DETAILED ACTION
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 the Claims
The office action is in response to the claims filed on January 20, 2026, for the application filed on March 1, 2023, which claims priority to the provisional application filed on March 2, 2022. Claims 1, 3 – 8, and 10 – 20 are currently pending and have been examined.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 10, and 20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Kinnunen (U.S. Pub. No. 2021/0007658 A1) in view of Robinson (U.S. Pub. No. 2022/0096007 A1), Tran ‘064 (U.S. Pub. No. 2014/0143064 A1), and Shaw (U.S. Pub. No. 2024/0000375 A1).
Regarding claims 1, 10, and 20, Breslow teaches the limitations of representative claim 20 in bold as:
a system for providing health-related insights to a user, (Paragraph [0007] of Kinnunen. In the instant application, the broadest reasonable interpretation of “a system for providing health-related insights to a user” reads on the system in Kinnunen (Paragraph [0007]) for providing feedback to a user for improving performance level management.) comprising:
a wearable ring device (Paragraphs [0119] – [0120] and [0239] of Kinnunen. In the instant application, the broadest reasonable interpretation of “a wearable ring device” reads on the wearable electronic device in Kinnunen (Paragraphs [0119] – [0120] and [0239]) that is a ring configured to be worn on a finger.) configured to:
measure, via one or more light-emitting components and one or more light-receiving sensors arranged on an inner curved surface of the wearable ring device configured to be worn on a finger of a user, baseline physiological data, the baseline physiological data collected throughout a plurality of sleep days, wherein the baseline physiological data comprises at least heart rate data, sleep data, and temperature data, the sleep data comprising at least sleep pattern data and sleep regularity data associated with the user (Paragraphs [0121], [0171], [0239], [0257], and [0259] – [0260] of Kinnunen. In the instant application, the broadest reasonable interpretation of “measure … baseline physiological data, the baseline physiological data collected throughout a plurality of sleep days, wherein the baseline physiological data comprises at least heart rate data, sleep data, and temperature data, the sleep data comprising at least sleep pattern data and sleep regularity data associated with the user” reads on the activity in Kinnunen (Paragraphs [0121], [0171], [0239], [0257], and [0259] – [0260]) of measuring, for past days, heart rate, temperatures, sleep duration, heart-rate-variability, sleeping pattern, duration of sleep, sleep cycle of the user, etc.); and
measure, via the one or more light-emitting components and the one or more light-receiving sensors arranged on the inner curved surface of the wearable ring device, additional physiological data, the additional physiological data collected throughout a first sleep day subsequent to the plurality of sleep days (The limitation “additional physiological data” in claim 20 is replaced with “additional heart rate data” in claim 10.) (Paragraphs [0121], [0171], [0239], [0257], and [0259] – [0260] of Kinnunen. In the instant application, the broadest reasonable interpretation of “measure … baseline physiological data, the baseline physiological data collected throughout a plurality of sleep days, wherein the baseline physiological data comprises at least heart rate data, sleep data, and temperature data, the sleep data comprising at least sleep pattern data and sleep regularity data associated with the user” reads on the activity in Kinnunen (Paragraphs [0121], [0171], [0239], [0257], and [0259] – [0260]) of measuring, for present day, heart rate, temperatures, sleep duration, heart-rate-variability, sleeping pattern, duration of sleep, sleep cycle of the user, etc.), wherein the wearable ring device comprises:
a ring-shaped housing having the inner curved surface and an outer curved surface, wherein at least a portion of the inner curved surface is configured to contact a tissue of the finger of the user;
the one or more light-emitting components configured to emit light through the inner curved surface of the ring-shaped housing into the tissue of the user;
the one or more light-receiving sensors configured to receive the light transmitted by the one or more light-emitting components through the inner curved surface of the ring-shaped housing;
one or more processors disposed at least partially within the ring-shaped housing, the one or more processors electrically coupled with the one or more light-emitting components and the one or more light-receiving sensors (Paragraphs [0119], [0121], and [0162] of Kinnunen. In the instant application, the broadest reasonable interpretation of “one or more processors disposed at least partially within the ring-shaped housing, the one or more processors electrically coupled with … the one or more light-receiving sensors” reads on the controller in Kinnunen (Paragraphs [0119], [0121], and [0162]) of the wearable device (e.g., a ring configured to be suitably worn on a finger), with a light sensor embedded in the wearable device.);
a curved battery disposed at least partially within the ring-shaped housing, the curved battery electrically coupled with the one or more light-emitting components, the one or more light-receiving sensors, and the one or more processors; and
a communication module electrically coupled with the one or more processors, the communication module configured to transmit the baseline physiological data and the additional physiological data generated by the one or more processors (Paragraphs [0121] and [0238] of Kinnunen. In the instant application, the broadest reasonable interpretation of “a communication module electrically coupled with the one or more processors, the communication module configured to transmit the baseline physiological data and the additional physiological data generated by the one or more processors” reads on the communication module in Kinnunen (Paragraphs [0121] and [0238]) to establish a communication between the wearable electronic device and the mobile communication device, in order to transmit data related to the user's movement, heart rate, temperatures, sleep duration, circadian rhythm, heart-rate-variability, a respiration rate, and a sleeping pattern of the user.);
a user device communicatively coupled with the wearable ring device (Paragraphs [0121] and [0238] of Kinnunen. In the instant application, the broadest reasonable interpretation of “a user device communicatively coupled with the wearable ring device” reads on the mobile communication device in Kinnunen (Paragraphs [0121] and [0238]) in communication with the wearable electronic device.); and
one or more additional processors communicatively coupled with the wearable ring device and the user device (Paragraphs [0121] and [0238] of Kinnunen. In the instant application, the broadest reasonable interpretation of “a user device communicatively coupled with the wearable ring device” reads on the mobile communication device in Kinnunen (Paragraphs [0121] and [0238]) in communication with the wearable electronic device.), the one or more processors configured to:
determine, via the one or more additional processors communicatively coupled with the wearable ring device, a circadian rhythm chronotype associated with the user based at least in part on the sleep pattern data and the sleep regularity data (Paragraphs [0121] and [0238] of Kinnunen. In the instant application, the broadest reasonable interpretation of “the one or more additional processors communicatively coupled with the wearable ring device” reads on the server in Kinnunen (Paragraphs [0121] and [0238]) configured to communicate with the mobile communication device, with the mobile communication device configured to communicate with the wearable electronic device.);
