DETAILED ACTION
Applicant’s arguments, filed on 01/08/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 01/08/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-20 are the current claims hereby under examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 11 and 18 are objected to because of the following informalities:
In claim 11, line 13, “the multiple determination comprise” should read “the multiple determinations comprise”
In claim 18, line 2, “the define number” should read “the defined number”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the claim recites the limitation “an initial period estimate” in line 7. It is unclear if this is limitation is meant to refer to the initial period estimate in lines 3-4, or a different initial period estimate. If it is meant to refer to the initial period estimate from lines 3-4, it needs to refer back to it. If it is meant to refer to a different initial period estimate, it needs to be distinguished from the initial period estimate from lines 3-4. For purposes of examination, it is being interpreted as referring to the initial period estimate from lines 3-4. Claims 2-5 are also rejected due to their dependence on claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under the two-step 101 analysis, the claims fail to satisfy the criteria for subject matter eligibility.
Regarding Step 1, claims 1-20 are all within at least one of the four categories.
Claim 1 and its dependent claims disclose a device (machine).
Claim 6 and its dependent claims disclose a method (process).
Claim 11 and its dependent claims disclose a system (machine).
Regarding step 2A, Prong One, the independent claims 1, 6, and 11 recite an abstract idea. In particular, the claims generally recite the following:
Claim 1 and 11 analysis:
process the initial period estimate and heart rate data into a processed data set;
use the processed data set to make multiple determinations of a likelihood that a given future day is part of a fertility window, wherein the multiple determinations comprise: a first determination of a first likelihood that the given future day is part of the fertile window, the first determination corresponding to a first prediction day and using first data from the processed data set that corresponds to a defined number of days preceding the first prediction day; and a second determination of a second likelihood that the given future day is part of the fertile window, the second determination corresponding to a second prediction day and using second data from the processed data set that corresponds to the defined number of days preceding the second prediction day;
use the processed data set and the multiple determinations from the ovulation estimator to estimate an updated period estimate or updated fertility window;
determining a probability associated with the updated estimate.
These elements recited in claims 1 and 11 are drawn to abstract ideas since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgement, and opinion and using pen and paper.
Processing the initial period estimate and heart rate data into a processed data set is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. In light of the specification, the data is processed into a processed data set by weighting the data according to a set of criteria, for example, if there are enough days of heart rate data to use for evaluation. This process can be done mentally, as a person having ordinary skill in the art could receive the data and determine if enough heart rate data has been acquired and/or weight the data according to a set of criteria. There is nothing to suggest an undue level of complexity in processing the initial period estimate and heart rate data into a processed data set.
Using the processed data set to make multiple determinations of a likelihood that a given future day is part of a fertility window, wherein the multiple determinations comprise: a first determination of a first likelihood that the given future day is part of the fertile window, the first determination corresponding to a first predication day and using first data from the processed data set that corresponds to a defined number of days preceding the first prediction day; and a second determination of a second likelihood that the given future day is part of the fertile window, the second determination corresponding to a second predication day and using second data from the processed data set that corresponds to the defined number of days preceding the second prediction day is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could easily examine heart rate data and an initial period estimate to determine when the fertile window will occur by comparing it to known datasets, evaluating known timeframes for periods and fertile windows, or other analysis techniques used to estimate fertility windows for multiple determinations using the corresponding number of days. This limitation is based on calculations and mathematical principles, which can be performed by hand. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in using the processed data set to make multiple determinations of a likelihood that a given future day is part of a fertility window, wherein the multiple determinations comprise: a first determination of a first likelihood that the given future day is part of the fertile window, the first determination corresponding to a first predication day and using first data from the processed data set that corresponds to a defined number of days preceding the first prediction day; and a second determination of a second likelihood that the given future day is part of the fertile window, the second determination corresponding to a second predication day and using second data from the processed data set that corresponds to the defined number of days preceding the second prediction day.
Using the processed data set and the multiple determinations from the ovulation estimator to estimate an updated period estimate or an updated fertility window is an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could easily look at heart rate data, an initial period estimate, and an estimate of a fertility window and estimate the date of the next period. A person could easily determine an estimate for a period date or fertility window by comparing the received data to known datasets, evaluating known timeframes for periods and fertile windows, or other analysis techniques used to estimate period dates. There is nothing to suggest an undue level of complexity in using the processed data set and the output from the ovulation estimator to estimate an updated period estimate or updated fertility window.
Determining a probability associated with the updated estimate is drawn to an abstract idea since it is a mental process that can practically be performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could easily determine a probability of the updated estimate mentally or with the aid of pen and paper by performing the proper calculations and evaluations used to determine probability. There is nothing to suggest an undue level of complexity in determining a probability associated with the updated estimate.
Claim 6 analysis:
determining whether the heart rate data comprises at least a minimum number of samples of heart rate data;
determining a first ovulation window using the heart rate data and the initial estimate of the fertility window;
estimating a second ovulation window using the additional heart rate data, the estimating the second ovulation window comprising making multiple determinations of a likelihood that a given future day is part of a fertility window, wherein the multiple determinations comprise: a first determination of a first likelihood that the given future day is part of the fertile window, the first determination corresponding to a first predication day and using first data from the processed data set that corresponds to a defined number of days preceding the first prediction day; and a second determination of a second likelihood that the given future day is part of the fertile window, the second determination corresponding to a second predication day and using second data from the processed data set that corresponds to the defined number of days preceding the second prediction day;
determining a probability associated with the second ovulation window using a confidence threshold.
These elements recited in claim 6 are drawn to abstract ideas since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgement, and opinion and using a pen and paper.
Determining whether the heart rate data comprises at least a minimum number of samples of heart rate data is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art can easily look at the gathered heart rate data and determine whether it comprises a minimum number of samples of heart rate data to perform the analysis. There is nothing to suggest an undue level of complexity in determining whether the heart rate data comprises at least a minimum number of samples of heart rate data.
Determining a first ovulation window using the heart rate data and the initial estimate of the fertility window is drawn to an abstract idea since it is a mental process that can practically be performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art can easily determine a first ovulation window using the data by performing the necessary calculations mentally or with the aid of pen and paper. This limitation is based on calculations, mathematical principles, and judgement, which can be performed by hand or mentally. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in determining a first ovulation window using the heart rate data and the initial estimate of the fertility window.
Estimating an ovulation window using the heart rate data and the initial estimate of the fertile window to make multiple determinations of a likelihood that a given future day is part of a fertility window, wherein the multiple determination comprise: a first determination of a first likelihood that the given future day is part of the fertile window, the first determination corresponding to a first predication day and using first data from the processed data set that corresponds to a defined number of days preceding the first prediction day; and a second determination of a second likelihood that the given future day is part of the fertile window, the second determination corresponding to a second predication day and using second data from the processed data set that corresponds to the defined number of days preceding the second prediction day is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could easily examine heart rate data and an initial estimate of the fertile window to determine when the ovulation window will occur by comparing it to known datasets, evaluating known timeframes for ovulation and fertile windows, or other analysis techniques used to estimate ovulation windows for multiple determinations using the corresponding number of days. This limitation is based on calculations and mathematical principles, which can be performed by hand. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in estimating an ovulation window using the heart rate data and the initial estimate of the fertile window to make multiple determinations of a likelihood that a given future day is part of a fertility window, wherein the multiple determination comprise: a first determination of a first likelihood that the given future day is part of the fertile window, the first determination corresponding to a first predication day and using first data from the processed data set that corresponds to a defined number of days preceding the first prediction day; and a second determination of a second likelihood that the given future day is part of the fertile window, the second determination corresponding to a second predication day and using second data from the processed data set that corresponds to the defined number of days preceding the second prediction day.
