Detailed Notice
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 .
Status of Claims
Claims 1- 21 are currently pending.
Claims 1, 8, and 9 are amended.
Claims 10-21 are new.
Claims 1-21 are rejected.
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 1-21 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.
Step 1:
In the instant case, claims 1-7 are directed toward a computer system (i.e., machine), claims 8, 10-15 are directed toward a non-transitory computer-readable storage medium (i.e., manufacture), and claims 9 and 16-21 are directed toward a method (i.e., process). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 1, 8, and 9 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “A computer system configured to communicate with a display generation component and one or more input devices, comprising: one or more processors; and memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for: receiving, via the one or more input devices, a first set of data corresponding to one or more past occurrences of a first type of recurring health-related event of a first user; receiving, via the one or more input devices, a first user input corresponding to a request to display a first user interface; in response to receiving the first user input, displaying, via the display generation component, the first user interface, wherein displaying the first user interface includes displaying a first indication that corresponds to a first predicted date for a future, predicted occurrence of the first type of recurring health-related event, wherein: in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the first predicted date has a first prediction confidence level, the first indication is displayed, via the display generation component, with has a first visual appearance; and in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the first predicted date has a second prediction confidence level, different from the first prediction confidence level, the first indication is displayed, via the display generation component, with has a second visual appearance that is different from the first visual appearance; receiving, via the one or more input devices, a second user input corresponding to a user request to record a second set of data, wherein the second set of data corresponds to a current or past date; and in response to receiving the second user input: in accordance with a determination that the second set of data includes an indication that a second type of health event has occurred, ceasing to display the first indication”.
The limitations of receiving, a first set of data corresponding to one or more past occurrences of a first type of recurring health-related event of a first user; receiving, a first user input corresponding to a request to display; in response to receiving the first user input, wherein displaying the first user interface includes displaying a first indication that corresponds to a first predicted date for a future, predicted occurrence of the first type of recurring health-related event, wherein: in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the first predicted date has a first prediction confidence level, the first indication is displayed, with has a first visual appearance; and in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the first predicted date has a second prediction confidence level, different from the first prediction confidence level, the first indication is displayed, with has a second visual appearance that is different from the first visual appearance; receiving, a second user input corresponding to a user request to record a second set of data, wherein the second set of data corresponds to a current or past date; and in response to receiving the second user input: in accordance with a determination that the second set of data includes an indication that a second type of health event has occurred, ceasing to display the first indication, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of receiving, displaying, and creasing displaying, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Further, the abstract idea of claims 8 and 9 are identical as the abstract idea of claim 1. This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of a certain method of organizing human activity because it recites managing personal behavior or relationships or interactions between people.
Dependent claims 2-7 and 10-21 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 8, and 9. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A—Prong 2:
Claims 1-21 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “processors”, “memory”, “display generation component”, “user interface”, “non-transitory computer-readable medium”, “computer system”, and “input devices”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 8 and [0047], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “one or more processors; and memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for”, “non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors of a computer system that is configured to communicate with a display generation component and one or more input devices, the one or more programs including instructions for”, “communicate with a display generation component and one or more input device”, “via the one or more input devices”, “via the one or more input devices”, “a first user interface”, “displaying, via the display generation component, the first user interface”, “via the display generation component”, “via the display generation component”, “via the one or more input devices”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “receiving, via the one or more input devices, a first set of data corresponding to one or more past occurrences of a first type of recurring health-related event of a first user; receiving, via the one or more input devices, a first user input corresponding to a request to display a first user interface” and “receiving, via the one or more input devices, a second user input corresponding to a user request to record a second set of data, wherein the second set of data corresponds to a current or past date”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Additionally, dependent claims 2-7 and 10-21 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 1, 8, and 9, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent claims 2-7 and 10-21 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 8, and 9, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving, via the one or more input devices, a first set of data corresponding to one or more past occurrences of a first type of recurring health-related event of a first user; receiving, via the one or more input devices, a first user input corresponding to a request to display a first user interface” and “receiving, via the one or more input devices, a second user input corresponding to a user request to record a second set of data, wherein the second set of data corresponds to a current or past date), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-21 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 4-9, 12-15, 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Lafon et al. (US 20210145415 A1), in view of Crowley et al. (US 20200381099 A1), hereinafter Crowley.