identify, via the one or more additional processors communicatively coupled with the wearable ring device, baseline restorative moment data associated with the circadian rhythm chronotype of the user based at least in part on the heart rate data and the temperature data, the baseline restorative moment data comprising a first quantity of restorative moments within the plurality of sleep days in which the user is in a relaxed state (Paragraphs [0121], [0153], [0167], [0171], [0238] – [0239], [0257], [0259] – [0260], and [0268] of Kinnunen. In the instant application, the broadest reasonable interpretation of “the one or more additional processors communicatively coupled with the wearable ring device” reads on the server in Kinnunen (Paragraphs [0121] and [0238]) configured to communicate with the mobile communication device, with the mobile communication device configured to communicate with the wearable electronic device. The broadest reasonable interpretation of “identify … baseline restorative moment data associated with the circadian rhythm chronotype of the user based at least in part on the heart rate data and the temperature data, the baseline restorative moment data comprising a first quantity of restorative moments within the plurality of sleep days in which the user is in a relaxed state” reads on the activity in Kinnunen (Paragraph [0167] and [0171]) of identifying, based on past days’ measurements, heart rate data and body temperature of the user when the lowest body temperature during nighttime is measured.);
identify, via the one or more additional processors communicatively coupled with the wearable ring device, additional restorative moment data associated with the user based at least in part on the additional physiological data, the additional restorative moment data comprising a second quantity of restorative moments within the first sleep day (Paragraphs [0121], [0153], [0167], [0171], [0238] – [0239], [0257], [0259] – [0260], and [0268] of Kinnunen. In the instant application, the broadest reasonable interpretation of “the one or more additional processors communicatively coupled with the wearable ring device” reads on the server in Kinnunen (Paragraphs [0121] and [0238]) configured to communicate with the mobile communication device, with the mobile communication device configured to communicate with the wearable electronic device. The broadest reasonable interpretation of “identify … the additional restorative moment data comprising a second quantity of restorative moments within the first sleep day” reads on the activity in Kinnunen (Paragraph [0167] and [0171]) of identifying, based on present day measurements, heart rate data and body temperature of the user when the lowest body temperature during nighttime is measured.);
compare, via the one or more additional processors communicatively coupled with the wearable ring device, the additional restorative moment data and the baseline restorative moment data associated with the circadian rhythm chronotype of the user (Paragraphs [0121], [0153], and [0238] of Kinnunen. In the instant application, the broadest reasonable interpretation of “the one or more additional processors communicatively coupled with the wearable ring device” reads on the server in Kinnunen (Paragraphs [0121] and [0238]) configured to communicate with the mobile communication device, with the mobile communication device configured to communicate with the wearable electronic device. The broadest reasonable interpretation of “compare … the additional restorative moment data and the baseline restorative moment data associated with the circadian rhythm chronotype of the user” reads on the activity in Kinnunen (Paragraph [0153]) of comparing a value of the parameter (e.g., the user's heart rate, and user's body temperature) to a long term average.);
identify, via the one or more additional processors communicatively coupled with the wearable ring device, a trigger condition for providing a health-related insight associated with the second quantity of restorative moments based at least in part on comparing the additional restorative moment data and the baseline restorative moment data (Paragraphs [0121], [0238], [0249], and [0262] – [0275] of Kinnunen. In the instant application, the broadest reasonable interpretation of “the one or more additional processors communicatively coupled with the wearable ring device” reads on the server in Kinnunen (Paragraphs [0121] and [0238]) configured to communicate with the mobile communication device, with the mobile communication device configured to communicate with the wearable electronic device. broadest reasonable interpretation of “identify … a trigger condition for providing a health-related insight associated with the second quantity of restorative moments based at least in part on comparing the additional restorative moment data and the baseline restorative moment data” reads on the activity in Kinnunen (Paragraphs [0249] and [0268]) of searching for the most negative single contributing parameter when the readiness score (i.e., based on sleep score, user's lowest moving average heart rate during the night, users' maximum body temperature during the night, etc.) is below a personal long-term average.); and
transmit a signal to cause a graphical user interface of the user device to display the health-related insight associated with the additional physiological data (Paragraphs [0244], [0253], [0256], and [0262] – [0275] of Kinnunen. In the instant application, the broadest reasonable interpretation of “transmit a signal to cause a graphical user interface of the user device to display the health-related insight associated with the additional physiological data” reads on the feedback module in Kinnunen (Paragraphs [0244], [0253], [0256], and [0262] – [0275]) providing, via an exemplary user interface, feedback, alerts, and messages to a user, with the feedback including a graph of numeric data measured from the user, and a written instruction. The graph may show the readiness score determined for the user over the course of last five days, and user average over longer term, with sleep duration and circadian rhythm related factors taken into account in sleep score, which is one parameter used to determine the readiness score. However, readiness score is also taking into account other parameters too, such as users' previous day's physical activity level, physical activity accumulated over e.g. 2 weeks, user's lowest moving average heart rate during the night, users' maximum body temperature during the night, etc. A readiness score in Kinnunen exceeding the previous day's level with a clear margin (e.g. >5%) triggers a positive message about recovery.);
Kinnunen does not appear to explicitly disclose, but Robinson teaches the limitation identified in bold as “measure, via one or more light-emitting components and one or more light-receiving sensors arranged on an inner curved surface of the wearable ring device configured to be worn on a finger of a user, baseline physiological data, the baseline physiological data collected throughout a plurality of sleep days, wherein the baseline physiological data comprises at least heart rate data, sleep data, and temperature data, the sleep data comprising at least sleep pattern data and sleep regularity data associated with the user” (Paragraphs [0158] – [0159] and [0162] and FIG. 19 of Robinson. In the instant application, the broadest reasonable interpretation of “the one or more light-emitting components and one or more light-receiving sensors arranged on an inner curved surface of the wearable ring device configured to be worn on a finger of a user” reads on the one or more emitters (1905 and 1906) and the one or more detectors (1907 and 1908) of Robinson (Paragraphs [0158] – [0159] and [0162] and FIG. 19) arranged on the inner curved surface of the apparatus (i.e., finger ring) configured to be worn on a finger.).
