in claim 25 wherein said wearable sensing system is configured to measure user data chosen from heart rate, distal body temperature, and heart rate variability. A system as described in claim 9 wherein said wearable sensing system comprises a ring worn on said user’s finger. A system as described in claim 9 wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger. A system as described in claim 17 wherein said computer data input from said periodic capture ultradian rhythm sensor comprises time series information of a user’s ultradian rhythms. A system as described in claim 17 wherein said starting vasomotor events transformation parameters comprise quantified ultradian rhythm data. A system as described in claim 17 wherein said starting vasomotor events transformation parameters comprises data analysis of said computer data input, signal processing of said computer data input, variance analysis of said computer data input, spectral analysis of said computer data input, Fourier transform of said computer data input, time series analysis of said computer data input, wavelet transformations of said computer data input, power wavelet transformations of said computer data input, and any combination thereof. A system as described in claim 1 wherein said periodic capture ultradian rhythm sensor comprises a continuous capture ultradian rhythm sensor. A process for determination of vasomotor events for a user comprising the steps of:
- periodically sensing ultradian rhythms of said user;
- automatically accepting a data input to a computer based at least in part on said step of periodically sensing said ultradian rhythms;
- establishing in said computer a first vasomotor event determination model automated vasomotor event computational transformation program with starting vasomotor event transformation parameters;
- automatically applying said first vasomotor event determination model automated vasomotor event computational transformation program with said starting vasomotor event transformation parameters, to at least some of said ultradian rhythms to automatically create a first vasomotor event determination model data transform;
- generating a first vasomotor event determination model completed vasomotor event determination output based on said first vasomotor event determination model data transform;
- automatically varying said starting vasomotor event transformation parameters for said first vasomotor event determination model automated vasomotor event computational transformation program to establish a second vasomotor event determination model automated vasomotor event computational transformation program that differs from said first vasomotor event determination model automated vasomotor event computational transformation program in the way that it determines vasomotor events from said data input;
- automatically applying said second vasomotor event determination model automated vasomotor event computational transformation program with said automatically varied vasomotor event transformation parameters, to at least some of said ultradian rhythms to automatically create a second vasomotor event determination model data transform;
- generating a different, second vasomotor event determination model transformed completed vasomotor event determination output based on said second vasomotor event determination model data transform;
- automatically comparing said first vasomotor event determination model transformed completed vasomotor event determination output with said different, second vasomotor event determination model transformed completed vasomotor event determination output;
- automatically determining whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide an existence of a vasomotor event;
- providing a vasomotor event indication based on said step of automatically determining whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide said existence of said vasomotor event; and
- storing automatically improved vasomotor event transformation parameters that are determined to provide said likely existence of said vasomotor event for future use to automatically self improve vasomotor event determination models. A process as described in claim 33 wherein said step of automatically varying said starting vasomotor event transformation parameters for said first vasomotor event determination model automated vasomotor event computational transformation program to establish a second vasomotor event determination model automated vasomotor event computational transformation program that differs from said first vasomotor event determination model automated vasomotor event computational transformation program in the way that it determines vasomotor event from data comprises the step of automatically cumulatively varying previously applied vasomotor event transformation parameters for said automated vasomotor event computational transformation program to establish a varied automated vasomotor event computational transformation program. A process as described in claim 33 and further comprising the step of providing a user input to which said step of automatically determining whether said transformed vasomotor event determination output or said varied transform vasomotor event determination output is likely to provide the existence of a vasomotor event is responsive. A process as described in claim 33 wherein said step of automatically determining whether said first vasomotor event determination model completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide the existence of a vasomotor event comprises the step of automatically applying said first vasomotor event determination model completed vasomotor event determination output and said different, second vasomotor event determination model transformed completed vasomotor event determination output to a plurality of vasomotor events. A process as described in claim 33 wherein said ultradian rhythms is chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof. A process as described in claim 33 wherein said vasomotor event is chosen from vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, menopause, drug reactions, post-traumatic stress disorder, and any combination thereof.