Regarding claim 1 Lafon teaches a computer system configured to communicate with a display generation component and one or more input devices, comprising: one or more processors (Lafon, [0064]: “the device can include many types of memory, data storage, or computer-readable media, such as data storage for program instructions for execution by a processor”); and memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for (Lafon, [0064]: “the device can include many types of memory, data storage, or computer-readable media, such as data storage for program instructions for execution by a processor” and [0074]): receiving, via the one or more input device, a first set of data corresponding to one or more past occurrences of a first type of recurring health-related event of a first user (Lafon, [0021]: “The device can preprocess the data or send raw observations to the database server. 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”, [0041], and [0046]: “Such menstrual cycle event determination can be associated with events in the past and present (e.g., detection) and/or events in the future (e.g., prediction). Thus, it should be understood that the discussion of techniques used to predict events are equally applicable to detecting past or present events as appropriate. “Determining” can be inclusive of detecting past/present events as well as predicting future events”); receiving, via the one or more input device, a first user input corresponding to a request to display a first user interface (Lafon, [0017]: “In this example, the woman can wear the smart watch 104 on her arm, and can view health information 106 on a display screen of the watch. In many embodiments the display will be a touch sensitive display that will also allow the woman to input or annotate information about her cycle as discussed elsewhere herein”, [0070]: “A user can then select any of these cycles to obtain the second interface page 850 which displays information for that cycle similar to that described with respect to FIG. 7. Such an interface enables a user to quickly view information for cycles that may have been atypical, for example, and see how that impacted various symptoms, such as whether an atypical menstrual cycle was associated with more headaches or cramping”, and [0080]); in response to receiving the first user input, displaying, via the display generation component, the first user interface, wherein displaying the first user interface includes displaying a first indication that corresponds to a first predicted date for a future, predicted occurrence of the first type of recurring health-related event, wherein (Lafon, [0014]: “This information can be used to predict/detect upcoming/previous menstrual cycle events, such as the start and/or stop of ovulation or menstruation. In order to improve the accuracy of those predictions, one or more health metrics can be monitored for the user that can be correlated with the menstrual cycle… This information can then be used to update the predictive model, as well as to update individual event predictions based at least in part upon the current values of those metrics for the woman. Information about the predictions, and updates to the predictions, can be surfaced to the user, which can help with planning around events such as menstruation and ovulation, which can be important for woman trying to conceive or trying to avoid conception, among other such reasons. Such information can also be used to provide “insights” to a user”, [0016]: “Such a system may also provide useful guidance to women in terms of predicting when significant events such as menstruation or ovulation will occur, so they can plan around such events”, [0021]-[0024], and [0038]): in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the first predicted date has a first prediction confidence level, the first indication is displayed, via the display generation component, with a first visual appearance (Lafon, [0059]: “There may also be different confidence levels or other factors that can impact the relative weightings as well. The weight values chosen can also depend on the signal-to-noise ration of some signals. Menstrual cycle event prediction can be associated with a relative percentage of accuracy. For example, a level of confidence for fertile window prediction can be determined. In addition, or alternative to determining a binary value for the fertile window (true or false), such a level of confidence can be useful to a user who is trying to conceive or is avoiding conception as she can determine how likely she is within a fertile window”, [0061]: “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. In some embodiments the symptoms of various users can be determined and the application can begin to predict when those users will suffer cramps, acne, headaches, tender breasts, poor sleep quality or durations, etc. The application might also provide different views depending upon a user's goals, such as a different view if the user is attempting to conceive versus not conceive. In some embodiments the application might also provide recommendations for improving health or achieving the goal, based at least in part upon the monitored health information. Recommendations can also be made to see a doctor in cases where the body data might indicate a potential medical condition”, [0075], and [0079]); in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the first predicted date has a second prediction confidence level, different from the first prediction confidence level, the first indication id displayed, via the display generation component, with a second visual appearance that is different from the first visual appearance (Lafon, [0059]: “There may also be different confidence levels or other factors that can impact the relative weightings as well. The weight values chosen can also depend on the signal-to-noise ration of some signals. Menstrual cycle event prediction can be associated with a relative percentage of accuracy. For example, a level of confidence for fertile window prediction can be determined. In addition, or alternative to determining a binary value for the fertile window (true or false), such a level of confidence can be useful to a user who is trying to conceive or is avoiding conception as she can determine how likely she is within a fertile window”, [0061]: “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. In some embodiments the symptoms of various users can be determined and the application can begin to predict when those users will suffer cramps, acne, headaches, tender breasts, poor sleep quality or durations, etc. The application might also provide different views depending upon a user's goals, such as a different view if the user is attempting to conceive versus not conceive. In some embodiments the application might also provide recommendations for improving health or achieving the goal, based at least in part upon the monitored health information. Recommendations can also be made to see a doctor in cases where the body data might indicate a potential medical condition”, [0075], and [0079]).
Lafon does not teach receiving, via one or more input devices, a second user input corresponding to a user request to record a second set of data, wherein the second set of data corresponds to a current or past date; and in response to receiving the second use input; in accordance with a determination that the second set of data includes an indication that a second type of health event has occurred, ceasing to display the first indication.
However, Crowley teaches receiving, via one or more input devices, a second user input corresponding to a user request to record a second set of data, wherein the second set of data corresponds to a current or past date (Lafon, [0041]: “this historical information can include date and/or time information for at least the approximate beginning and end times of menstruation, ovulation dates (given by hormonal testing), and other menstrual cycle symptoms over a plurality of past cycles”, [0046]-[0047]: “Such menstrual cycle event determination can be associated with events in the past and present (e.g., detection) and/or events in the future (e.g., prediction). Thus, it should be understood that the discussion of techniques used to predict events are equally applicable to detecting past or present events as appropriate. “Determining” can be inclusive of detecting past/present events as well as predicting future events… two or more measurements can be combined to attempt to improve the predictions, whether using user input-based predictions as discussed above or based upon measured or detected body and health data alone. For example, in one embodiment a woman's heart rate information and blood or tissue chemistry can be used to predict timing of events related to the woman's menstrual cycle”, and [0048]); and in response to receiving the second use input (Crowley, [0060]: “There is a need for electronic devices that provide efficient methods and interfaces for managing health information and functions. For example, it is advantageous to provide timely health-related notifications and cease to display unhelpful notifications”[0226]: “Generally, opening a second application while in a first application does not close the first application. When the second application is displayed and the first application ceases to be displayed, the first application becomes a background application”, and [0479]: “while displaying the second notification, the first electronic device receives a set of one or more inputs that includes an input corresponding to selection of the second affordance (e.g., 1317). In some embodiments, in response to receiving the set of one or more inputs, the first electronic device ceases to share health data, associated with the first electronic device, with the second electronic device”); in accordance with a determination that the second set of data includes an indication that a second type of health event has occurred, ceasing to display the first indication (Crowley, [0060]: “There is a need for electronic devices that provide efficient methods and interfaces for managing health information and functions. For example, it is advantageous to provide timely health-related notifications and cease to display unhelpful notifications”[0226]: “Generally, opening a second application while in a first application does not close the first application. When the second application is displayed and the first application ceases to be displayed, the first application becomes a background application”, and [0479]: “while displaying the second notification, the first electronic device receives a set of one or more inputs that includes an input corresponding to selection of the second affordance (e.g., 1317). In some embodiments, in response to receiving the set of one or more inputs, the first electronic device ceases to share health data, associated with the first electronic device, with the second electronic device”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lafon to incorporate the teachings of Crowley and account for computer user interfaces, and more specifically to techniques and user interfaces for managing health information and functions (Crowley, Abstract and [0002]-[0003]).