Determining a probability associated with the second ovulation window is drawn to an abstract idea since it is a mental process that can practically be performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could easily determine a probability of the second ovulation window mentally or with the aid of pen and paper by performing the proper calculations and evaluations used to determine probability. There is nothing to suggest an undue level of complexity in determining a probability associated with the second ovulation window estimate.
Regarding Step 2A, Prong Two, claims 1, 6, and 11 do not recite additional elements that integrate the exception into a practical application. Therefore, the claims are directed to the abstract idea. The additional elements merely:
Recite the words “apply it” or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “a calendar module” (claim 1, line 3 and claim 11, line 4), “a preprocessing module” (claim 1, line 6 and claim 11, line 6), “an ovulation estimator” (claim 1, line 10 and claim 11, line 10), “a period estimator” (claim 1, line 22), “a period update module” (claim 1, line 26), “a fertile window update module” (claim 11, line 22), as these are all merely processors that receive data and process the data to provide the desired output).
Add insignificant extra-solution activity (the pre-solution activity of: generic data-gathering components (e.g., “a wearable device” (claim 1, line 1), “a wearable device comprising a heart rate sensor” (claim 11, line 3), , “a heart rate sensor” (claim 1, line 5), “receive an initial period estimate from the calendar module” (claim 1, line 7 and claim 11, line 7), “receive heart rate data from the heart rate sensor” (claim 1, line 8 and claim 11, line 8), “receive the processed data set from the preprocessing module” (claim 1, line 11 and claim 11, line 11), “receive the processed data set and an output from the ovulation estimator” (claim 1, line 23), “”receiving an initial estimate of a fertile window and an initial estimate of a period” (claim 6, line 3), and “receiving heart rate data from a heart rate sensor of the electronic device” (claim 6, line 4)); and insignificant post-solution activity (e.g., “causing the wearable device to output an initial period estimate” (claim 1, lines 3-4), “cause the wearable device to output the updated period estimate” (claim 1, lines 28-29), “cause the wearable device to output the initial period estimate” (claim 1, lines 31-32), “causing the electronic device to output the first ovulation window” (claim 6, line 10), “cause the electronic device to update the output of the first ovulation window with the second ovulation window” (claim 6, lines 27-28), “cause the wearable device to output an initial fertility window” (claim 11, lines 4-5), “Cause the wearable device to output the updated fertility window” (claim 11, lines 24-25) and “cause the wearable device to continue to output the initial fertility window” (claim 11, lines 31-32))).
As a whole, the additional elements merely serve to gather information to be used by the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Regarding Step 2B, claims 1, 6, and 11 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above.
Claims 1, 6, and 11 do not recite additional elements that amount to significantly more than the judicial exception itself. In particular, “a calendar module” does not qualify as significantly more because this limitation merely describes a calendar used to hold period dates and display them. The limitations “a heart rate sensor” and “a wearable device comprising a heart rate sensor” are also not significantly more as they are known methods for gathering heart rate data. Similarly, the steps of receiving data from various sources do not qualify as significantly more because the limitation merely describes receiving data and does not incorporate any means of gathering the data as part of the claimed invention. Also, the steps involving displaying or outputting values are insignificant extra-solution activity to the judicial exception, as these steps merely displaying the result of the abstract idea using conventional, routine, and well-known elements.
The data-gathering components of “a heart rate sensor” and “a wearable device comprising a heart rate sensor” is nothing more than a conventional sensor used to measure heart rate. Such sensors are evidenced by:
US Patent Application Publication No. 20200359958 (Kaida) discloses physiological information such as heart rate are measured using well-known sensors such as a wearable watch ([0038]).
US Patent Application Publication No. 20210349016 (Plechinger) discloses conventional sensor devices such as those used in sports watches are suitable for measuring various vital parameters such as heart rate ([0003]).
US Patent Application Publication 20200329982 (Hengstmann) discloses that monitoring heart rate is a routine function in smart watches ([0005]).
US Patent Application Publication 20180338721 (Wang) discloses that smart watches that may provide heart rate monitoring are already known (Wang, [0076]).
Further, the elements “a preprocessing module”, “an ovulation estimator”, “a period estimator”, “a fertile window update module”, and “a period update module” are all elements in a processor, therefore they do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Regarding the dependent claims, claims 2-5 depend on claim 1, claims 7-10 depend on claim 6, and claims 12-20 depend on claim 11. The dependent claims merely further define the abstract idea or are additional data output that is well-understood, routine, and previously known to the industry.
For example, the following are dependent claims reciting abstract ideas and can be performed in the human mind, describes insignificant pre-solution or post-solution activity, or simply describes the nature of the data:
(Claim 2): “a fertile window update module configured to: receive an estimate of the fertility window from the ovulation estimator; and in the event the estimate of the fertility window starts in the future, provide the estimate of the fertility window to the calendar” further describes insignificant post-solution activity;
(Claim 3): “wherein the calendar module is further operative to display the estimate of the fertility window and the initial period estimate or the updated period estimate to a user” further describes insignificant post-solution activity;
(Claim 4): “wherein the ovulation estimator is a combination of an LSTM neural network and a deep neural network” is based in mathematical concept that can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The analysis involved with these techniques are based in observing the recorded data and performing calculations to determine the desired results. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 5): “wherein the initial period estimate comprises: an estimate of a period start date; and an estimate of a period end date” further describes insignificant pre-solution activity or simply describes the nature of the data;
(Claim 7): “after estimating the second ovulation window, estimating a period using the heart rate data and the initial estimate of the period; and in response to estimating the period, updating a display of the period that is electronically accessible by a user” is directed to an abstract idea, as it can be performed practically in the human mind, or with the aid of pen and paper. One of ordinary skill in the art could easily estimate a period using heart rate data and an initial estimate of a period through techniques such as comparing to known datasets, evaluating known relations between periods and heart rates, or other analysis techniques used to estimate periods. There is nothing to suggest an undue level of complexity in estimating a period using heart rate data and an initial estimate of a period. Updating a display of the period that is electronically accessible by a user further describes insignificant post-solution activity;
(Claim 8): “wherein the minimum number of samples comprises heart rate data from at least half of the defined number of days” further describes the abstract idea, as it merely provides further clarification as to what the minimum number of samples of the heart rate is that is used to determine if there is enough heart rate data to analyze or simply describes the nature of the data;
(Claim 9): “wherein the heart rate data comprises a basal sedentary waking heart rate” further describes insignificant pre-solution activity or simply describes the nature of the data;
(Claim 10): “wherein the heart rate data further comprises a basal sedentary sleeping heart rate” further describes insignificant pre-solution activity or simply describes the nature of the data;
(Claim 12): “a period estimator operative to: receive the processed data set and an output from the ovulation estimator; and use the processed data set and the output from the ovulation estimator to estimate a period date” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could easily look at heart rate data, an initial period estimate, and an estimate of a fertility window and estimate the date of the next period. A person could easily determine an estimate for a period date by comparing the received data to known datasets, evaluating known timeframes for periods and fertile windows, or other analysis techniques used to estimate period dates. There is nothing to suggest an undue level of complexity in using the processed data set and the output from the ovulation estimator to estimate a period date;
(Claim 13): “a period update module configured to: receive the estimate of the period date from the period estimator; and in the event the estimate of the period date starts in the future and the period has not been previously updated during a present menstrual cycle, provide the estimate of the period date to the calendar” further describes insignificant post-solution activity;
(Claim 14): “wherein the calendar module is further operative to display the estimate of the fertility window and the estimate of the period date to a user” further describes insignificant post-solution activity;
(Claim 15): “wherein the ovulation estimator is a combination of an LSTM neural network and a deep neural network” is based in mathematical concept that can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The analysis involved with these techniques are based in observing the recorded data and performing calculations to determine the desired results. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 16): “wherein the initial period estimate further comprises: an estimate of a period start date; and an estimate of a period end date” further describes insignificant pre-solution activity or simply describes the nature of the data;
(Claim 17): “wherein the preprocessing module is operative to determine whether the heart rate data covers a defined period of time prior to processing the initial period estimate and heart rate data into the processed data set” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art can easily look at the gathered heart rate data and determine whether it falls into the criteria of having a sufficient amount of heart rate data to use for analysis. There is nothing to suggest an undue level of complexity in determining whether the heart rate data covers a sufficient period of time;
(Claim 18): “wherein the defined period of time is at least half of the define number of days” further describes the abstract idea, as it merely provides further clarification as to what the sufficient period of time is that is used to determine if there is enough heart rate data to analyze;
(Claim 19): “wherein the heart rate data comprises a basal sedentary waking heart rate” further describes insignificant pre-solution activity or simply describes the nature of the data;
(Claim 20): “wherein the heart rate data comprises a basal sedentary sleeping heart rate” further describes insignificant pre-solution activity or simply describes the nature of the data.