Kinnunen does not appear to explicitly disclose, but Robinson teaches the limitation identified in bold as “measure, via the one or more light-emitting components and the one or more light-receiving sensors arranged on the inner curved surface of the wearable ring device, additional physiological data, the additional physiological data collected throughout a first sleep day subsequent to the plurality of sleep days” (Paragraphs [0158] – [0159] and [0162] and FIG. 19 of Robinson. In the instant application, the broadest reasonable interpretation of “the one or more light-emitting components and the one or more light-receiving sensors arranged on the inner curved surface of the wearable ring device” reads on the one or more emitters (1905 and 1906) and the one or more detectors (1907 and 1908) of Robinson (Paragraphs [0158] – [0159] and [0162] and FIG. 19) arranged on the inner curved surface of the apparatus (i.e., finger ring) configured to be worn on a finger.).
Kinnunen does not appear to explicitly disclose, but Robinson teaches the limitation identified in bold as “a ring-shaped housing having the inner curved surface and an outer curved surface, wherein at least a portion of the inner curved surface is configured to contact a tissue of the finger of the user” (Paragraph [0158] and FIG. 19 of Robinson. In the instant application, the broadest reasonable interpretation of “a ring-shaped housing having the inner curved surface and an outer curved surface, wherein at least a portion of the inner curved surface is configured to contact a tissue of the finger of the user” reads on the apparatus (i.e., finger ring) on Robinson (Paragraph [0158] and FIG. 19) having the inner curved surface configured to be worn on a finger.).
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Kinnunen does not appear to explicitly disclose, but Robinson teaches the limitation identified in bold as “the one or more light-emitting components configured to emit light through the inner curved surface of the ring-shaped housing into the tissue of the user” (Paragraphs [0209], [0299], and [0321] – [0322] and FIG. 9 of Tran ‘064. In the instant application, the broadest reasonable interpretation of “the one or more light-emitting components configured to emit light through the inner curved surface of the ring-shaped housing into the tissue of the user” reads on the light source in Tran ‘064 (Paragraphs [0209], [0299], and [0321] – [0322] and FIG. 9) emitting light from a window (i.e., in the inner curved surface) into the tissue of the of the user.).
Kinnunen does not appear to explicitly disclose, but Robinson teaches the limitation identified in bold as “the one or more light-receiving sensors configured to receive the light transmitted by the one or more light-emitting components through the inner curved surface of the ring-shaped housing” (Paragraphs [0209], [0299], and [0321] – [0322] and FIG. 9 of Tran ‘064. In the instant application, the broadest reasonable interpretation of “the one or more light-receiving sensors configured to receive the light transmitted by the one or more light-emitting components through the inner curved surface of the ring-shaped housing” reads on the photo-detector in Tran ‘064 (Paragraphs [0209], [0299], and [0321] – [0322] and FIG. 9) receiving reflected light in a second window (i.e., in the inner curved surface).).
Kinnunen does not appear to explicitly disclose, but Robinson teaches the limitation identified in bold as “one or more processors disposed at least partially within the ring-shaped housing, the one or more processors electrically coupled with the one or more light-emitting components and the one or more light-receiving sensors” (Paragraphs [0209], [0299], and [0321] – [0322] and FIG. 9 of Tran ‘064. In the instant application, the broadest reasonable interpretation of “the one or more light-emitting components” reads on the light source in Tran ‘064 (Paragraphs [0209], [0299], and [0321] – [0322] and FIG. 9) emitting light from a window (i.e., in the inner curved surface) into the tissue of the of the user.).
Kinnunen does not appear to explicitly disclose, but Robinson teaches the limitation identified in bold as “a curved battery disposed at least partially within the ring-shaped housing, the curved battery electrically coupled with the one or more light-emitting components, the one or more light-receiving sensors, and the one or more processors” (Paragraphs [0162], [0185], and [0207] of Robinson. In the instant application, the broadest reasonable interpretation of “a curved battery disposed at least partially within the ring-shaped housing, the curved battery electrically coupled with the one or more light-emitting components, the one or more light-receiving sensors, and the one or more processors” reads on the battery in Robinson (Paragraphs [0162], [0185], and [0207]) for the control system (i.e., processor), with the battery providing power to the emitters and the detector.).
Kinnunen does not appear to explicitly disclose, but Robinson teaches the limitation identified in bold as “determine, via the one or more additional processors communicatively coupled with the wearable ring device, a circadian rhythm chronotype associated with the user based at least in part on the sleep pattern data and the sleep regularity data” (Paragraphs [0004] and [0077] of Shaw. In the instant application, the broadest reasonable interpretation of “determine … a circadian rhythm chronotype associated with the user based at least in part on the sleep pattern data and the sleep regularity data” reads on the step in Shaw (Paragraphs [0004] and [0077]) of determining the specific chronotype classification based on the sleep/wake times, activities record, and heart rate.).
Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of filing to modify the wearable system of Breslow (Paragraph [0276]) to further include: the activity of measuring, via one or more light-emitting components and one or more light-receiving sensors arranged on an inner curved surface of the wearable ring device configured to be worn on a finger of a user, baseline physiological data, the baseline physiological data collected throughout a plurality of sleep days, wherein the baseline physiological data comprises at least heart rate data, sleep data, and temperature data, the sleep data comprising at least sleep pattern data and sleep regularity data associated with the user, the activity of measuring, via the one or more light-emitting components and the one or more light-receiving sensors arranged on the inner curved surface of the wearable ring device, additional physiological data, the additional physiological data collected throughout a first sleep day subsequent to the plurality of sleep days, the ring-shaped housing including the inner curved surface and an outer curved surface, wherein at least a portion of the inner curved surface is configured to contact a tissue of the finger of the user, and the curved battery disposed at least partially within the ring-shaped housing, the curved battery electrically coupled with the one or more light-emitting components, the one or more light-receiving sensors, and the one or more processors, as taught by Robinson (Paragraphs [0158] – [0159], [0162] – [0163], [0185], [0207], [0321] and FIG. 19) configured to be worn on a finger of a user in order to provide information to the user so that the user can make near-term lifestyle changes that can improve physical performance, health, and general wellbeing (Abstract of Robinson); the one or more light-emitting components configured to emit light through the inner curved surface of the ring-shaped housing into the tissue of the user, the one or more light-receiving sensors configured to receive the light transmitted by the one or more light-emitting components through the inner curved surface of the ring-shaped housing, and the one or more light-emitting components, as taught by Tran ‘064 (Paragraphs [0209], [0299], and [0321] – [0322] and FIG. 9) in order to provide timely identification and treatment of a patient condition (Paragraph [0002] of Tran ‘064); and the activity of determining, via the one or more additional processors communicatively coupled with the wearable ring device, a circadian rhythm chronotype associated with the user based at least in part on the sleep pattern data and the sleep regularity data, as taught by Shaw (Paragraphs [0004] and [0077]) in order to maximize energy for different activities and provide notifications at different times based on a determined chronotype (Paragraph [0007] of Shaw).