Regarding claim 4 Lafon further teaches displaying the first user interface includes displaying a third indication that corresponds to a second predicted date, different from the first predicted date, for a second future, prediction occurrence of the first type of recurring health-related event, wherein (Lafon, [0059]-[0062]): in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the second predicted date has a third prediction confidence level, the third indication has a third visual appearance (Lafon, [0059]-[0062]); and in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the second predicted date has a fourth prediction confidence level, different from the third prediction confidence level, the third indication has a fourth visual appearance that is different from the third visual appearance. (Lafon, [0059]-[0062]).
Regarding claim 5 Lafon further teaches the first user interface includes a textual prediction of when the first type of recurring health-related event will occur (Lafon, [0053] ad [0061]).
Regarding claim 6 Lafon further teaches the prediction includes a notification that includes information corresponding to the prediction (Lafon, [0014]: “This information can then be used to update the predictive model, as well as to update individual event predictions based at least in part upon the current values of those metrics for the woman. Information about the predictions, and updates to the predictions, can be surfaced to the user, which can help with planning around events such as menstruation and ovulation, which can be important for woman trying to conceive or trying to avoid conception, among other such reasons”, [0021]: “The data can then be analyzed at the database server, such as by using a prediction and recording algorithm. The output of the algorithm can be fed back to an application executing on the user's phone or tracker, among other such options. The device can preprocess the data or send raw observations to the database server. 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”, [0044]: “If the likely start of menses or ovulation is changed and is within a specified time window, such as within one or two days of the current time, then a notification might be generated for the user indicating the likely upcoming event”, and [0061]: “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 7 Lafon further teaches the first type of recurring health-related event is an occurrence of a menstrual cycle event and/or a fertility event (Lafon, FIG. 8-9, [0044]: “If the likely start of menses or ovulation is changed and is within a specified time window, such as within one or two days of the current time, then a notification might be generated for the user indicating the likely upcoming event”, [0057]: “Further, as changes in the RHR information are determined over the cycle, those predictions can be updated, such as when RHR becomes indicative of a beginning of menstruation or ovulation/fertile window about to occur, etc.”, [0059], and [0061]).
Regarding claim 8 Lafon further teaches a non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors of a computer system that is configured to communicate with a display generation component and one or more input devices, the one or more programs including instructions for (Lafon, [0064]: “the device can include many types of memory, data storage, or computer-readable media, such as data storage for program instructions for execution by a processor”): receiving, via the one or more input device, a first set of data corresponding to one or more past occurrences of a first type of recurring health-related event of a first user (Lafon, [0021]: “The device can preprocess the data or send raw observations to the database server. 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”, [0041], and [0046]: “Such menstrual cycle event determination can be associated with events in the past and present (e.g., detection) and/or events in the future (e.g., prediction). Thus, it should be understood that the discussion of techniques used to predict events are equally applicable to detecting past or present events as appropriate. “Determining” can be inclusive of detecting past/present events as well as predicting future events”); receiving, via the one or more input devices, a first user input corresponding to a request to display a first user interface (Lafon, [0017]: “In this example, the woman can wear the smart watch 104 on her arm, and can view health information 106 on a display screen of the watch. In many embodiments the display will be a touch sensitive display that will also allow the woman to input or annotate information about her cycle as discussed elsewhere herein”, [0070]: “A user can then select any of these cycles to obtain the second interface page 850 which displays information for that cycle similar to that described with respect to FIG. 7. Such an interface enables a user to quickly view information for cycles that may have been atypical, for example, and see how that impacted various symptoms, such as whether an atypical menstrual cycle was associated with more headaches or cramping”, and [0080]); in response to receiving the first user input, displaying, via the display generation component, the first user interface, wherein displaying the first user interface includes displaying a first indication that corresponds to a first predicted date for a future, predicted occurrence of the first type of recurring health-related event (Lafon, [0014]: “This information can be used to predict/detect upcoming/previous menstrual cycle events, such as the start and/or stop of ovulation or menstruation. In order to improve the accuracy of those predictions, one or more health metrics can be monitored for the user that can be correlated with the menstrual cycle… This information can then be used to update the predictive model, as well as to update individual event predictions based at least in part upon the current values of those metrics for the woman. Information about the predictions, and updates to the predictions, can be surfaced to the user, which can help with planning around events such as menstruation and ovulation, which can be important for woman trying to conceive or trying to avoid conception, among other such reasons. Such information can also be used to provide “insights” to a user”, [0016]: “Such a system may also provide useful guidance to women in terms of predicting when significant events such as menstruation or ovulation will occur, so they can plan around such events”, [0021]-[0024], and [0038]), wherein: in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the first predicted date has a first prediction confidence level, the first indication is displayed, via the display generation component, with a first visual appearance (Lafon, [0059]: “There may also be different confidence levels or other factors that can impact the relative weightings as well. The weight values chosen can also depend on the signal-to-noise ration of some signals. Menstrual cycle event prediction can be associated with a relative percentage of accuracy. For example, a level of confidence for fertile window prediction can be determined. In addition, or alternative to determining a binary value for the fertile window (true or false), such a level of confidence can be useful to a user who is trying to conceive or is avoiding conception as she can determine how likely she is within a fertile window”, [0061]: “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. In some embodiments the symptoms of various users can be determined and the application can begin to predict when those users will suffer cramps, acne, headaches, tender breasts, poor sleep quality or durations, etc. The application might also provide different views depending upon a user's goals, such as a different view if the user is attempting to conceive versus not conceive. In some embodiments the application might also provide recommendations for improving health or achieving the goal, based at least in part upon the monitored health information. Recommendations can also be made to see a doctor in cases where the body data might indicate a potential medical condition”, [0075], and [0079]); in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the first predicted date has a second prediction confidence level, different from the first prediction confidence level, the first indication is displayed, via the display generation component, with a second visual appearance that is different from the first visual appearance (Lafon, [0059]: “There may also be different confidence levels or other factors that can impact the relative weightings as well. The weight values chosen can also depend on the signal-to-noise ration of some signals. Menstrual cycle event prediction can be associated with a relative percentage of accuracy. For example, a level of confidence for fertile window prediction can be determined. In addition, or alternative to determining a binary value for the fertile window (true or false), such a level of confidence can be useful to a user who is trying to conceive or is avoiding conception as she can determine how likely she is within a fertile window”, [0061]: “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. In some embodiments the symptoms of various users can be determined and the application can begin to predict when those users will suffer cramps, acne, headaches, tender breasts, poor sleep quality or durations, etc. The application might also provide different views depending upon a user's goals, such as a different view if the user is attempting to conceive versus not conceive. In some embodiments the application might also provide recommendations for improving health or achieving the goal, based at least in part upon the monitored health information. Recommendations can also be made to see a doctor in cases where the body data might indicate a potential medical condition”, [0075], and [0079]).