The dependent claims do not recite significantly more than the abstract ideas. Therefore, claims 1-20 are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lafon (US 10765409) in view of Webster (WO 2020013830) and Frenz (US 20190029875).
Regarding independent claim 1, Lafon teaches a wearable device for estimating portions of a menstrual cycle (Column 3, lines 1-4: “a woman can wear or utilize a device that is able to automatically measure or determine aspects of the health or wellbeing of the woman”), comprising:
a calendar module configured to cause the wearable device to output an initial period estimate (Column 4, lines 29-34: “In some embodiments the app can present a calendar view that can show historical cycle data, such as may correspond to the dates when menstruation occurred in the past. The view can also indicate predicted times or dates for future menstruation based on the prediction values”; Column 9, lines 18-20: “the user information will be used to make an initial prediction”);
a heart rate sensor (Column 3, lines 28-35: “In one embodiment, a woman can wear a smart watch that contains an optical photoplethysmogram (PPG) and an accelerometer. The PPG can obtain volumetric measurements by illuminating the skin, such as by using an emitter on a side of the watch proximate the woman's wrist, and measuring a change in absorption of the light over time. The frequency of these changes can be representative of the heart rate or pulse of the user”);
a preprocessing module (Column 15, line 23-24: “the device includes at least one processor”. As the preprocessing module has the same functionality as a processor, it is analogous to the processor described in Lafon. Similarly, “the at least one processor” of Lafon encompasses all the capabilities of the several processors described.) operative to:
receive an initial period estimate from the calendar module (Column 4, lines 29-34: “In some embodiments the app can present a calendar view that can show historical cycle data, such as may correspond to the dates when menstruation occurred in the past. The view can also indicate predicted times or dates for future menstruation based on the prediction values”; Column 9, lines 18-20: “the user information will be used to make an initial prediction”);
receive heart rate data from the heart rate sensor (Column 3, lines 28-35: “In one embodiment, a woman can wear a smart watch that contains an optical photoplethysmogram (PPG) and an accelerometer. The PPG can obtain volumetric measurements by illuminating the skin, such as by using an emitter on a side of the watch proximate the woman's wrist, and measuring a change in absorption of the light over time. The frequency of these changes can be representative of the heart rate or pulse of the user”); and
process the initial period estimate and heart rate data into a processed data set (Column 11, lines 21-28: “The data can be collected over time and filtered to reduce noise and random variations in the data, which may be due to natural variations as well as outside influences such as changes in exercise, diet, stress, and the like. Other types of processing of the data can be used as well as would be apparent to one of ordinary skill in the art”. The data includes the initial period estimate and the heart rate data, and the filtering is the processing.);
an ovulation estimator (Column 15, line 23-24: “the device includes at least one processor”. As the ovulation estimator has the same functionality as a processor, it is analogous to the processor described in Lafon. Similarly, “the at least one processor” of Lafon encompasses all the capabilities of the several processors described.) operative to:
receive the processed data set from the preprocessing module (as all the processors described are encompassed by the “at least one processor” of Lafon, the processor is already in possession of the processed data set); and
use the processed data set to make determinations of a likelihood that a given future day is part of a fertility window, wherein the determinations comprise: a first determination of a first likelihood that the given future day is part of the fertile window, the first determination corresponding to a first prediction day and using first data from the processed data set that corresponds to a defined number of days preceding the first prediction day (Column 4, lines 32-36: “The view can also indicate predicted times or dates for future menstruation based on the prediction values. Other information can be surfaced or available as well, as may relate to predicted times of ovulation or fertile windows”; Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration. The duration in one embodiment can then be given by: P.sub.n=f(P.sub.n-1P.sub.n-2P.sub.n-3,RHR) where RHR is a vector of the resting heart rate values from previous days. The RHR vector can be thirty days of data, one year of data, etc. as well as all data available for the user.”; Column 13, lines 33-38: “Various algorithms and approaches can be used to analyze the data within the scope of the various embodiments. As mentioned, a first approach can obtain information from a user about her cycle. This can include approximate cycle length, as well as information about the starting and stopping times of menstruation for two or more cycles”; Column 14, lines 41-44: “an application might provide a countdown until an upcoming period, or a calendar view that lists predicted days of ovulation, fertile window, and menstruation”; Column 5, lines 35-37: “Certain techniques can be used to classify days as likely being associated with cycle events”. The first data is the heart rate (RHR) values from previous days, and the defined number of days is the length of time of the RHR vector, which can be thirty days, one year, or all the data available for the user. The likelihood that the given future day is part of the fertile window is the predicted dates for the fertile window, which is shown by whether the day on the calendar shows as being a predicted day of the fertile window. The first prediction day is the day in which the predictions are determined.).
However, Lafon does not teach making multiple determinations, wherein the multiple determinations comprise: a second determination of a second likelihood that the given future day is part of the fertile window, the second determination corresponding to a second prediction day and using second data from the processed data set that corresponds to the defined number of days preceding the second prediction day.
Webster discloses a system for sensing body temperature and predicting ovulation events of a user. Specifically, Webster teaches making multiple determinations including a second determination (Page 41, line 30 – Page 42, line 2: “This automatic user-preference ovulation transformation comparator (171) can act to automatically compare any number of a variety of generated ovulation prediction outputs, namely indications that can be used to determine the likely existence of an ovulation event”. This citation discloses making multiple predictions and comparing the predictions to determine the likelihood of an ovulation event.). Lafon and Webster are analogous arts as they are both related to devices that monitor parameters of a user and predict aspects of the user’s menstrual cycle.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the multiple determinations from Webster into the device from Lafon as it allows the device to determine more predictions, which can be used to create a more accurate and comprehensive prediction of the menstrual cycle events, ensuring the user is more informed on the possible dates of these events.