39. A process as described in claim 33 or 38 wherein said vasomotor event indication is chosen from an identification of a vasomotor event and a prediction of a future vasomotor event.
40. A process as described in claim 33 wherein said first vasomotor event determination model transformed completed vasomotor event determination output is chosen from a first vasomotor event prediction model completed vasomotor event prediction output and a first vasomotor event identification model transformed completed vasomotor event identification output and wherein said different, second vasomotor event determination model transformed completed vasomotor event determination output is chosen from a different, second vasomotor event prediction model transformed completed vasomotor event prediction output and a different, second vasomotor event identification model transformed completed vasomotor event identification output.
41. A process as described in claim 33 wherein said step of periodically sensing ultradian rhythms for said user comprises a step of automatically sensing ultradian rhythms for said user.
42. A process as described in claim 41 wherein said step of automatically sensing ultradian rhythms for said user comprises a wearable sensing system.
43. A process as described in claim 42 wherein said wearable sensing measures user data chosen from heart rate, distal body temperature, and heart rate variability.
44. A process as described in claim 42 wherein said wearable sensing system comprises a ring worn on said user’s finger.
45. A process as described in claim 42 wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger.
46. A process as described in claim 33 wherein said data input based at least in part on said step of periodically sensing said ultradian rhythms comprises time series information of said ultradian rhythms. A process as described in claim 33 wherein said starting vasomotor event transformation parameters comprises quantifying said data input to provide quantified ultradian rhythms. A process as described in claim 33 wherein said step of periodically sensing ultradian rhythms of said user comprises a step of continuously sensing ultradian rhythms of said user. A process as described in claim 47 and further comprising analyzing said data input with an analysis chosen from data analysis, signal processing, analysis of variance, spectral analysis, Fourier transform, time series analysis, wavelet transformations, power wavelet transformations, and any combination thereof. A computerized vasomotor event determination system comprising:
- a periodic capture ultradian rhythm sensor for placement in contact with a user;
- a computer data input from said periodic capture ultradian rhythm sensor;
- a computer processor operated first vasomotor event determination model automated vasomotor event computational transformation program with starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a first vasomotor event determination model data transform generated from said computer processor operated first vasomotor event determination model automated vasomotor event computational transformation program with said starting vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a first vasomotor event determination model transformed completed vasomotor event determination output generated from said first vasomotor event determination model data transform;
- a computer processor operated varied automated vasomotor event computational transformation program configured to generate automated varied vasomotor event transformation parameters;
- a computer processor operated second vasomotor event determination model automated vasomotor event computational transformation program generated from said automated varied vasomotor event transformation parameters and which differs from said computer processor operated first vasomotor event determination model automated vasomotor event computational transformation program in the way it determines a vasomotor event from said computer data input;
- a computer processor operated second vasomotor event determination model data transform generated from said computer processor operated second vasomotor event determination model automated vasomotor event computational transformation program with said automated varied vasomotor event transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a different, second vasomotor event determination model transformed completed vasomotor event determination output generated from said computer processor operated second vasomotor event determination model data transform;
- a computer processor operated automatic vasomotor event transformation comparator responsive to said first vasomotor event determination model transformed completed vasomotor event determination output and said different, second vasomotor event determination model transformed completed vasomotor event determination output and configured to automatically determine whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to provide an existence of a vasomotor event;
- a vasomotor event indication based on said automatic determination of whether said first vasomotor event determination model transformed completed vasomotor event determination output or said different, second vasomotor event determination model transformed completed vasomotor event determination output is likely to said existence of said vasomotor event;
- automatically improved vasomotor event transformation parameters based on said determined vasomotor event determination model transformed completed vasomotor event determination output that is likely to provide said existence of a vasomotor event; and
- a computer processor operated automatic vasomotor event determination model self improvement program using said automatically improved vasomotor event transformation parameters for future use.
51. A system as described in claim 50 wherein said automated varied vasomotor event transformation parameters comprises automatically cumulatively varied vasomotor event transformation parameters generated from previously determined vasomotor event transformation parameters.