Lafon does not teach receiving, via one or more input devices, a second user input corresponding to a user request to record a second set of data, wherein the second set of data corresponds to a current or past date; and in response to receiving the second use input; in accordance with a determination that the second set of data includes an indication that a second type of health event has occurred, ceasing to display the first indication.
However, Crowley teaches receiving, via one or more input devices, a second user input corresponding to a user request to record a second set of data, wherein the second set of data corresponds to a current or past date (Lafon, [0041]: “this historical information can include date and/or time information for at least the approximate beginning and end times of menstruation, ovulation dates (given by hormonal testing), and other menstrual cycle symptoms over a plurality of past cycles”, [0046]-[0047]: “Such menstrual cycle event determination can be associated with events in the past and present (e.g., detection) and/or events in the future (e.g., prediction). Thus, it should be understood that the discussion of techniques used to predict events are equally applicable to detecting past or present events as appropriate. “Determining” can be inclusive of detecting past/present events as well as predicting future events… two or more measurements can be combined to attempt to improve the predictions, whether using user input-based predictions as discussed above or based upon measured or detected body and health data alone. For example, in one embodiment a woman's heart rate information and blood or tissue chemistry can be used to predict timing of events related to the woman's menstrual cycle”, and [0048]); and in response to receiving the second use input (Crowley, [0060]: “There is a need for electronic devices that provide efficient methods and interfaces for managing health information and functions. For example, it is advantageous to provide timely health-related notifications and cease to display unhelpful notifications”[0226]: “Generally, opening a second application while in a first application does not close the first application. When the second application is displayed and the first application ceases to be displayed, the first application becomes a background application”, and [0479]: “while displaying the second notification, the first electronic device receives a set of one or more inputs that includes an input corresponding to selection of the second affordance (e.g., 1317). In some embodiments, in response to receiving the set of one or more inputs, the first electronic device ceases to share health data, associated with the first electronic device, with the second electronic device”); in accordance with a determination that the second set of data includes an indication that a second type of health event has occurred, ceasing to display the first indication (Crowley, [0060]: “There is a need for electronic devices that provide efficient methods and interfaces for managing health information and functions. For example, it is advantageous to provide timely health-related notifications and cease to display unhelpful notifications”[0226]: “Generally, opening a second application while in a first application does not close the first application. When the second application is displayed and the first application ceases to be displayed, the first application becomes a background application”, and [0479]: “while displaying the second notification, the first electronic device receives a set of one or more inputs that includes an input corresponding to selection of the second affordance (e.g., 1317). In some embodiments, in response to receiving the set of one or more inputs, the first electronic device ceases to share health data, associated with the first electronic device, with the second electronic device”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lafon to incorporate the teachings of Crowley and account for computer user interfaces, and more specifically to techniques and user interfaces for managing health information and functions (Crowley, Abstract and [0002]-[0003]).