The Lafon/Webster combination teaches wherein the multiple determinations comprise: a second determination of a second likelihood that the given future day is part of the fertile window (Webster, Page 41, line 30 – Page 42, line 2: “This automatic user-preference ovulation transformation comparator (171) can act to automatically compare any number of a variety of generated ovulation prediction outputs, namely indications that can be used to determine the likely existence of an ovulation event”; Lafon, Column 4, lines 32-36: “The view can also indicate predicted times or dates for future menstruation based on the prediction values. Other information can be surfaced or available as well, as may relate to predicted times of ovulation or fertile windows”), the second determination corresponding to a second prediction day (Lafon, Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration. The duration in one embodiment can then be given by: P.sub.n=f(P.sub.n-1P.sub.n-2P.sub.n-3,RHR) where RHR is a vector of the resting heart rate values from previous days. The RHR vector can be thirty days of data, one year of data, etc. as well as all data available for the user.”. The second prediction day is the day in which the second prediction is determined) and using second data from the processed data set that corresponds to the defined number of days preceding the second prediction day (Lafon, Column 4, lines 32-36: “The view can also indicate predicted times or dates for future menstruation based on the prediction values. Other information can be surfaced or available as well, as may relate to predicted times of ovulation or fertile windows”; Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration. The duration in one embodiment can then be given by: P.sub.n=f(P.sub.n-1P.sub.n-2P.sub.n-3,RHR) where RHR is a vector of the resting heart rate values from previous days. The RHR vector can be thirty days of data, one year of data, etc. as well as all data available for the user.”; Column 13, lines 33-38: “Various algorithms and approaches can be used to analyze the data within the scope of the various embodiments. As mentioned, a first approach can obtain information from a user about her cycle. This can include approximate cycle length, as well as information about the starting and stopping times of menstruation for two or more cycles”; Column 14, lines 41-44: “an application might provide a countdown until an upcoming period, or a calendar view that lists predicted days of ovulation, fertile window, and menstruation”; Column 5, lines 35-37: “Certain techniques can be used to classify days as likely being associated with cycle events”. The second data is the heart rate (RHR) values from previous days, and the defined number of days is the length of time of the RHR vector, which can be thirty days, one year, or all the data available for the user.); and
a period estimator (Lafon, Column 15, line 23-24: “the device includes at least one processor”. As the period estimator has the same functionality as a processor, it is analogous to the processor described in Lafon. Similarly, “the at least one processor” of Lafon encompasses all the capabilities of the several processors described.) operative to:
receive the processed data set and an output from the ovulation estimator (as all the processors described are encompassed by the “at least one processor” of Lafon, the processor is already in possession of the processed data set and an output from the ovulation estimator); and
use the processed data set and the multiple determinations from the ovulation estimator to estimate an updated period estimate (Lafon, Column 8, lines 44-48: “the patterns of RHR variation and menstrual cycle events can be used together to predict 304 (or update predictions for) one or more event dates for a current or upcoming menstrual cycle as discussed herein”; Webster, Page 41, line 30 – Page 42, line 2: “This automatic user-preference ovulation transformation comparator (171) can act to automatically compare any number of a variety of generated ovulation prediction outputs, namely indications that can be used to determine the likely existence of an ovulation event”. This citation discloses making multiple predictions and comparing the predictions to determine the likelihood of an ovulation event. Webster teaches using multiple determinations to predict an event, and Lafon discloses using the data to update the prediction, which is the updated period estimate.); and
a period update module (Lafon, Column 15, line 23-24: “the device includes at least one processor”. As the period update module has the same functionality as a processor, it is analogous to the processor described in Lafon. Similarly, “the at least one processor” of Lafon encompasses all the capabilities of the several processors described.).
However, the Lafon/Webster combination does not teach in response to determining that a probability associated with the updated period estimate satisfies a threshold, cause the wearable device to output the updated period estimate; and in response to determining that the probability associated with the updated period estimate does not satisfy the threshold, cause the wearable device to continue to output the initial period estimate.
Webster teaches in response to determining that a probability associated with the updated period is probable, cause the wearable device to output the updated estimate; and in response to determining that a probability associated with the updated estimate is not as probable, cause the wearable device to continue to output the initial estimate (Page 40, line 12-14: “the user or a default setting could indicate a least false positive indication as the desired criterion. In this type of configuration, the system could be configured so as to be considered as having a least a false positive ovulation transformation comparator”; Claim 299: “said step of automatically determining whether said first transformation computation ovulation prediction output or said second transformation computation ovulation prediction output is likely to provide a more user-preference aligned indication of the likely existence of an ovulation event comprises the step of automatically utilizing whichever computation ovulation prediction output provides the least false positive indications of a likely existence of an ovulation event based upon said step of determining the likely existence of an ovulation event”. This citation teaches only outputting the “least false positive” prediction, which is the most probable prediction).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the output being determined on which result is more probable from Webster into the Lafon/Webster combination as it allows the device to only output the most probable estimate, which ensures that the user is getting the most accurate period estimate.
However, the Lafon/Webster combination does not teach wherein a probability is determined by determining that the probability associated with the updated period estimate does or does not satisfy a threshold.
Frenz discloses a system and method to predict bleeding patterns of women. Specifically, Frenz teaches determining a probability by determining if a probability associated with the estimate does or does not satisfy a threshold ([0116]: “the minimum number of predictor values can be defined such that a prediction can only be made from the minimum number of predictor values having a probability above a defined threshold value”). Lafon, Webster, and Frenz are analogous arts as they are all related to systems that monitor the menstrual cycle of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the determination of the probability from Frenz into the Lafon/Webster combination as the combination is silent on how the probability is determined, and Frenz discloses a suitable process for determining a probability in an analogous device.
Regarding claim 2, the Lafon/Webster/Frenz combination teaches the wearable device of claim 1, further comprising a fertile window update module (Lafon, Column 15, line 23-24: “the device includes at least one processor”. As the fertile window update module has the same functionality as a processor, it is analogous to the processor described in Lafon. Similarly, “the at least one processor” of Lafon encompasses all the capabilities of the several processors described.) configured to:
receive an estimate of the fertility window from the ovulation estimator (as all the processors described are encompassed by the “at least one processor” of Lafon, the processor is already in possession of the estimate of the fertility window from the ovulation estimator); and
in the event the estimate of the fertility window starts in the future, provide the estimate of the fertility window to the calendar (Lafon, Column 14, lines 37-44: “the cycle information can be surfaced in a number of different ways. There can be various options through which a user can navigate, or there can be specific interfaces or displays provided, among other such options. For example, an application might provide a countdown until an upcoming period, or a calendar view that lists predicted days of ovulation, fertile window, and menstruation”).
Regarding claim 3, the Lafon/Webster/Frenz combination teaches the wearable device of claim 2, wherein the calendar module is further operative to display the estimate of the fertility window and the initial period estimate or the updated period estimate to a user (Lafon, Column 14, lines 37-44: “the cycle information can be surfaced in a number of different ways. There can be various options through which a user can navigate, or there can be specific interfaces or displays provided, among other such options. For example, an application might provide a countdown until an upcoming period, or a calendar view that lists predicted days of ovulation, fertile window, and menstruation”).
Regarding claim 4, the Lafon/Webster/Frenz combination teaches the wearable device of claim 1, wherein the ovulation estimator is a combination of an LSTM neural network and a deep neural network (Lafon, Column 5, lines 22-39: “A convolutional neural network can be designed to extract HR metrics during sleep that varies in correlation with the menstrual cycle and improves prediction accuracy. The CNN can be trained on data during different sleep stages and the optimal sleep stage can be determined to predict menstrual cycle events such as menses, ovulation, or fertile window. A long short term memory neural network (LSTM), hidden Markov model, or other time series model can be designed to predict events of the next menstrual cycle based on previous menstrual cycle history, this model can also take into account any of the appropriate variables discussed herein. Multiple LSTM models can be trained to predict different parts of the menstrual cycle in various embodiments. Certain techniques can be used to classify days as likely being associated with cycle events. Such classification of past days (e.g., for detection) and future days (e.g., for prediction) can be useful in a variety of circumstances”).
Regarding claim 5, the Lafon/Webster/Frenz combination teaches the wearable device of claim 1, wherein the initial period estimate comprises: an estimate of a period start date; and an estimate of a period end date (Lafon, Column 2, lines 1-2: “historical cycle information can be obtained for a user that can contain information such as the start and stop dates for menstruation over a number of past cycles”; Column 4, lines 29-34: “In some embodiments the app can present a calendar view that can show historical cycle data, such as may correspond to the dates when menstruation occurred in the past. The view can also indicate predicted times or dates for future menstruation based on the prediction values”; Column 9, lines 18-20: “the user information will be used to make an initial prediction”).