52. A system as described in claim 50 and further comprising a user input to which said first vasomotor event determination model transformed completed vasomotor event determination output and said different, second vasomotor event determination model transformed completed vasomotor event determination output is responsive.
53. A system as described in claim 50 wherein said automatic ovulation transformation comparator is responsive to a plurality of vasomotor events.
54. A system as described in claim 50 wherein said ultradian rhythm sensor is capable of capturing ultradian rhythms chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof.
55. A system as described in claim 50 wherein said vasomotor event is chosen from vasomotor symptoms, night sweats, hot flashes, flushes, heart palpitations, sleep disturbances, changes in blood pressure during perimenopause, perimenopause, menopause, drug reactions, post-traumatic stress disorder, and any combination thereof.
56. A process as described in claim 50 or 55 wherein said vasomotor event indication is chosen from a detection of a vasomotor event and a prediction of a future vasomotor event.
57. A process as described in claim 50 wherein said first vasomotor event determination model transformed completed vasomotor event determination output is chosen from a first vasomotor event prediction model transformed completed vasomotor event prediction output and a first vasomotor event identification model transformed
60 completed vasomotor event identification output and wherein said different, second vasomotor event determination model transformed completed vasomotor event determination output is chosen from a different, second vasomotor event prediction model transformed completed vasomotor event prediction output and a different, second vasomotor event identification model transformed completed vasomotor event identification output. A system as described in claim 50 wherein said periodic capture ultradian rhythm sensor comprises an automatic periodic capture ultradian rhythm sensor. A system as described in claim 58 wherein said automatic periodic capture ultradian rhythm sensor comprises a wearable sensing system. A system as described in claim 59 wherein said wearable sensing system is configured to measure user data chosen from heart rate, distal body temperature, and heart rate variability. A system as described in claim 59 wherein said wearable sensing system comprises a ring worn on said user’s finger. A system as described in claim 59 wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger. A system as described in claim 50 wherein said computer data input from said periodic capture ultradian rhythm sensor comprises time series information of a user’s ultradian rhythms. A system as described in claim 50 wherein said starting vasomotor event transformation parameters comprise quantified ultradian rhythm data. A system as described in claim 50 wherein said starting vasomotor event transformation parameters comprises data analysis of said computer data input, signal processing of said computer data input, variance analysis of said computer data input, spectral analysis of said computer data input, Fourier transform of said computer data input, time series analysis of said computer data input, wavelet transformations of said computer data input, power wavelet transformations of said computer data input, and any combination thereof.
61 A system as described in claim 50 wherein said periodic capture ultradian rhythm sensor comprises a continuous capture ultradian rhythm sensor. A process for prediction of the onset of ovulation for a user comprising the steps of:
- periodically sensing ultradian rhythms for said user;
- automatically accepting a data input to a computer based at least in part on said step of periodically sensing said ultradian rhythms;
- establishing in said computer a first ovulation prediction model automated ovulation computational transformation program with starting ovulation transformation parameters;
- automatically applying said first ovulation prediction model automated ovulation computational transformation program with said starting ovulation transformation parameters, to at least some of said ultradian rhythms to automatically create a first ovulation prediction model data transform;
- generating a first ovulation prediction model completed ovulation prediction output based on said ovulation prediction model data transform;
- automatically varying said starting transformation parameters for said first ovulation prediction model automated ovulation computational transformation program to establish a second ovulation prediction model automated ovulation computational transformation program that differs from said first ovulation prediction model automated ovulation computational transformation program in the way that it predicts ovulation from said data input;