Regarding claim 9 Lafon further teaches a method, comprising: at a computer system that is configured to communicate with a display generation component and one or more input devices (Lafon, [0064]: “the device can include many types of memory, data storage, or computer-readable media, such as data storage for program instructions for execution by a processor”): receiving, via the one or more input devices, a first set of data corresponding to one or more past occurrences of a first type of recurring health-related event of a first user (Lafon, [0021]: “The device can preprocess the data or send raw observations to the database server. 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”, [0041], and [0046]: “Such menstrual cycle event determination can be associated with events in the past and present (e.g., detection) and/or events in the future (e.g., prediction). Thus, it should be understood that the discussion of techniques used to predict events are equally applicable to detecting past or present events as appropriate. “Determining” can be inclusive of detecting past/present events as well as predicting future events”); receiving, via the one or more input devices, a first user input corresponding to a request to display a first user interface (Lafon, [0017]: “In this example, the woman can wear the smart watch 104 on her arm, and can view health information 106 on a display screen of the watch. In many embodiments the display will be a touch sensitive display that will also allow the woman to input or annotate information about her cycle as discussed elsewhere herein”, [0070]: “A user can then select any of these cycles to obtain the second interface page 850 which displays information for that cycle similar to that described with respect to FIG. 7. Such an interface enables a user to quickly view information for cycles that may have been atypical, for example, and see how that impacted various symptoms, such as whether an atypical menstrual cycle was associated with more headaches or cramping”, and [0080]); in response to receiving the first user input, displaying, via the display generation component, the first user interface, wherein displaying the first user interface includes displaying a first indication that corresponds to a first predicted date for a future, predicted occurrence of the first type of recurring health-related event (Lafon, [0014]: “This information can be used to predict/detect upcoming/previous menstrual cycle events, such as the start and/or stop of ovulation or menstruation. In order to improve the accuracy of those predictions, one or more health metrics can be monitored for the user that can be correlated with the menstrual cycle… This information can then be used to update the predictive model, as well as to update individual event predictions based at least in part upon the current values of those metrics for the woman. Information about the predictions, and updates to the predictions, can be surfaced to the user, which can help with planning around events such as menstruation and ovulation, which can be important for woman trying to conceive or trying to avoid conception, among other such reasons. Such information can also be used to provide “insights” to a user”, [0016]: “Such a system may also provide useful guidance to women in terms of predicting when significant events such as menstruation or ovulation will occur, so they can plan around such events”, [0021]-[0024], and [0038]), wherein: in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the first predicted date has a first prediction confidence level, the first indication is displayed, via the display generation component, with a first visual appearance (Lafon, [0059]: “There may also be different confidence levels or other factors that can impact the relative weightings as well. The weight values chosen can also depend on the signal-to-noise ration of some signals. Menstrual cycle event prediction can be associated with a relative percentage of accuracy. For example, a level of confidence for fertile window prediction can be determined. In addition, or alternative to determining a binary value for the fertile window (true or false), such a level of confidence can be useful to a user who is trying to conceive or is avoiding conception as she can determine how likely she is within a fertile window”, [0061]: “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. In some embodiments the symptoms of various users can be determined and the application can begin to predict when those users will suffer cramps, acne, headaches, tender breasts, poor sleep quality or durations, etc. The application might also provide different views depending upon a user's goals, such as a different view if the user is attempting to conceive versus not conceive. In some embodiments the application might also provide recommendations for improving health or achieving the goal, based at least in part upon the monitored health information. Recommendations can also be made to see a doctor in cases where the body data might indicate a potential medical condition”, [0075], and [0079]); in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the first predicted date has a second prediction confidence level, different from the first prediction confidence level, the first indication is displayed, via the display generation component, with a second visual appearance that is different from the first visual appearance (Lafon, [0059]: “There may also be different confidence levels or other factors that can impact the relative weightings as well. The weight values chosen can also depend on the signal-to-noise ration of some signals. Menstrual cycle event prediction can be associated with a relative percentage of accuracy. For example, a level of confidence for fertile window prediction can be determined. In addition, or alternative to determining a binary value for the fertile window (true or false), such a level of confidence can be useful to a user who is trying to conceive or is avoiding conception as she can determine how likely she is within a fertile window”, [0061]: “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. In some embodiments the symptoms of various users can be determined and the application can begin to predict when those users will suffer cramps, acne, headaches, tender breasts, poor sleep quality or durations, etc. The application might also provide different views depending upon a user's goals, such as a different view if the user is attempting to conceive versus not conceive. In some embodiments the application might also provide recommendations for improving health or achieving the goal, based at least in part upon the monitored health information. Recommendations can also be made to see a doctor in cases where the body data might indicate a potential medical condition”, [0075], and [0079]).
Lafon does not teach receiving, via one or more input devices, a second user input corresponding to a user request to record a second set of data, wherein the second set of data corresponds to a current or past date; and in response to receiving the second use input; in accordance with a determination that the second set of data includes an indication that a second type of health event has occurred, ceasing to display the first indication.
However, Crowley teaches receiving, via one or more input devices, a second user input corresponding to a user request to record a second set of data, wherein the second set of data corresponds to a current or past date (Lafon, [0041]: “this historical information can include date and/or time information for at least the approximate beginning and end times of menstruation, ovulation dates (given by hormonal testing), and other menstrual cycle symptoms over a plurality of past cycles”, [0046]-[0047]: “Such menstrual cycle event determination can be associated with events in the past and present (e.g., detection) and/or events in the future (e.g., prediction). Thus, it should be understood that the discussion of techniques used to predict events are equally applicable to detecting past or present events as appropriate. “Determining” can be inclusive of detecting past/present events as well as predicting future events… two or more measurements can be combined to attempt to improve the predictions, whether using user input-based predictions as discussed above or based upon measured or detected body and health data alone. For example, in one embodiment a woman's heart rate information and blood or tissue chemistry can be used to predict timing of events related to the woman's menstrual cycle”, and [0048]); and in response to receiving the second use input (Crowley, [0060]: “There is a need for electronic devices that provide efficient methods and interfaces for managing health information and functions. For example, it is advantageous to provide timely health-related notifications and cease to display unhelpful notifications”[0226]: “Generally, opening a second application while in a first application does not close the first application. When the second application is displayed and the first application ceases to be displayed, the first application becomes a background application”, and [0479]: “while displaying the second notification, the first electronic device receives a set of one or more inputs that includes an input corresponding to selection of the second affordance (e.g., 1317). In some embodiments, in response to receiving the set of one or more inputs, the first electronic device ceases to share health data, associated with the first electronic device, with the second electronic device”); in accordance with a determination that the second set of data includes an indication that a second type of health event has occurred, ceasing to display the first indication (Crowley, [0060]: “There is a need for electronic devices that provide efficient methods and interfaces for managing health information and functions. For example, it is advantageous to provide timely health-related notifications and cease to display unhelpful notifications”[0226]: “Generally, opening a second application while in a first application does not close the first application. When the second application is displayed and the first application ceases to be displayed, the first application becomes a background application”, and [0479]: “while displaying the second notification, the first electronic device receives a set of one or more inputs that includes an input corresponding to selection of the second affordance (e.g., 1317). In some embodiments, in response to receiving the set of one or more inputs, the first electronic device ceases to share health data, associated with the first electronic device, with the second electronic device”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lafon to incorporate the teachings of Crowley and account for computer user interfaces, and more specifically to techniques and user interfaces for managing health information and functions (Crowley, Abstract and [0002]-[0003]).