Regarding independent claim 6, Lafon teaches a method performed by an electronic device for providing estimates of a fertile window, comprising:
receiving an initial estimate of a fertile window and an initial estimate of a period (Column 4, lines 29-38: “In some embodiments the app can present a calendar view that can show historical cycle data, such as may correspond to the dates when menstruation occurred in the past. The view can also indicate predicted times or dates for future menstruation based on the prediction values. Other information can be surfaced or available as well, as may relate to predicted times of ovulation or fertile windows, and in some instances even periods during which menstrual cycle related symptoms such as PMS are likely to be encountered.”; Column 9, lines 18-20: “the user information will be used to make an initial prediction”);
receiving heart rate data from a heart rate sensor of the electronic device (Column 3, lines 28-35: “In one embodiment, a woman can wear a smart watch that contains an optical photoplethysmogram (PPG) and an accelerometer. The PPG can obtain volumetric measurements by illuminating the skin, such as by using an emitter on a side of the watch proximate the woman's wrist, and measuring a change in absorption of the light over time. The frequency of these changes can be representative of the heart rate or pulse of the user”);
determining whether the heart rate data comprises at least a defined number of samples of heart rate data (Column 3, lines 59-62: “RHR values can be determined in other ways as well, such as by only using time segments where the user has been still for at least a minimum period of time, such as at least five minutes”; Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration. The duration in one embodiment can then be given by: P.sub.n=f(P.sub.n-1P.sub.n-2P.sub.n-3,RHR) where RHR is a vector of the resting heart rate values from previous days. The RHR vector can be thirty days of data, one year of data, etc. as well as all data available for the user.”; Column 8, lines 19-25: “Concurrently with the analysis and predictions in at least some embodiments, heart rate information such as the resting heart rate (RHR) can be monitored 306 for the respective user. As mentioned, this may include using the tracking device during a sleep period and after a minimum period of inactivity to obtain RHR date for the user using one or more approaches as discussed and suggested herein”. The defined number of samples of heart rate data is thirty days of data, one year of data, or all the data available for the user, and the system determines whether the heart rate data comprises at least the defined number of samples depending on the required amount of samples.);
in the event the heart rate data comprises at least the defined number of samples of heart rate data, determining a first ovulation window using the heart rate data and the initial estimate of the fertile window (Column 4, lines 32-36: “The view can also indicate predicted times or dates for future menstruation based on the prediction values. Other information can be surfaced or available as well, as may relate to predicted times of ovulation or fertile windows”; Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration”):
causing the electronic device to output the first ovulation window (Column 14, lines 41-44: “an application might provide a countdown until an upcoming period, or a calendar view that lists predicted days of ovulation, fertile window, and menstruation”; Column 5, lines 35-37: “Certain techniques can be used to classify days as likely being associated with cycle events”).
However, Lafon does not teach estimating a second ovulation data with additional heart rate data.
Webster teaches making multiple determinations including a second determination (Page 41, line 30 – Page 42, line 2: “This automatic user-preference ovulation transformation comparator (171) can act to automatically compare any number of a variety of generated ovulation prediction outputs, namely indications that can be used to determine the likely existence of an ovulation event”. This citation discloses making multiple predictions and comparing the predictions to determine the likelihood of an ovulation event.).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the multiple determinations from Webster into the method from Lafon as it allows the method to determine more predictions, which can be used to create a more accurate and comprehensive prediction of the menstrual cycle events, ensuring the user is more informed on the possible dates of these events.
The Lafon/Webster combination teaches wherein the multiple determinations comprise:
a first determination of a first likelihood that the given future day is part of the fertile window, the first determination corresponding to a first prediction day and using first data from the processed data set that corresponds to a defined number of days preceding the first prediction day (Lafon, Column 4, lines 32-36: “The view can also indicate predicted times or dates for future menstruation based on the prediction values. Other information can be surfaced or available as well, as may relate to predicted times of ovulation or fertile windows”; Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration. The duration in one embodiment can then be given by: P.sub.n=f(P.sub.n-1P.sub.n-2P.sub.n-3,RHR) where RHR is a vector of the resting heart rate values from previous days. The RHR vector can be thirty days of data, one year of data, etc. as well as all data available for the user.”; Column 13, lines 33-38: “Various algorithms and approaches can be used to analyze the data within the scope of the various embodiments. As mentioned, a first approach can obtain information from a user about her cycle. This can include approximate cycle length, as well as information about the starting and stopping times of menstruation for two or more cycles”; Column 14, lines 41-44: “an application might provide a countdown until an upcoming period, or a calendar view that lists predicted days of ovulation, fertile window, and menstruation”; Column 5, lines 35-37: “Certain techniques can be used to classify days as likely being associated with cycle events”. The first data is the heart rate (RHR) values from previous days, and the defined number of days is the length of time of the RHR vector, which can be thirty days, one year, or all the data available for the user. The likelihood that the given future day is part of the fertile window is the predicted dates for the fertile window, which is shown by whether the day on the calendar shows as being a predicted day of the fertile window. The first prediction day is the day in which the predictions are determined.); and
a second determination of a second likelihood that the given future day is part of the fertile window, the second determination corresponding to a second prediction day (Lafon, Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration. The duration in one embodiment can then be given by: P.sub.n=f(P.sub.n-1P.sub.n-2P.sub.n-3,RHR) where RHR is a vector of the resting heart rate values from previous days. The RHR vector can be thirty days of data, one year of data, etc. as well as all data available for the user.”. The second prediction day is the day in which the second prediction is determined) and using second data from the processed data set that corresponds to the defined number of days preceding the second prediction day (Lafon, Column 4, lines 32-36: “The view can also indicate predicted times or dates for future menstruation based on the prediction values. Other information can be surfaced or available as well, as may relate to predicted times of ovulation or fertile windows”; Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration. The duration in one embodiment can then be given by: P.sub.n=f(P.sub.n-1P.sub.n-2P.sub.n-3,RHR) where RHR is a vector of the resting heart rate values from previous days. The RHR vector can be thirty days of data, one year of data, etc. as well as all data available for the user.”; Column 13, lines 33-38: “Various algorithms and approaches can be used to analyze the data within the scope of the various embodiments. As mentioned, a first approach can obtain information from a user about her cycle. This can include approximate cycle length, as well as information about the starting and stopping times of menstruation for two or more cycles”; Column 14, lines 41-44: “an application might provide a countdown until an upcoming period, or a calendar view that lists predicted days of ovulation, fertile window, and menstruation”; Column 5, lines 35-37: “Certain techniques can be used to classify days as likely being associated with cycle events”. The second data is the heart rate (RHR) values from previous days, and the defined number of days is the length of time of the RHR vector, which can be thirty days, one year, or all the data available for the user.).
However, the Lafon/Webster combination does not teach determining a probability associated with the second ovulation window.
Frenz discloses determining a probability of the estimate with a confidence threshold ([0116]: “the minimum number of predictor values can be defined such that a prediction can only be made from the minimum number of predictor values having a probability above a defined threshold value”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the probability with a confidence threshold form Frenz into the Lafon/Webster combination as it allows the combination to determine the likelihood that the estimate is correct, which can inform the user more clearly about whether the estimate is accurate.
However, the Lafon/Webster/Frenz combination does not teach determining whether to cause the electronic device to update the output of the first ovulation window with the second ovulation window based on comparing the probability to a confidence threshold.
Webster teaches determining whether to cause the electronic device to update the output of the first estimate with the second estimate based on comparing the probability to a confidence threshold (Page 40, line 12-14: “the user or a default setting could indicate a least false positive indication as the desired criterion. In this type of configuration, the system could be configured so as to be considered as having a least a false positive ovulation transformation comparator”; Claim 299: “said step of automatically determining whether said first transformation computation ovulation prediction output or said second transformation computation ovulation prediction output is likely to provide a more user-preference aligned indication of the likely existence of an ovulation event comprises the step of automatically utilizing whichever computation ovulation prediction output provides the least false positive indications of a likely existence of an ovulation event based upon said step of determining the likely existence of an ovulation event”. This citation teaches only outputting the “least false positive” prediction, which is the most probable prediction).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the output being determined on which result is more probable from Webster into the Lafon/Webster combination as it allows the device to only output the most probable estimate, which ensures that the user is getting the most accurate estimate.