Claims 3 – 4, 11, and 17 – 18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Kinnunen as modified by Robinson, Tran ‘064, and Shaw and applied to an associated one of claims 1 and 10, and further in view of Breslow (U.S. Pub. No. 2016/0374569 A1).
Regarding claim 3, Kinnunen as modified by Robinson, Tran ‘064, and Shaw and applied to claim 1 does not appear to explicitly disclose, but Breslow teaches the limitations in bold as:
wherein the baseline physiological data further comprises temperature data (Paragraph [0067] of Breslow, In some embodiments, the wearable system may be configured to record other physiological parameters including, but not limited to, skin temperature (using a thermometer).), the method further comprising:
identifying baseline restorative moment data associated with the user based at least in part on the heart rate data and the temperature data, the baseline restorative moment data comprising a first quantity of restorative moments within the plurality of sleep days in which the user is in a relaxed state (Paragraph [0058] of Breslow, Exemplary embodiments provide wearable physiological measurements systems that are configured to provide continuous measurement of heart rate... Paragraph [0075] of Breslow, The sensors 602 may include a thermometer for monitoring the user's body or skin temperature. In one embodiment, the sensors may be used to recognize sleep based on a temperature drop, … and reduced heart rate as measured by the heart rate monitor. The body temperature, in conjunction with heart rate monitoring and motion, may be used to interpret whether a user is sleeping or just resting, as body temperature drops significantly when an individual is about to fall asleep), and how well an individual is sleeping as motion indicates a lower quality of sleep. Paragraph [0140] of Breslow, An intensity score or indicator provides an accurate indication of the cardiovascular intensities experienced by the user … during any desired period of time (e.g., during a week or month). The intensity score is customized and adapted for the unique physiological properties of the user and takes into account, for example, the user's … resting heart rate, maximum heart rate, and the like. Paragraph [0159] of Breslow, In one exemplary embodiment, the recovery score is a weighted combination of the user's heart rate variability (HRV), resting heart rate, sleep quality indicated by a sleep score, and recent strain (indicated, in one example, by the intensity score of the user)... By considering sleep and HRV alone or in combination, the user can understand how exercise-ready he/she is each day and to understand how he/she arrived at the exercise-readiness score each day, for example, whether a low exercise-readiness score is a predictor of poor recovery habits or an inappropriate training schedule. This insight aids the user in adjusting his/her daily activities, exercise regimen and sleeping schedule therefore obtain the most out of his/her training. In the instant application, the broadest reasonable interpretation of “identifying baseline restorative moment data associated with the user based at least in part on the heart rate data and the temperature data, the baseline restorative moment data comprising a first quantity of restorative moments within the plurality of sleep days in which the user is in a relaxed state” reads on the method of Breslow (Paragraph [0058], [0075], [0140], and [0159]) determining the recovery score and the intensity score by taking into account the user's body temperature and resting heart rate during any desired period of time (e.g., during a week or month, throughout an exercise routine).); and
identifying additional restorative moment data associated with the user based at least in part on the additional physiological data, the additional restorative moment data comprising a second quantity of restorative moments within the first sleep day (Paragraph [0058] of Breslow, Exemplary embodiments provide wearable physiological measurements systems that are configured to provide continuous measurement of heart rate... Paragraph [0140] of Breslow, An intensity score or indicator provides an accurate indication of the cardiovascular intensities experienced by the user during … during any desired period of time (e.g., during a week or month). The intensity score is customized and adapted for the unique physiological properties of the user and takes into account, for example, the user's … resting heart rate, maximum heart rate, and the like... If determined for a period of including and beyond an exercise routine, the intensity score provides an indication of … the activities the user performed after the routine (e.g., resting on the couch, active day of shopping) that may affect their recovery or exercise readiness. Paragraph [0159] of Breslow, In one exemplary embodiment, the recovery score is a weighted combination of the user's heart rate variability (HRV), resting heart rate, sleep quality indicated by a sleep score, and recent strain (indicated, in one example, by the intensity score of the user). In the instant application, the broadest reasonable interpretation of the “identifying additional restorative moment data associated with the user based at least in part on the additional physiological data, the additional restorative moment data comprising a second quantity of restorative moments within the first sleep day” reads on the method of Breslow (Paragraph [0058], [0140], and [0159]) determining the recovery score and the intensity score by taking into account, for example, the user's body temperature and resting heart rate as continuously measured by the wearable physiological measurements system after previously determining one or more other recovery scores and intensity scores.),
wherein identifying the trigger condition for providing the health-related insight is based at least in part on a comparison between the baseline restorative moment data and the additional restorative moment data (Paragraph [0010] of Breslow, The computer program product may include code that performs the step of calculating additional recovery scores after one or more other waking events of the user for comparison to the recovery score. Paragraph [0262] of Breslow, As shown in step 2720, the method 2700 may include calculating a recovery score for the user based upon the heart rate variability from the last phase of sleep. The calculation may be based on other sources of data… The method 2700 may further include calculating additional recovery scores after one or more other waking events of the user for comparison to the previously calculated recovery score. In the instant application, the broadest reasonable interpretation of “identifying the trigger condition for providing the health-related insight is based at least in part on a comparison between the baseline restorative moment data and the baseline restorative moment data” reads on the method 2700 of Breslow (Paragraphs [0010] and [0262]) performing the step of calculating additional recovery scores after one or more other waking events of the user for comparison to the previously calculated recovery score.).
Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, and Shaw such that the baseline physiological data further comprises temperature data, the method further comprising: b. identifying baseline restorative moment data associated with the user based at least in part on the heart rate data and the temperature data, the baseline restorative moment data comprising a first quantity of restorative moments within the plurality of sleep days in which the user is in a relaxed state, identifying additional restorative moment data associated with the user based at least in part on the additional physiological data, the additional restorative moment data comprising a second quantity of restorative moments within the first sleep day, wherein identifying the trigger condition for providing the health-related insight is based at least in part on a comparison between the baseline restorative moment data and the additional restorative moment data, as taught by Breslow (Paragraphs [0010], [0058], [0067], [0140], [0159], [0211], and [0262]), in order to provide accurate instrumentation and quantification of physical recovery resulting from sleep (Paragraph [0002] of Breslow).