- automatically applying said second ovulation prediction model automated ovulation computational transformation program with said automatically varied ovulation transformation parameters, to at least some of said ultradian rhythms to automatically create a second ovulation prediction model data transform;
- generating a different, second ovulation prediction model transformed completed ovulation prediction output based on a data transform of said second ovulation prediction model;
- automatically comparing said first ovulation prediction model completed ovulation prediction output with said different, second ovulation prediction model transformed completed ovulation prediction output;
62 - automatically determining whether said first ovulation prediction model completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide an indication of the likely existence of an ovulation event;
- providing an ovulation indication based on said step of automatically determining whether said first ovulation prediction model completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide said indication of the likely existence of an ovulation event; and
- storing automatically improved ovulation transformation parameters that are determined to provide said desired selection criterion indication of the likely existence of an ovulation event for future use to automatically self improve said ovulation prediction models. A process as described in claim 67 wherein said step of automatically varying said starting ovulation transformation parameters for said first ovulation prediction model automated ovulation computational transformation program to establish a second ovulation prediction model automated ovulation computational transformation program that differs from said first ovulation prediction model automated ovulation computational transformation program in the way that it predicts ovulation from data comprises the step of automatically cumulatively varying previously applied ovulation transformation parameters for said automated ovulation computational transformation program to establish a varied automated ovulation computational transformation program. A process as described in claim 67 and further comprising the step of providing a user input to which said step of automatically determining whether said transformed ovulation prediction output or said varied transform ovulation prediction output is likely to provide said indication of the likely existence of an ovulation event is responsive. A process as described in claim 67 wherein said step of automatically determining whether said first ovulation prediction model completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide said indication of the likely existence of an
63 ovulation event comprises the step of automatically applying said first ovulation prediction model completed ovulation prediction output and said different, second ovulation prediction model transformed completed ovulation prediction output to a plurality of ovulation events.
71. A process as described in claim 67 wherein said ultradian rhythms is chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof.
72. A process as described in claim 67 wherein said ovulation event is chosen from a preovulatory change in reproductive hormones and a luteinizing hormone surge.
73. A process as described in claim 67 wherein said ovulation event comprises a release of an egg from an ovary.
74. A process as described in claim 67 wherein said step of periodically sensing ultradian rhythms for said user comprises a step of automatically sensing ultradian rhythms for said user.
75. A process as described in claim 74 wherein said step of automatically sensing ultradian rhythms for said user comprises a wearable sensing system.
76. A process as described in claim 75 wherein said wearable sensing system measures user data chosen from heart rate, distal body temperature, and heart rate variability.
77. A process as described in claim 75 wherein said wearable sensing system comprises a ring worn on said user’s finger.
78. A process as described in claim 75 wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger.
79. A process as described in claim 67 wherein said data input based at least in part on said step of periodically sensing said ultradian rhythms comprises time series information of said ultradian rhythms.
80. A process as described in claim 67 wherein said starting ovulation transformation parameters comprises quantifying said data input to provide quantified ultradian rhythms.
64
81. A process as described in claim 80 and further comprising analyzing said data input with an analysis chosen from data analysis, signal processing, analysis of variance, spectral analysis, Fourier transform, time series analysis, power wavelet transformations, wavelet transformations, and any combination thereof.
82. A process as described in claim 67 wherein said step of periodically sensing ultradian rhythms of said user comprises a step of continuously sensing ultradian rhythms of said user.