Regarding claim 7, the Lafon/Webster/Frenz combination teaches the method of claim 6, further comprising:
after estimating the second ovulation window, estimating a period using the heart rate data and the initial estimate of the period (Lafon, Column 8, lines 44-48: “the patterns of RHR variation and menstrual cycle events can be used together to predict 304 (or update predictions for) one or more event dates for a current or upcoming menstrual cycle as discussed herein”), and
in response to estimating the period, updating a display of the period that is electronically accessible by a user (Lafon, Column 14, lines 37-44: “the cycle information can be surfaced in a number of different ways. There can be various options through which a user can navigate, or there can be specific interfaces or displays provided, among other such options. For example, an application might provide a countdown until an upcoming period, or a calendar view that lists predicted days of ovulation, fertile window, and menstruation”).
Regarding claim 8, the Lafon/Webster/Frenz combination teaches the method of claim 6, wherein the defined number of samples comprises heart rate data from at least half of the defined number of days (Lafon, Column 7, lines 42-46: “the phase of RHR can be correlated with the point in the menstrual cycle. With enough data, the RHR phase can be used to improve predictions as to menstrual cycle events, such as the start of menstruation or ovulation, etc.”; Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration. The duration in one embodiment can then be given by: P.sub.n=f(P.sub.n-1P.sub.n-2P.sub.n-3,RHR) where RHR is a vector of the resting heart rate values from previous days. The RHR vector can be thirty days of data, one year of data, etc. as well as all data available for the user.”. The heart rate data includes thirty days of data, one year of data, or all data available for the user. The defined number of samples is all the data from these days, which is data from at least half of the defined number of days.).
Regarding claim 9, the Lafon/Webster/Frenz combination teaches the method of claim 6, wherein the heart rate data comprises a basal sedentary waking heart rate (Lafon, Column 3, lines 35-42: “Because these measurements can be susceptible to motion effects, it may be preferable in at least some embodiments to attempt to determine the resting heart rate (RHR) of the woman. This may be accomplished at night while the woman is sleeping, for example, although other periods of low activity (or even periods that are activity independent) can be used as well within the scope of the various embodiments”. A basal sedentary waking heart rate is a period of low activity.).
Regarding claim 10, the Lafon/Webster/Frenz combination teaches the method of claim 9, wherein the heart rate data further comprises a basal sedentary sleeping heart rate (Lafon, Column 3, lines 35-42: “Because these measurements can be susceptible to motion effects, it may be preferable in at least some embodiments to attempt to determine the resting heart rate (RHR) of the woman. This may be accomplished at night while the woman is sleeping, for example, although other periods of low activity (or even periods that are activity independent) can be used as well within the scope of the various embodiments”).
Regarding independent claim 11, Lafon teaches a system for estimating portions of a menstrual cycle, comprising:
a wearable device (Column 3, lines 1-4: “a woman can wear or utilize a device that is able to automatically measure or determine aspects of the health or wellbeing of the woman”) comprising heart rate sensor (Column 3, lines 28-35: “In one embodiment, a woman can wear a smart watch that contains an optical photoplethysmogram (PPG) and an accelerometer. The PPG can obtain volumetric measurements by illuminating the skin, such as by using an emitter on a side of the watch proximate the woman's wrist, and measuring a change in absorption of the light over time. The frequency of these changes can be representative of the heart rate or pulse of the user”);
a calendar module (Column 4, lines 29-34: “In some embodiments the app can present a calendar view that can show historical cycle data, such as may correspond to the dates when menstruation occurred in the past. The view can also indicate predicted times or dates for future menstruation based on the prediction values”) configured to cause the wearable device to output an initial fertility window (Column 4, lines 32-36: “The view can also indicate predicted times or dates for future menstruation based on the prediction values. Other information can be surfaced or available as well, as may relate to predicted times of ovulation or fertile windows”);
a preprocessing module (Column 15, line 23-24: “the device includes at least one processor”. As the preprocessing module has the same functionality as a processor, it is analogous to the processor described in Lafon. Similarly, “the at least one processor” of Lafon encompasses all the capabilities of the several processors described.) operative to:
receive an initial period estimate from the calendar module (Column 4, lines 29-34: “In some embodiments the app can present a calendar view that can show historical cycle data, such as may correspond to the dates when menstruation occurred in the past. The view can also indicate predicted times or dates for future menstruation based on the prediction values”; Column 9, lines 18-20: “the user information will be used to make an initial prediction”);
receive heart rate data from the heart rate sensor (Column 3, lines 28-35: “In one embodiment, a woman can wear a smart watch that contains an optical photoplethysmogram (PPG) and an accelerometer. The PPG can obtain volumetric measurements by illuminating the skin, such as by using an emitter on a side of the watch proximate the woman's wrist, and measuring a change in absorption of the light over time. The frequency of these changes can be representative of the heart rate or pulse of the user”) ; and
process the initial period estimate and heart rate data into a processed data set (Column 11, lines 21-28: “The data can be collected over time and filtered to reduce noise and random variations in the data, which may be due to natural variations as well as outside influences such as changes in exercise, diet, stress, and the like. Other types of processing of the data can be used as well as would be apparent to one of ordinary skill in the art”. The data includes the initial period estimate and the heart rate data, and the filtering is the processing.);
an ovulation estimator (Column 15, line 23-24: “the device includes at least one processor”. As the ovulation estimator has the same functionality as a processor, it is analogous to the processor described in Lafon. Similarly, “the at least one processor” of Lafon encompasses all the capabilities of the several processors described.) operative to:
receive the processed data set from the preprocessing module (as all the processors described are encompassed by the “at least one processor” of Lafon, the processor is already in possession of the processed data set); and
use the processed data set to make determinations of a likelihood that a given future day is part of a fertility window, wherein the determination comprise: a first determination of a first likelihood that the given future day is part of the fertile window, the first determination corresponding to a first prediction day and using first data from the processed data set that corresponds to a defined number of days preceding the first prediction day (Column 4, lines 32-36: “The view can also indicate predicted times or dates for future menstruation based on the prediction values. Other information can be surfaced or available as well, as may relate to predicted times of ovulation or fertile windows”; Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration. The duration in one embodiment can then be given by: P.sub.n=f(P.sub.n-1P.sub.n-2P.sub.n-3,RHR) where RHR is a vector of the resting heart rate values from previous days. The RHR vector can be thirty days of data, one year of data, etc. as well as all data available for the user.”; Column 13, lines 33-38: “Various algorithms and approaches can be used to analyze the data within the scope of the various embodiments. As mentioned, a first approach can obtain information from a user about her cycle. This can include approximate cycle length, as well as information about the starting and stopping times of menstruation for two or more cycles”; Column 14, lines 41-44: “an application might provide a countdown until an upcoming period, or a calendar view that lists predicted days of ovulation, fertile window, and menstruation”; Column 5, lines 35-37: “Certain techniques can be used to classify days as likely being associated with cycle events”. The first data is the heart rate (RHR) values from previous days, and the defined number of days is the length of time of the RHR vector, which can be thirty days, one year, or all the data available for the user. The likelihood that the given future day is part of the fertile window is the predicted dates for the fertile window, which is shown by whether the day on the calendar shows as being a predicted day of the fertile window. The first prediction day is the day in which the predictions are determined.).
However, Lafon does not teach making multiple determinations, wherein the multiple determinations comprise: a second determination of a second likelihood that the given future day is part of the fertile window, the second determination corresponding to a second prediction day and using second data from the processed data set that corresponds to the defined number of days preceding the second prediction day.