Regarding claim 4, Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow and applied to claim 3 teaches the limitations in bold as identifying the trigger condition based at least in part on a comparison between the first quantity of restorative moments and the second quantity of restorative moments (Paragraph [0010] of Breslow, The computer program product may include code that performs the step of calculating additional recovery scores after one or more other waking events of the user for comparison to the recovery score. Paragraph [0262] of Breslow, As shown in step 2720, the method 2700 may include calculating a recovery score for the user based upon the heart rate variability from the last phase of sleep. The calculation may be based on other sources of data… The method 2700 may further include calculating additional recovery scores after one or more other waking events of the user for comparison to the previously calculated recovery score. In the instant application, the broadest reasonable interpretation of “identifying the trigger condition based at least in part on a comparison between the first quantity of restorative moments and the second quantity of restorative moments” reads on the method 2700 of Breslow (Paragraphs [0010] and [0262]) performing the step of calculating additional recovery scores after one or more other waking events of the user for comparison to the previously calculated recovery score.).
Regarding claim 11, Kinnunen as modified by Robinson, Tran ‘064, and Shaw and applied to claim 10 does not appear to explicitly disclose, but Breslow teaches the limitations in bold as:
wherein the baseline heart rate data comprises a first average heart rate per sleep day throughout the plurality of sleep days, and wherein the additional heart rate data comprises a second average heart rate throughout the first sleep day (Paragraph [0161] of Breslow, With regard to the user's HRV used in determining the recovery score, suitable techniques for analyzing HRV include, but are not limited to, time-domain methods, frequency-domain methods, geometric methods and non-linear methods. In one embodiment, the HRV metric of the root-mean-square of successive differences (RMSSD) of RR intervals is used. The analytics system may consider the magnitude of the differences between 7-day moving averages and 3-day moving averages of these readings for a given day. Paragraph [0163] of Breslow, With regard to the user's resting heart rate, moving averages of the resting heart rate are analyzed to determine significant deviations.
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Consideration of the moving averages is important since day-to-day physiological variation is quite large even in healthy individuals. In the instant application, the broadest reasonable interpretation of “the baseline heart rate data comprises [a] first average heart rate per sleep day throughout the plurality of sleep days, and wherein the additional heart rate data comprises a second average heart rate throughout the first sleep day” reads on the method of Breslow (Paragraphs [0161] and [0163], FIG. 18B) including the step of analyzing the moving averages of resting heart rate to determine significant deviations), the method further comprising:
identifying the trigger condition based at least in part on a difference between the first average heart rate and the second average heart rate satisfying a threshold (Paragraph [0161] of Breslow, With regard to the user's HRV used in determining the recovery score, suitable techniques for analyzing HRV include, but are not limited to, time-domain methods, frequency-domain methods, geometric methods and non-linear methods. In one embodiment, the HRV metric of the root-mean-square of successive differences (RMSSD) of RR intervals is used. The analytics system may consider the magnitude of the differences between 7-day moving averages and 3-day moving averages of these readings for a given day. Paragraph [0163] of Breslow, With regard to the user's resting heart rate, moving averages of the resting heart rate are analyzed to determine significant deviations. Consideration of the moving averages is important since day-to-day physiological variation is quite large even in healthy individuals. In the instant application, the broadest reasonable interpretation of “identifying the trigger condition based at least in part on a difference between the first average heart rate and the second average heart rate satisfying a threshold” reads on the method of Breslow (Paragraphs [0161] and [0163], FIG. 18B) including the step of determining significant deviations in moving averages of resting heart rate.
Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, and Shaw such that the baseline heart rate data comprises a first average heart rate per sleep day throughout the plurality of sleep days, and wherein the additional heart rate data comprises a second average heart rate throughout the first sleep day, the method further comprising: b. identifying the trigger condition based at least in part on a difference between the first average heart rate and the second average heart rate satisfying a threshold, as taught by Breslow (Paragraphs [0010], [0058], [0067], [0140], [0159], [0211], and [0262]), in order to provide accurate instrumentation and quantification of physical recovery resulting from sleep (Paragraph [0002] of Breslow).
Regarding claim 17, Kinnunen as modified by Robinson, Tran ‘064, and Shaw and applied to claim 10 does not appear to explicitly disclose, but Breslow teaches the limitations in bold as:
the baseline heart rate data and the additional heart rate data comprise heart rates for the user while the user is awake (Paragraph [0178] of Breslow, FIGS. 15A-18B illustrate an exemplary user interface 1500 for displaying physiological data specific to a user as rendered on visual display device. Paragraph [0179] of Breslow, The user interface 1500 may also include panels for presenting information on the user's workouts—a workout panel 1514 accessible using tab 1516, day—a day panel 1518 accessible using tab 1520... Paragraph [0184] of Breslow, The day panel 1518 may include information on health parameters of the user during the current day including, but not limited to, the number of calories burned and the number of calories taken in 1500 (which may be based on user input on the foods eaten), a graph 1554 of the day's continuous heart rate, statistics 1556 on the resting heart rate and steps taken by the user that day, a graph 1558 of the calories burned that and other days, and the like.), or
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wherein the baseline heart rate data and the additional heart rate data comprise heart rates for the user while the user is asleep (Paragraph [0178] of Breslow, FIGS. 15A-18B illustrate an exemplary user interface 1500 for displaying physiological data specific to a user as rendered on visual display device. Paragraph [0179] of Breslow, The user interface 1500 may also include panels for presenting information on the user's … sleep—a sleep panel 1522 accessible using tab 1524. Paragraph [0187] of Breslow, The sleep panel 1522 may include information on health parameters of the user during sleep including, but not limited to, an overlaid graph 1573 of heart rate and movement during sleep, statistics 1574 on the maximum heart rate, minimum heart rate, number of times the user awoke during sleep, average movement during sleep, a sleep cycle indicator 1576 showing durations spent awake, in light sleep, in deep sleep and in REM sleep, and a sleep duration graph 1578 showing the number of hours slept over a period of time.).
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Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, and Shaw such that the baseline heart rate data and the additional heart rate data comprise heart rates for the user while the user is awake, b. wherein the baseline heart rate data and the additional heart rate data comprise heart rates for the user while the user is asleep, as taught by Breslow (Paragraphs [0010], [0058], [0067], [0140], [0159], [0211], and [0262]), in order to provide accurate instrumentation and quantification of physical recovery resulting from sleep (Paragraph [0002] of Breslow).