83. A computerized ovulation prediction system comprising:
- a periodic capture ultradian rhythm sensor for placement in contact with user;
- a computer data input from said periodic capture ultradian rhythm sensor;
- a computer processor operated first ovulation prediction model automated ovulation computational transformation program with starting ovulation transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a first ovulation prediction model data transform generated from said computer processor operated first ovulation prediction model automated ovulation computational transformation program with said starting ovulation transformation parameters responsive to said computer data input from said periodic capture ultradian rhythm sensor;
- a first ovulation prediction model transformed completed ovulation prediction output generated from said first ovulation prediction model data transform;
- a computer processor operated varied automated ovulation computational transformation program configured to generate automated varied ovulation transformation parameters;
- a computer processor operated second ovulation prediction model automated ovulation computational transformation program generated from said automated varied ovulation transformation parameters and which differs from said computer processor operated first ovulation prediction model automated ovulation computational transformation program in the way it predicts ovulation from said computer data input;
65 - a computer processor operated second ovulation prediction model data transform generated from said computer processor operated second ovulation prediction model automated ovulation computational transformation program with said automated varied ovulation transformation parameters responsive to computer data input from said periodic capture ultradian rhythm sensor;
- a different, second ovulation prediction model transformed completed ovulation prediction output generated from a data transform of said second prediction model;
- a computer processor operated automatic ovulation transformation comparator responsive to said first ovulation prediction model transformed completed ovulation prediction output and said different, second ovulation prediction model transformed completed ovulation prediction output and configured to automatically determine whether said first ovulation prediction model transformed completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide an indication of the likely existence of an ovulation event;
- an ovulation indication based on said automatic determination of whether said first ovulation prediction model transformed completed ovulation prediction output or said different, second ovulation prediction model transformed completed ovulation prediction output is likely to provide said indication of the likely existence of an ovulation event;
- automatically improved ovulation transformation parameters based on said determined ovulation prediction model transformed completed ovulation prediction output that is likely to provide said indication of the likely existence of an ovulation event; and
- a computer processor operated automatic ovulation prediction model self improvement program using said automatically improved ovulation transformation parameters for future use. A system as described in claim 83 wherein said automated varied ovulation transformation parameters comprises automatically cumulatively varied ovulation transformation parameters generated from previously determined ovulation transformation parameters.
66
85. A system as described in claim 83 and further comprising a user input to which said an automatic ovulation transformation comparator is responsive.
86. A system as described in claim 83 wherein said automatic ovulation transformation comparator is responsive to a plurality of ovulation events.
87. A system as described in claim 83 wherein said ultradian rhythm sensor is capable of capturing ultradian rhythms chosen from ultradian rhythms of core body temperature measurements, ultradian rhythms of distal body temperature measurements, ultradian rhythms of heart rate measurements, ultradian rhythms of heart rate variability measurements, and any combination thereof.
88. A system as described in claim 83 wherein said ovulation event is chosen from a preovulatory change in reproductive hormones and a luteinizing hormone surge.
89. A system as described in claim 83 wherein said ovulation event comprises a release of an egg from an ovary.
90. A system as described in claim 83 wherein said periodic capture ultradian rhythm sensor comprises an automatic periodic capture ultradian rhythm sensor.
91. A system as described in claim 90 wherein said automatic periodic capture ultradian rhythm sensor comprises a wearable sensing system.
92. A system as described in claim 91 wherein said wearable sensing system is configured to measure user data chosen from heart rate, distal body temperature, and heart rate variability.
93. A system as described in claim 91 wherein said wearable sensing system comprises a ring worn on said user’s finger.
94. A system as described in claim 91 wherein said wearable sensing system is chosen from a sensor worn on a wrist, a wrist band, a vaginal sensor, an internal body sensor, and a sensor worn on a finger.
95. A system as described in claim 83 wherein said computer data input from said periodic capture ultradian rhythm sensor comprises time series information of a user’s ultradian rhythms.
96. A system as described in claim 83 wherein said starting ovulation transformation parameters comprise quantified ultradian rhythm data.
67
97. A system as described in claim 83 wherein said starting ovulation transformation parameters comprises data analysis of said computer data input, signal processing of said computer data input, variance analysis of said computer data input, spectral analysis of said computer data input, Fourier transform of said computer data input, time series analysis of said computer data input, wavelet transformations of said computer data input, power wavelet transformations of said computer data input, and any combination thereof.
98. A system as described in claim 83 wherein said periodic capture ultradian rhythm sensor comprises a continuous capture ultradian rhythm sensor. 99. A system as described in claim 18, 52, or 85 wherein said user input comprises user’s symptoms, signs of depression, vaginal dryness, libido, memory function, family history, if the user smokes, if the user consumes alcohol, and any combination thereof.
100. A system as described in claim 1, 35, or 69 wherein said user input comprises user’s symptoms, signs of depression, vaginal dryness, libido, memory function, family history, if the user smokes, if the user consumes alcohol, and any combination thereof.
68
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