Webster discloses a system for sensing body temperature and predicting ovulation events of a user. Specifically, Webster teaches making multiple determinations including a second determination (Page 41, line 30 – Page 42, line 2: “This automatic user-preference ovulation transformation comparator (171) can act to automatically compare any number of a variety of generated ovulation prediction outputs, namely indications that can be used to determine the likely existence of an ovulation event”. This citation discloses making multiple predictions and comparing the predictions to determine the likelihood of an ovulation event.). Lafon and Webster are analogous arts as they are both related to devices that monitor parameters of a user and predict aspects of the user’s menstrual cycle.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the multiple determinations from Webster into the device from Lafon as it allows the device to determine more predictions, which can be used to create a more accurate and comprehensive prediction of the menstrual cycle events, ensuring the user is more informed on the possible dates of these events.
The Lafon/Webster combination teaches wherein the multiple determinations comprise: a second determination of a second likelihood that the given future day is part of the fertile window (Webster, Page 41, line 30 – Page 42, line 2: “This automatic user-preference ovulation transformation comparator (171) can act to automatically compare any number of a variety of generated ovulation prediction outputs, namely indications that can be used to determine the likely existence of an ovulation event”; Lafon, Column 4, lines 32-36: “The view can also indicate predicted times or dates for future menstruation based on the prediction values. Other information can be surfaced or available as well, as may relate to predicted times of ovulation or fertile windows”), the second determination corresponding to a second prediction day (Lafon, Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration. The duration in one embodiment can then be given by: P.sub.n=f(P.sub.n-1P.sub.n-2P.sub.n-3,RHR) where RHR is a vector of the resting heart rate values from previous days. The RHR vector can be thirty days of data, one year of data, etc. as well as all data available for the user.”. The second prediction day is the day in which the second prediction is determined) and using second data from the processed data set that corresponds to the defined number of days preceding the second prediction day (Lafon, Column 4, lines 32-36: “The view can also indicate predicted times or dates for future menstruation based on the prediction values. Other information can be surfaced or available as well, as may relate to predicted times of ovulation or fertile windows”; Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration. The duration in one embodiment can then be given by: P.sub.n=f(P.sub.n-1P.sub.n-2P.sub.n-3,RHR) where RHR is a vector of the resting heart rate values from previous days. The RHR vector can be thirty days of data, one year of data, etc. as well as all data available for the user.”; Column 13, lines 33-38: “Various algorithms and approaches can be used to analyze the data within the scope of the various embodiments. As mentioned, a first approach can obtain information from a user about her cycle. This can include approximate cycle length, as well as information about the starting and stopping times of menstruation for two or more cycles”; Column 14, lines 41-44: “an application might provide a countdown until an upcoming period, or a calendar view that lists predicted days of ovulation, fertile window, and menstruation”; Column 5, lines 35-37: “Certain techniques can be used to classify days as likely being associated with cycle events”. The second data is the heart rate (RHR) values from previous days, and the defined number of days is the length of time of the RHR vector, which can be thirty days, one year, or all the data available for the user.);
a fertile window update module (Lafon, Column 15, line 23-24: “the device includes at least one processor”. As the fertile window update module has the same functionality as a processor, it is analogous to the processor described in Lafon. Similarly, “the at least one processor” of Lafon encompasses all the capabilities of the several processors described.) configured to:
receive the processed data set and an output from the ovulation estimator as all the processors described are encompassed by the “at least one processor” of Lafon, the processor is already in possession of the processed data set and an output from the ovulation estimator); and
use the processed data set and the multiple determinations from the ovulation estimator to estimate an updated fertility window (Lafon, Column 8, lines 44-48: “the patterns of RHR variation and menstrual cycle events can be used together to predict 304 (or update predictions for) one or more event dates for a current or upcoming menstrual cycle as discussed herein”; Webster, Page 41, line 30 – Page 42, line 2: “This automatic user-preference ovulation transformation comparator (171) can act to automatically compare any number of a variety of generated ovulation prediction outputs, namely indications that can be used to determine the likely existence of an ovulation event”. This citation discloses making multiple predictions and comparing the predictions to determine the likelihood of an ovulation event. Webster teaches using multiple determinations to predict an event, and Lafon discloses using the data to update the prediction, which is the updated fertility window estimate.): and
a fertility update module (Lafon, Column 15, line 23-24: “the device includes at least one processor”. As the fertility update module has the same functionality as a processor, it is analogous to the processor described in Lafon. Similarly, “the at least one processor” of Lafon encompasses all the capabilities of the several processors described.).
However, the Lafon/Webster combination does not teach in response to determining that a probability associated with the updated fertility window satisfies a threshold, cause the wearable device to output the updated fertility window: and in response to determining that the probability associated with the updated fertility window does not satisfy the threshold, cause the wearable device to continue to output the initial fertility window.
Webster teaches in response to determining that a probability associated with the updated estimate is probable, cause the wearable device to output the updated estimate; and in response to determining that a probability associated with the updated estimate is not as probable, cause the wearable device to continue to output the initial estimate (Page 40, line 12-14: “the user or a default setting could indicate a least false positive indication as the desired criterion. In this type of configuration, the system could be configured so as to be considered as having a least a false positive ovulation transformation comparator”; Claim 299: “said step of automatically determining whether said first transformation computation ovulation prediction output or said second transformation computation ovulation prediction output is likely to provide a more user-preference aligned indication of the likely existence of an ovulation event comprises the step of automatically utilizing whichever computation ovulation prediction output provides the least false positive indications of a likely existence of an ovulation event based upon said step of determining the likely existence of an ovulation event”. This citation teaches only outputting the “least false positive” prediction, which is the most probable prediction).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the output being determined on which result is more probable from Webster into the Lafon/Webster combination as it allows the device to only output the most probable estimate, which ensures that the user is getting the most accurate fertility window estimate.
However, the Lafon/Webster combination does not teach wherein a probability is determined by determining that the probability associated with the updated fertility window estimate does or does not satisfy a threshold.
Frenz discloses a system and method to predict bleeding patterns of women. Specifically, Frenz teaches determining a probability by determining if a probability associated with the estimate does or does not satisfy a threshold ([0116]: “the minimum number of predictor values can be defined such that a prediction can only be made from the minimum number of predictor values having a probability above a defined threshold value”). Lafon, Webster, and Frenz are analogous arts as they are all related to systems that monitor the menstrual cycle of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the determination of the probability from Frenz into the Lafon/Webster combination as the combination is silent on how the probability is determined, and Frenz discloses a suitable process for determining a probability in an analogous device.
Regarding claim 12, the Lafon/Webster/Frenz combination teaches the system of claim 11, further comprising:
a period estimator (Lafon, Column 15, line 23-24: “the device includes at least one processor”. As the period estimator has the same functionality as a processor, it is analogous to the processor described in Lafon. Similarly, “the at least one processor” of Lafon encompasses all the capabilities of the several processors described.) operative to:
receive the processed data set and an output from the ovulation estimator (as all the processors described are encompassed by the “at least one processor” of Lafon, the processor is already in possession of the processed data set and an output from the ovulation estimator); and
use the processed data set and the output from the ovulation estimator to estimate a period date (Lafon, Column 8, lines 44-48: “the patterns of RHR variation and menstrual cycle events can be used together to predict 304 (or update predictions for) one or more event dates for a current or upcoming menstrual cycle as discussed herein”).
Regarding claim 13, the Lafon/Webster/Frenz combination teaches the system of claim 12, further comprising:
a period update module (Lafon, Column 15, line 23-24: “the device includes at least one processor”. As the period update module has the same functionality as a processor, it is analogous to the processor described in Lafon. Similarly, “the at least one processor” of Lafon encompasses all the capabilities of the several processors described.) configured to:
receive the estimate of the period date from the period estimator (as all the processors described are encompassed by the “at least one processor” of Lafon, the processor is already in possession of the estimate of the period from the period estimator); and
in the event the estimate of the period date starts in the future and the period has not been previously updated during a present menstrual cycle, provide the estimate of the period date to the calendar (Lafon, Column 14, lines 37-44: “the cycle information can be surfaced in a number of different ways. There can be various options through which a user can navigate, or there can be specific interfaces or displays provided, among other such options. For example, an application might provide a countdown until an upcoming period, or a calendar view that lists predicted days of ovulation, fertile window, and menstruation”).