Regarding claim 18, Kinnunen as modified by Robinson, Tran ‘064, and Shaw and applied to claim 10 does not appear to explicitly disclose, but Breslow teaches the limitations in bold as identifying the user has awaken from sleep based at least in part on the additional heart rate data, an interaction with the graphical user interface, or both, wherein causing the graphical user interface to display the health-related insight is based at least in part on identifying the user has awaken from sleep (Paragraph [0178] of Breslow, FIGS. 15A-18B illustrate an exemplary user interface 1500 for displaying physiological data specific to a user as rendered on visual display device. Paragraph [0179] of Breslow, The user interface 1500 may also include panels for presenting information on the user's workouts—a workout panel 1514 accessible using tab 1516, day—a day panel 1518 accessible using tab 1520... Paragraph [0184], The day panel 1518 may include information on health parameters of the user during the current day including, but not limited to, the number of calories burned and the number of calories taken in 1500 (which may be based on user input on the foods eaten), a graph 1554 of the day's continuous heart rate, statistics 1556 on the resting heart rate and steps taken by the user that day, a graph 1558 of the calories burned that and other days, and the like.).
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Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, and Shaw to further include the activity of identifying the user has awaken from sleep based at least in part on the additional heart rate data, an interaction with the graphical user interface, or both, wherein causing the graphical user interface to display the health-related insight is based at least in part on identifying the user has awaken from sleep, as taught by Breslow (Paragraphs [0010], [0058], [0067], [0140], [0159], [0211], and [0262]), in order to provide accurate instrumentation and quantification of physical recovery resulting from sleep (Paragraph [0002] of Breslow).
Claims 5 – 6 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow and applied to claim 3 above, and further in view of Tran ‘656 (U.S. Pub. No. 2021/0233656 A1).
Regarding claim 5, Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow and applied to claim 3 does not appear to explicitly disclose, but Tran ‘656 teaches the limitations in bold as:
the baseline physiological data comprises an average quantity of restorative moments per sleep day throughout the plurality of sleep days (Paragraph [0211], One embodiment determines a heart rate intensity (HR intensity) and to determine a real-time workload status: a sprint or compensating state if the HR reading or HR intensity significantly increases; an aerobic endurance state if the increased HR intensity is fixed within a zone wherein a HR intensity fluctuates between a range; and a dynamic recovery state if the HR intensity decreases about 1% to about 20% than the average HR intensity of the aerobic endurance state for a time period that is longer than that of the sprint or compensating state. In the instant application, the broadest reasonable interpretation of “the baseline physiological data comprises an average quantity of restorative moments per sleep day throughout the plurality of sleep days” reads on the average HR intensity of Tran ‘656 (Paragraph [0211]) in the aerobic endurance state for a time period that is longer than that of the sprint or compensating state.), the method further comprising:
identifying the trigger condition based at least in part on a difference between the second quantity of restorative moments and the average quantity of restorative moments satisfying a threshold (Paragraph [0211], One embodiment determines a heart rate intensity (HR intensity) and to determine a real-time workload status: a sprint or compensating state if the HR reading or HR intensity significantly increases; an aerobic endurance state if the increased HR intensity is fixed within a zone wherein a HR intensity fluctuates between a range; and a dynamic recovery state if the HR intensity decreases about 1% to about 20% than the average HR intensity of the aerobic endurance state for a time period that is longer than that of the sprint or compensating state. In the instant application, the broadest reasonable interpretation of “identifying the trigger condition based at least in part on a difference between the second quantity of restorative moments and the average quantity of restorative moments satisfying a threshold” reads on the dynamic recovery state of Tran ‘656 (Paragraph [0211]) if the HR intensity decreases about 1% to about 20% than the average HR intensity of the aerobic endurance state for a time period that is longer than that of the sprint or compensating state.).
Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow such that the trigger condition is based at least in part on a difference between the second quantity of restorative moments and the average quantity of restorative moments satisfying a threshold, as taught by Tran ‘656 (Paragraph [0211]), in order to improve the understanding of correlations between user behaviors, physical activity metrics, and appropriate therapeutic interventions (Paragraph [0015] of Tran ‘656).
Regarding claim 6, Kinnunen as modified by Robinson, Tran ‘064, Shaw, Breslow, and Tran ‘656 and applied to claim 5 teaches the limitations in bold as:
the difference satisfies the threshold if the second quantity of restorative moments exceeds the first quantity of restorative moments by a first threshold quantity of restorative moments (Paragraph [0211], One embodiment determines a heart rate intensity (HR intensity) and to determine a real-time workload status: … an aerobic endurance state if the increased HR intensity is fixed within a zone wherein a HR intensity fluctuates between a range… In the instant application, the broadest reasonable interpretation of “the difference satisfies the threshold if the second quantity of restorative moments exceeds the first quantity of restorative moments by a first threshold quantity of restorative moments” reads on the HR intensity of Tran ‘656 (Paragraph [0211]) is fixed within a zone wherein the HR intensity fluctuates between a range.), or
wherein the difference satisfies the threshold if the second quantity of restorative moments is less than the first quantity of restorative moments by a second threshold quantity of restorative moments (Paragraph [0211], One embodiment determines a heart rate intensity (HR intensity) and to determine a real-time workload status: … a dynamic recovery state if the HR intensity decreases about 1% to about 20% than the average HR intensity of the aerobic endurance state for a time period that is longer than that of the sprint or compensating state.).
Regarding claim 8, Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow and applied to claim 3 does not appear to explicitly disclose, but Tran ‘656 teaches the limitation in bold as the first quantity of restorative moments and the second quantity of restorative moments comprise time intervals in which the user is awake and in the relaxed state (Paragraph [0211], The coaching can optimize HR intensity (also known as percent heart rate reserve, heart rate capacity, target heart rate, or % HRR). For example, the maximum heart rate (HRmax) is the highest heart rate an individual can safely achieve through exercise stress, and depends on age. The resting heart rate (HRrest) of an individual may be obtained by any suitable method including, for example, a heart rate measurement taken when the activity level of the individual is sufficiently low to be considered inactive. Alternatively, the HRrest is an individual's heart rate when he/she is at rest, that is lying down but awake, and not having recently exerted themselves.).
Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow such that the first quantity of restorative moments and the second quantity of restorative moments comprise time intervals in which the user is awake and in the relaxed state, as taught by Tran ‘656 (Paragraph [0211]), in order to improve the understanding of correlations between user behaviors, physical activity metrics, and appropriate therapeutic interventions (Paragraph [0015] of Tran ‘656).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow and applied to claim 3 above, and further in view of NPL Sajjadieh.
Regarding claim 7, Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow and applied to claim 3 teaches the limitation in bold as:
the additional physiological data comprises additional heart rate data and additional temperature data (Paragraph [0058] of Breslow, Exemplary embodiments provide wearable physiological measurements systems that are configured to provide continuous measurement of heart rate... Paragraph [0067] of Breslow, In some embodiments, the wearable system may be configured to record other physiological parameters including, but not limited to, skin temperature (using a thermometer).), the method further comprising:
Breslow does not appear to explicitly disclose:
identifying the first quantity of restorative moments based at least in part on the heart rate data being less than or equal to a heart rate threshold and the temperature data being within a temperature range of a baseline temperature; and
identifying the second quantity of restorative moments based at least in part on the additional heart rate data being less than or equal to the heart rate threshold and the additional temperature data being within the temperature range of the baseline temperature.
NPL Sajjadieh teaches that it was old and well known in the art of wearable digital health technologies (DHT) at the time of the filing that heart rate decreases during sleep and generally follows the circadian curve of body temperature (Page 139 of NPL Sajjadieh, It is known that heart rate decreases during sleep and generally follows the circadian curve of body temperature.).
Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow to include the step of identifying the first quantity of restorative moments and the second quantity of restorative moments based at least in part on the heart rate data being less than or equal to a heart rate threshold and the temperature data being within a temperature range of a baseline temperature, as taught by NPL Sajjadieh (p. 139), in order to detect a restorative moment that can account for a decrease in heart rate during sleep as was well known in the art of wearable digital health technologies (DHT) at the time of the filing.
Claims 12 – 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow and applied to claim 11 above, and further in view of Tran ‘656.
Regarding claim 12, Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow and applied to claim 11 does not appear to explicitly disclose, but Tran ‘656 teaches the limitations in bold as:
the difference satisfies the threshold if the second average heart rate exceeds the first average heart rate by a first threshold value (Paragraph [0211] of Tran ‘656, One embodiment determines a heart rate intensity (HR intensity) and to determine a real-time workload status: a sprint or compensating state if the HR reading or HR intensity significantly increases; an aerobic endurance state if the increased HR intensity is fixed within a zone wherein a HR intensity fluctuates between a range; and a dynamic recovery state if the HR intensity decreases about 1% to about 20% than the average HR intensity of the aerobic endurance state for a time period that is longer than that of the sprint or compensating state. The coaching can optimize HR intensity (also known as percent heart rate reserve, heart rate capacity, target heart rate, or % HRR). For example, the maximum heart rate (HRmax) is the highest heart rate an individual can safely achieve through exercise stress, and depends on age. The resting heart rate (HRrest) of an individual may be obtained by any suitable method including, for example, a heart rate measurement taken when the activity level of the individual is sufficiently low to be considered inactive.), or
the difference satisfies the threshold if the second average heart rate is less than the first average heart rate by a second threshold value (Paragraph [0211] of Tran ‘656, One embodiment determines a heart rate intensity (HR intensity) and to determine a real-time workload status: a sprint or compensating state if the HR reading or HR intensity significantly increases; an aerobic endurance state if the increased HR intensity is fixed within a zone wherein a HR intensity fluctuates between a range; and a dynamic recovery state if the HR intensity decreases about 1% to about 20% than the average HR intensity of the aerobic endurance state for a time period that is longer than that of the sprint or compensating state. The coaching can optimize HR intensity (also known as percent heart rate reserve, heart rate capacity, target heart rate, or % HRR). For example, the maximum heart rate (HRmax) is the highest heart rate an individual can safely achieve through exercise stress, and depends on age. The resting heart rate (HRrest) of an individual may be obtained by any suitable method including, for example, a heart rate measurement taken when the activity level of the individual is sufficiently low to be considered inactive.).
Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow such that the difference satisfies the threshold if the second average heart rate exceeds the first average heart rate by a first threshold value or the difference satisfies the threshold if the second average heart rate is less than the first average heart rate by a second threshold value, as taught by Tran ‘656 (Paragraph [0211]), in order to improve the understanding of correlations between user behaviors, physical activity metrics, and appropriate therapeutic interventions (Paragraph [0015] of Tran ‘656).
Regarding claim 13, Kinnunen as modified by Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow and applied to claim 11 does not appear to explicitly disclose, but Tran ‘656 teaches the limitations in bold as the difference satisfies the threshold if the second average heart rate is approximately equal to the first average heart rate (Paragraph [0211] of Tran ‘656, One embodiment determines a heart rate intensity (HR intensity) and to determine a real-time workload status: … a dynamic recovery state if the HR intensity decreases about 1% to about 20% than the average HR intensity of the aerobic endurance state for a time period that is longer than that of the sprint or compensating state. In the instant application, the broadest reasonable interpretation of “the difference satisfies the threshold if the second average heart rate is approximately equal to the first average heart rate” reads on the method of Tran ‘656 (Paragraph [0211]) including the step of determining a real-time workload status of a dynamic recovery state if the HR intensity decreases about 1% than the average HR intensity of the aerobic endurance state.).
Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, Shaw, and Breslow to include the step of determining a real-time workload status of a dynamic recovery state if the HR intensity decreases about 1% than the average HR intensity of the aerobic endurance state, as taught by Tran ‘656 (Paragraph [0211]), in order to improve the understanding of correlations between user behaviors, physical activity metrics, and appropriate therapeutic interventions (Paragraph [0015] of Tran ‘656).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable Kinnunen as modified by Robinson, Tran ‘064, and Shaw and applied to claim 10 above, and further in view of Tobiassen (U.S. Pub. No. 2019/0228633 A1).