Regarding claim 12 Lafon teaches displaying the first user interface includes displaying a third indication that corresponds to a second predicted date, different from the first predicted date, for a second future, prediction occurrence of the first type of recurring health-related event, wherein (Lafon, [0059]-[0062]): in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the second predicted date has a third prediction confidence level, the third indication has a third visual appearance (Lafon, [0059]-[0062]); and in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the second predicted date has a fourth prediction confidence level, different from the third prediction confidence level, the third indication has a fourth visual appearance that is different from the third visual appearance (Lafon, [0059]-[0062]).
Regarding claim 13 Lafon further teaches the first user interface includes a textual prediction of when the first type of recurring health-related event will occur (Lafon, [0053] ad [0061]).
Regarding claim 14 Lafon further teaches the prediction includes a notification that includes information corresponding to the prediction (Lafon, [0014]: “This information can then be used to update the predictive model, as well as to update individual event predictions based at least in part upon the current values of those metrics for the woman. Information about the predictions, and updates to the predictions, can be surfaced to the user, which can help with planning around events such as menstruation and ovulation, which can be important for woman trying to conceive or trying to avoid conception, among other such reasons”, [0021]: “The data can then be analyzed at the database server, such as by using a prediction and recording algorithm. The output of the algorithm can be fed back to an application executing on the user's phone or tracker, among other such options. The device can preprocess the data or send raw observations to the database server. 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”, [0044]: “If the likely start of menses or ovulation is changed and is within a specified time window, such as within one or two days of the current time, then a notification might be generated for the user indicating the likely upcoming event”, and [0061]: “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 Lafon further teaches the first type of recurring health-related event is an occurrence of a menstrual cycle event and/or a fertility event (Lafon, [0014]: “This information can then be used to update the predictive model, as well as to update individual event predictions based at least in part upon the current values of those metrics for the woman. Information about the predictions, and updates to the predictions, can be surfaced to the user, which can help with planning around events such as menstruation and ovulation, which can be important for woman trying to conceive or trying to avoid conception, among other such reasons”, [0016]: “Such a system may also provide useful guidance to women in terms of predicting when significant events such as menstruation or ovulation will occur, so they can plan around such events”, and [0024]: “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”).
Regarding claim 18 Lafon further teaches displaying the first user interface includes displaying a third indication that corresponds to a second predicted date, different from the first predicted date, for a second future, prediction occurrence of the first type of recurring health-related event, wherein (Lafon, [0059]-[0062]): in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the second predicted date has a third prediction confidence level, the third indication has a third visual appearance (Lafon, [0059]-[0062]); and in accordance with a determination based on at least the first set of data that a prediction that the first type of recurring health-related event will occur on the second predicted date has a fourth prediction confidence level, different from the third prediction confidence level, the third indication has a fourth visual appearance that is different from the third visual appearance (Lafon, [0059]-[0062]).
Regarding claim 19 Lafon further teaches the first user interface includes a textual prediction of when the first type of recurring health-related event will occur (Lafon, [0053] ad [0061]).
Regarding claim 20 Lafon further teaches the prediction includes a notification that includes information corresponding to the prediction (Lafon, [0014]: “This information can then be used to update the predictive model, as well as to update individual event predictions based at least in part upon the current values of those metrics for the woman. Information about the predictions, and updates to the predictions, can be surfaced to the user, which can help with planning around events such as menstruation and ovulation, which can be important for woman trying to conceive or trying to avoid conception, among other such reasons”, [0021]: “The data can then be analyzed at the database server, such as by using a prediction and recording algorithm. The output of the algorithm can be fed back to an application executing on the user's phone or tracker, among other such options. The device can preprocess the data or send raw observations to the database server. 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”, [0044]: “If the likely start of menses or ovulation is changed and is within a specified time window, such as within one or two days of the current time, then a notification might be generated for the user indicating the likely upcoming event”, and [0061]: “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 21 Lafon further teaches the first type of recurring health-related event is an occurrence of a menstrual cycle event and/or a fertility event (Lafon, FIG. 3, FIG. 4, [0014]: “This information can be used to predict/detect upcoming/previous menstrual cycle events, such as the start and/or stop of ovulation or menstruation. In order to improve the accuracy of those predictions, one or more health metrics can be monitored for the user that can be correlated with the menstrual cycle”, [0016]: “Other factors can be used to make such a determination as well, such as the use of body temperature readings as a guide to a woman's fertility window and ovulation”, and [0022]: “In one embodiment a user interface can automatically populate a calendar with the likely start date of the menses, and the most likely date of ovulation as well as the fertile window”).
Claims 2-3, 10-11, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Lafon et al. (US 20210145415 A1), in view of Crowley et al. (US 20200381099 A1), hereinafter Crowley, and Barton-Sweeney (US 20190307431 A1).
Regarding claim 2 Lafon teaches displaying the first user interface further includes displaying a second indication that corresponds to one of the one or more past occurrences of the first type of recurring health-related event of the first user (Lafon, [0059]: “There may also be different confidence levels or other factors that can impact the relative weightings as well. The weight values chosen can also depend on the signal-to-noise ration of some signals. Menstrual cycle event prediction can be associated with a relative percentage of accuracy. For example, a level of confidence for fertile window prediction can be determined. In addition, or alternative to determining a binary value for the fertile window (true or false), such a level of confidence can be useful to a user who is trying to conceive or is avoiding conception as she can determine how likely she is within a fertile window”).
Lafon and Crowley do not teach wherein the second indication includes a color associated with the first type of recurring health-related event.
However, Barton-Sweeney teaches wherein the second indication includes a color associated with the first type of recurring health-related event (Barton-Sweeney, [0042], [0049], [0053], [0098], and [0115]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lafon and Crowley to incorporate the teachings of Barton-Sweeney and account for a method that is easy and cost effective to use while avoiding complications and side effects (Barton-Sweeney, [0002]-[0011]).