Regarding claim 14, the Lafon/Webster/Frenz combination teaches the system of claim 13, wherein the calendar module is further operative to display the estimate of the fertility window and the estimate of the period date to a user (Lafon, Column 14, lines 37-44: “the cycle information can be surfaced in a number of different ways. There can be various options through which a user can navigate, or there can be specific interfaces or displays provided, among other such options. For example, an application might provide a countdown until an upcoming period, or a calendar view that lists predicted days of ovulation, fertile window, and menstruation”).
Regarding claim 15, the Lafon/Webster/Frenz combination teaches the system of claim 11, wherein the ovulation estimator is a combination of an LSTM neural network and a deep neural network (Lafon, Column 5, lines 22-39: “A convolutional neural network can be designed to extract HR metrics during sleep that varies in correlation with the menstrual cycle and improves prediction accuracy. The CNN can be trained on data during different sleep stages and the optimal sleep stage can be determined to predict menstrual cycle events such as menses, ovulation, or fertile window. A long short term memory neural network (LSTM), hidden Markov model, or other time series model can be designed to predict events of the next menstrual cycle based on previous menstrual cycle history, this model can also take into account any of the appropriate variables discussed herein. Multiple LSTM models can be trained to predict different parts of the menstrual cycle in various embodiments. Certain techniques can be used to classify days as likely being associated with cycle events. Such classification of past days (e.g., for detection) and future days (e.g., for prediction) can be useful in a variety of circumstances”).
Regarding claim 16, the Lafon/Webster/Frenz combination teaches the system of claim 11, wherein the initial period estimate further comprises: an estimate of a period start date; and an estimate of a period end date (Lafon, Column 2, lines 1-2: “historical cycle information can be obtained for a user that can contain information such as the start and stop dates for menstruation over a number of past cycles”; Column 4, lines 29-34: “In some embodiments the app can present a calendar view that can show historical cycle data, such as may correspond to the dates when menstruation occurred in the past. The view can also indicate predicted times or dates for future menstruation based on the prediction values”; Column 9, lines 18-20: “the user information will be used to make an initial prediction”).
Regarding claim 17, the Lafon/Webster/Frenz combination teaches the system of claim 11, wherein the preprocessing module is operative to determine whether the heart rate data covers a defined period of time prior to processing the initial period estimate and heart rate data into the processed data set (Lafon, Column 3, lines 59-62: “RHR values can be determined in other ways as well, such as by only using time segments where the user has been still for at least a minimum period of time, such as at least five minutes”).
Regarding claim 18, the Lafon/Webster/Frenz combination teaches the system of claim 17, wherein the defined period of time is at least half of the define number of days (Lafon, Column 7, lines 42-46: “the phase of RHR can be correlated with the point in the menstrual cycle. With enough data, the RHR phase can be used to improve predictions as to menstrual cycle events, such as the start of menstruation or ovulation, etc.”; Column 4, line 58 – Column 5, line 4: “Alternative prediction algorithms can take as input a set of previous start dates of menstruation (t1, t2, t3, etc.), as well as the resulting calculated menstrual cycle lengths ((t2-t1), (t3-t2), . . . ). A multi-regression equation can then be used which takes as input the previous cycle lengths plus the RHR from a recent section of time, in order to predict the most likely next period duration. The duration in one embodiment can then be given by: P.sub.n=f(P.sub.n-1P.sub.n-2P.sub.n-3,RHR) where RHR is a vector of the resting heart rate values from previous days. The RHR vector can be thirty days of data, one year of data, etc. as well as all data available for the user.”. The heart rate data includes thirty days of data, one year of data, or all data available for the user. The defined number of samples is all the data from these days, which is data from at least half of the defined number of days.).
Regarding claim 19, the Lafon/Webster/Frenz combination teaches the system of claim 11, wherein the heart rate data comprises a basal sedentary waking heart rate (Lafon, Column 3, lines 35-42: “Because these measurements can be susceptible to motion effects, it may be preferable in at least some embodiments to attempt to determine the resting heart rate (RHR) of the woman. This may be accomplished at night while the woman is sleeping, for example, although other periods of low activity (or even periods that are activity independent) can be used as well within the scope of the various embodiments”. A basal sedentary waking heart rate is a period of low activity.).
Regarding claim 20, the Lafon/Webster/Frenz combination teaches the system of claim 19, wherein the heart rate data further comprises a basal sedentary sleeping heart rate (Lafon, Column 3, lines 35-42: “Because these measurements can be susceptible to motion effects, it may be preferable in at least some embodiments to attempt to determine the resting heart rate (RHR) of the woman. This may be accomplished at night while the woman is sleeping, for example, although other periods of low activity (or even periods that are activity independent) can be used as well within the scope of the various embodiments”).
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant has amended the claims to overcome most of the claim objections and 112(b) rejections. However the claim objection of claim 11 is reiterated, as the issue has not been resolved by amendment or argument. Additionally, the amendments have introduced new claim objections and 112(b) rejections, as described above.
Applicant’s arguments with respect to the 101 rejection have been fully considered but they are not found persuasive. Applicant argues that using subsequent data to increase the accuracy of the estimates and utilizing probabilities makes the claimed invention into a practical application. This argument is not persuasive, as these steps still encompass an abstract idea, as the collection and analysis of data recited above constitutes a mental process. This fact is not overcome by the addition of further analysis and data collection as that amounts to repeating steps of analysis and outputting results based on a confidence score and further does not overcome Step 2A Prong 2.
Applicant states without evidence that "the claim as a whole recites additional elements which amount to significantly more than the judicial exception" and it is unclear which additional elements are being referred to as all steps being performed can be accomplished with established equipment and methods and the device as claimed fails to distinguish itself from such. The wearable device is also a generic data gathering tool, which is evidenced in the 101 rejection above.
Applicant's arguments with regards to the multiple determinations and determining whether to output an updated estimate based on a probability associated with the updated estimate have been fully considered but they are not persuasive. Applicant argues that Lafon does not teach multiple determinations, however Lafon is not relied upon to teach this limitation, therefore this argument is not persuasive. Webster is relied upon to teach this limitation (Page 41, line 30 – Page 42, line 2: “This automatic user-preference ovulation transformation comparator (171) can act to automatically compare any number of a variety of generated ovulation prediction outputs, namely indications that can be used to determine the likely existence of an ovulation event”. This citation discloses making multiple predictions and comparing the predictions to determine the likelihood of an ovulation event.), as stated in the 103 rejection above.
Additionally, applicant states that neither Lafon or Webster teaches determining whether to output an updated estimate based on a probability associated with the updated estimate, however as stated in the 103 rejection above, Webster teaches this limitation (Page 40, line 12-14: “the user or a default setting could indicate a least false positive indication as the desired criterion. In this type of configuration, the system could be configured so as to be considered as having a least a false positive ovulation transformation comparator”; Claim 299: “said step of automatically determining whether said first transformation computation ovulation prediction output or said second transformation computation ovulation prediction output is likely to provide a more user-preference aligned indication of the likely existence of an ovulation event comprises the step of automatically utilizing whichever computation ovulation prediction output provides the least false positive indications of a likely existence of an ovulation event based upon said step of determining the likely existence of an ovulation event”. This citation teaches only outputting the “least false positive” prediction (i.e, the updated estimate), which is the most probable prediction (i.e., meaning that it is based on a probability associated with the updated estimate)). Therefore this argument is also unpersuasive.
Applicant’s arguments with respect to the probability having a threshold have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791