Regarding claim 3 Lafon and Crowley do not teach when the first prediction confidence level has a greater value than the second prediction confidence level, the first visual appearance includes a first color and the second visual appearance includes a second color different from the first color, and wherein the first color is more similar to the color associated with the first type of recurring health-related event than the second color is similar to the color associated with the first type of recurring health-related event; and when the second prediction confidence level has a greater value than the first prediction confidence level, the second visual appearance includes the first color and the first visual appearance includes the second color.
However, Barton-Sweeney teaches when the first prediction confidence level has a greater value than the second prediction confidence level, the first visual appearance includes a first color and the second visual appearance includes a second color different from the first color, and wherein the first color is more similar to the color associated with the first type of recurring health-related event than the second color is similar to the color associated with the first type of recurring health-related event; and when the second prediction confidence level has a greater value than the first prediction confidence level, the second visual appearance includes the first color and the first visual appearance includes the second color (Barton-Sweeney, [0042], [0049], [0053], [0098], and [0115]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lafon and Crowley to incorporate the teachings of Barton-Sweeney and account for a method that is easy and cost effective to use while avoiding complications and side effects (Barton-Sweeney, [0002]-[0011]).
Regarding claim 10 Lafon further teaches displaying the first user interface further includes displaying a second indication that corresponds to one of the one or more past occurrences of the first type of recurring health-related event of the first user (Lafon, [0059]: “There may also be different confidence levels or other factors that can impact the relative weightings as well. The weight values chosen can also depend on the signal-to-noise ration of some signals. Menstrual cycle event prediction can be associated with a relative percentage of accuracy. For example, a level of confidence for fertile window prediction can be determined. In addition, or alternative to determining a binary value for the fertile window (true or false), such a level of confidence can be useful to a user who is trying to conceive or is avoiding conception as she can determine how likely she is within a fertile window” and [0075]: “an intermediate HR estimation can be performed based on PPG signals from two or more light paths. For each of the acquired PPG signals, the PPG device may determine an estimate of the HR in beats-per-minute (BPM) and compute a confidence metric associated with the PPG signal, which is indicative of the signal quality for the particular light path associated with the PPG signal. It may also be possible to compute a confidence metric without an intermediate HR estimation, for example by characterizing characteristics (e.g., statistics) of the PPG signal or filtered versions of the PPG signal. In some embodiments, each confidence metric corresponds to a single PPG signal. In other cases, each confidence metric corresponds to multiple PPG signals. For example, a confidence metric may be computed for each way of combining the PPG signals (e.g., signals A+B, signals A+C, signals B+C, signals A+B+C, etc.), as well as for various combinations of PPG signals (e.g., selecting at least two of signals A, B, and C). In other cases, one confidence metric corresponds to a single PPG signal and another confidence metric corresponds to a combination of multiple PPG signals. The PPG device can select an HR estimate from the multiple HR estimates corresponding to the multiple light paths (e.g., by selecting the HR estimate of the PPG signal having the highest confidence metric). Alternatively, the PPG device may assign different weight values to the multiple HR estimates based on the confidence metric values associated with the individual and/or multiple PPG signals and compute a final HR estimate based on the weight values. The confidence values and/or the weight values may be updated or optimized using unsupervised machine learning. The PPG device may implement hysteresis logic which prevents jumping between light paths in a short time window if the confidence metric values corresponding to the two light paths are within a threshold value. The PPG device may also implement logic configured to bias the selection of HR estimates based on user data, activity data, movement data, or other data accessible by the PPG device. The PPG device may apply a smoothing filter on the HR estimates, for example, to improve accuracy and provide a better user experience”).
Lafon and Crowley do not teach wherein the second indication includes a color associated with the first type of recurring health-related event.
However, Barton-Sweeney teaches wherein the second indication includes a color associated with the first type of recurring health-related event (Barton-Sweeney, [0042], [0049], [0053], [0098], and [0115]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lafon and Crowley to incorporate the teachings of Barton-Sweeney and account for a method that is easy and cost effective to use while avoiding complications and side effects (Barton-Sweeney, [0002]-[0011]).
Regarding claim 11 Lafon and Crowley do not teach when the first prediction confidence level has a greater value than the second prediction confidence level, the first visual appearance includes a first color and the second visual appearance includes a second color different from the first color, and wherein the first color is more similar to the color associated with the first type of recurring health-related event than the second color is similar to the color associated with the first type of recurring health-related event; and when the second prediction confidence level has a greater value than the first prediction confidence level, the second visual appearance includes the first color and the first visual appearance includes the second color.
However, Barton-Sweeney teach when the first prediction confidence level has a greater value than the second prediction confidence level, the first visual appearance includes a first color and the second visual appearance includes a second color different from the first color, and wherein the first color is more similar to the color associated with the first type of recurring health-related event than the second color is similar to the color associated with the first type of recurring health-related event; and when the second prediction confidence level has a greater value than the first prediction confidence level, the second visual appearance includes the first color and the first visual appearance includes the second color (Barton-Sweeney, [0042], [0053], [0098]: “The indication of fertility level to the user may take one or more forms, including but not limited to: changing the color of the indicator 38; displaying a message on the display 42 or emitting an audible sound for example. The change of color may be defined based on level of risk of conception, e.g. red for high risk, green for low risk or yellow when the risk is uncertain as well as additional colors representing cycle events such as ovulation, peak fertile day, and menstruation. In other embodiments, the fertility level may be a numerical value or textual”, and [0115]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lafon and Crowley to incorporate the teachings of Barton-Sweeney and account for a method that is easy and cost effective to use while avoiding complications and side effects (Barton-Sweeney, [0002]-[0011]).
Regarding claim 16 Lafon and Crowley do no teach displaying the first user interface further includes displaying a second indication that corresponds to one of the one or more past occurrences of the first type of recurring health-related event of the first user, and wherein the second indication includes a color associated with the first type of recurring health-related event.
However, Barton-Sweeney teaches displaying the first user interface further includes displaying a second indication that corresponds to one of the one or more past occurrences of the first type of recurring health-related event of the first user, and wherein the second indication includes a color associated with the first type of recurring health-related event (Barton-Sweeney, [0042]: “the indicator 38 provides feedback to the user on the status of the measurements, like a timer for example, and also changes color to indicate the user's fertility status. The actuator 40 functions as a user input and allows the user to interact with the device 30”, [0049]: “the signal may initiate other control methods that adapt the operation of the device 30 such as changing the color or configuration of the indicator 38”, [0053]: “It should be appreciated that these wearable devices may also indicate or display a subset of the data/information, for example, a ring may have an indicator that changes color based on the users fertility level”, and [0098]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lafon and Crowley to incorporate the teachings of Barton-Sweeney and account for a method that is easy and cost effective to use while avoiding complications and side effects (Barton-Sweeney, [0002]-[0011]).
Regarding claim 17 Lafon and Crowley do not teach when the first prediction confidence level has a greater value than the second prediction confidence level, the first visual appearance includes a first color and the second visual appearance includes a second color different from the first color, and wherein the first color is more similar to the color associated with the first type of recurring health-related event than the second color is similar to the color associated with the first type of recurring health-related event; and when the second prediction confidence level has a greater value than the first prediction confidence level, the second visual appearance includes the first color and the first visual appearance includes the second color.
However, Barton-Sweeney teaches when the first prediction confidence level has a greater value than the second prediction confidence level, the first visual appearance includes a first color and the second visual appearance includes a second color different from the first color, and wherein the first color is more similar to the color associated with the first type of recurring health-related event than the second color is similar to the color associated with the first type of recurring health-related event; and when the second prediction confidence level has a greater value than the first prediction confidence level, the second visual appearance includes the first color and the first visual appearance includes the second color (Barton-Sweeney, [0042], [0053], [0098]: “The indication of fertility level to the user may take one or more forms, including but not limited to: changing the color of the indicator 38; displaying a message on the display 42 or emitting an audible sound for example. The change of color may be defined based on level of risk of conception, e.g. red for high risk, green for low risk or yellow when the risk is uncertain as well as additional colors representing cycle events such as ovulation, peak fertile day, and menstruation. In other embodiments, the fertility level may be a numerical value or textual”, and [0115]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lafon and Crowley to incorporate the teachings of Barton-Sweeney and account for a method that is easy and cost effective to use while avoiding complications and side effects (Barton-Sweeney, [0002]-[0011]).
Response to Arguments
Applicant's arguments filed 03/27/2026 have been fully considered but they are not persuasive. Regarding the 35 U.S.C. 101 Rejection, Applicant argues the claims do not recite an abstract idea because the amendments relate to a computer selectively displaying, or ceasing to display, a relevant indication in a different manner based on analyzing data recorded by the user. Examiner respectfully disagrees. The computer is an additional element that is not part of the abstract idea. However, the action of displaying a relevant indication in a different manner based on analyzing data recorded by the user is part of the abstract idea. MPEP 2106.04(a)(2)(II) recites “the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping”. The action of displaying is being applied to the computer. Therefore, the claims still recite an abstract idea.
Applicant further argues any alleged abstract idea is integrated into a practical application because it improves the function of a computer or improves another technology. Applicant argues the claims recite a specific manner of automatically displaying icons to the user based on usage and a specific manner of receiving data. Additionally, the claims enable a more efficient use of the computer by ceasing to display indications in response to the user recording a second health-related event without requiring additional inputs by the user to cease the display of these indications. Examiner respectfully disagrees. As stated above, displaying and ceasing to display, even automatically, icons to the user based on usage and a specific manner of receiving data are part of the abstract idea. An abstract idea cannot integrate itself into a practical application (see MPEP 2106.05(a) states “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. in addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)” and “Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology”). The additional elements are recited at a high level and are being “applied to” the abstract idea (see MPEP 2106.05(f) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)”). Therefore, the claims do not integrate the abstract idea into a practical application, nor amount to significantly more. The 35 U.S.C. 101 Rejection is maintained.
Regarding the 35 U.S.C. 102 and 103 Rejection, Applicant argues the prior art, Lafon does not teach the amendments to independent claims 1, 8, and 9. Additionally, Applicant argues Lafon does not teach (1) that period prediction indications are displayed differently based on the confidence level of the prediction and (2) that period prediction indications cease to be displayed in response to the user logging a second type of health-related event (e.g., pregnancy). Applicant further argues the prior art, Barton-Sweeney, does not cure the deficiencies of Lafon. Examiner respectfully disagrees. Applicant’s arguments with respect to the amendments to the claims 1, 8 and 9 (limitations for period prediction indications cease to be displayed in response to the user logging a second type of health-related event) 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 in regards to only the claim amendments of independent claims 1, 8 and 9. Under broadest reasonable interpretation, Lafon recites at paragraph [0059] “The data values may be weighted by different amounts, such as may be based upon strength of prediction or accuracy, among other such factors. These weightings can be updated or modified over time, such as may be based upon machine learning or changes in a woman's body or state, etc. There may also be different confidence levels or other factors that can impact the relative weightings as well. The weight values chosen can also depend on the signal-to-noise ration of some signals. Menstrual cycle event prediction can be associated with a relative percentage of accuracy. For example, a level of confidence for fertile window prediction can be determined. In addition, or alternative to determining a binary value for the fertile window (true or false), such a level of confidence can be useful to a user who is trying to conceive or is avoiding conception as she can determine how likely she is within a fertile window” (e.g., period prediction indications are displayed differently based on the confidence level of the prediction).
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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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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