Regarding Claim 14, Kinnunen as modified by Robinson, Tran ‘064, and Shaw and applied to claim 10 does not appear to explicitly disclose, but Tobiassen teaches the limitations in bold as identifying the trigger condition based at least in part on the additional heart rate data comprising an indication that the user is not wearing the wearable ring device, wherein the health-related insight comprises a reminder for the user to wear the wearable ring device (Paragraph [0014] of Tobiassen, An object of the present invention presented herein is to provide a user with the possibility to act before a fall occurs. Paragraph [0085], The sensors may be for measuring heart rate, blood oxygen saturation, sudden moves or G-forces, barometric pressure difference, and use or not use. An IR-based heart rate sensor can measure the heart rate continuously, i.e. measured at high enough, yet adjustable, frequency suitable for monitoring health related heart rate deviations... A skin temperature may be measured continuously by e.g. IR and may be analysed either alone or in combination with other above mentioned sensor readings. An unwanted skin temperature or a skin temperature in combination with other sensor readings may trigger alarms... An IR-sensor can detect if a watch is removed from the wrist and may trigger an alarm visible for care takers/dependants or for the user, reminding him/her to put the watch back on.).
Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, and Shaw to include the step of identifying the trigger condition based at least in part on the additional heart rate data comprising an indication that the user is not wearing the wearable device, as taught by Tobiassen (Paragraph [0085]), in order to provide a user with the possibility to act before a fall occurs (Paragraph [0014] of Tobiassen).
Claims 15 – 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen as modified by Robinson, Tran ‘064, and Shaw and applied to claim 10 above, and further in view of Zhao (U.S. Pub. No. 2016/0113569 A1).
Regarding claim 15, Kinnunen as modified by Robinson, Tran ‘064, and Shaw and applied to claim 10 does not appear to explicitly disclose, but Zhao teaches the limitations in bold as:
generating a baseline heart rate curve associated with the plurality of sleep days based at least in part on the baseline heart rate data; and
generating a heart rate curve associated with the first sleep day based at least in part on the additional heart rate data,
wherein identifying the trigger condition is based at least in part on a comparison of the heart rate curve and the baseline heart rate curve.
Zhao teaches that it was old and well known in the art of medical data processing at the time of the filing to implement generating the baseline heart rate curve, generating the heart rate curve, and identifying the trigger condition based on a comparison between the baseline heart rate curve and the heart rate curve (Paragraph [0044] of Zhao, Referring now to FIGS. 4a and 4b, the method 100 then proceeds to block 108 where a first user health deviation is detected. Similarly as discussed above, at block 108 the health issue detection module or detection circuit in user device and/or the system provider device compares the current user health data to the first user health profile in the database. FIG. 4a illustrates a first user health profile that includes a curve indicated by the previous user health data 302 (indicated by the dotted line) that was retrieved at previous times and analyzed to provide the first user health profile, along with current user health data 400 (indicated by the solid line) that was retrieved at a current time. As can be seen, the current user health data 400 differs from the first user health profile provided by the previous user health data (e.g., the spike 302a is not present in the current user health data 400), and such differences may be detected by the health issue detection module or detection circuit in the user device and/or the system provider device as a first user health deviation.
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Using the example provided above where the Y-axis 306 measures a user's heart rate and the X-axis measures time throughout the day on Mondays, the current user health data 400 may be indicative that the first user 200 has skipped exercising on a Monday. However, the comparison of similar current user health data and first user health profiles may be indicative of an irregular heartbeat for the first user 200, an irregular breathing pattern for the first user 200, an irregular brainwave pattern for the first user 200 (e.g., due to the first user 200 not sleeping), an irregular temperature for the first user 200, irregular movement for the for the first user 200, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, and Shaw to further include the step of generating the baseline heart rate curve, generating the heart rate curve, and identifying the trigger condition based on a comparison between the baseline heart rate curve and the heart rate curve, as taught by Zhao (Paragraph [0044], FIG. 4a), in order to provide for the early detection of possible negative health issues of a user without the need to visit a doctor and/or obtain specialized monitoring equipment, along with the suggestion of preventative health treatments that may prevent those negative health issues from getting worse (Paragraph [0023] of Zhao).
Regarding claim 16, Kinnunen as modified by Robinson, Tran ‘064, and Shaw and applied to claim 10 does not appear to explicitly disclose, but Zhao teaches the limitations in bold as:
generating a baseline heart rate curve based at least in part on a default heart rate curve, physiological data acquired from a plurality of additional users different from the user, or both; and
generating a heart rate curve associated with the first sleep day based at least in part on the additional heart rate data,
wherein identifying the trigger condition is based at least in part on a comparison of the heart rate curve and the baseline heart rate curve.
Zhao teaches that it was old and well known in the art of medical data processing at the time of the filing to implement generating the baseline heart rate curve, generating the heart rate curve, and identifying the trigger condition based on a comparison between the baseline heart rate curve and the heart rate curve (Paragraph [0044] of Zhao, Referring now to FIGS. 4a and 4b, the method 100 then proceeds to block 108 where a first user health deviation is detected. Similarly as discussed above, at block 108 the health issue detection module or detection circuit in user device and/or the system provider device compares the current user health data to the first user health profile in the database. FIG. 4a illustrates a first user health profile that includes a curve indicated by the previous user health data 302 (indicated by the dotted line) that was retrieved at previous times and analyzed to provide the first user health profile, along with current user health data 400 (indicated by the solid line) that was retrieved at a current time. As can be seen, the current user health data 400 differs from the first user health profile provided by the previous user health data (e.g., the spike 302a is not present in the current user health data 400), and such differences may be detected by the health issue detection module or detection circuit in the user device and/or the system provider device as a first user health deviation.
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Using the example provided above where the Y-axis 306 measures a user's heart rate and the X-axis measures time throughout the day on Mondays, the current user health data 400 may be indicative that the first user 200 has skipped exercising on a Monday. However, the comparison of similar current user health data and first user health profiles may be indicative of an irregular heartbeat for the first user 200, an irregular breathing pattern for the first user 200, an irregular brainwave pattern for the first user 200 (e.g., due to the first user 200 not sleeping), an irregular temperature for the first user 200, irregular movement for the for the first user 200, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, and Shaw to further include the step of generating the baseline heart rate curve, generating the heart rate curve, and identifying the trigger condition based on a comparison between the baseline heart rate curve and the heart rate curve, as taught by Zhao (Paragraph [0044], FIG. 4a), in order to provide for the early detection of possible negative health issues of a user without the need to visit a doctor and/or obtain specialized monitoring equipment, along with the suggestion of preventative health treatments that may prevent those negative health issues from getting worse (Paragraph [0023] of Zhao).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen as modified by Robinson, Tran ‘064, and Shaw and applied to claim 10 above, in view of White (U.S. Pub. No. 2020/0390339 A1).
Regarding claim 19, Robinson as modified by Breslow, Tran ‘064, and Shaw does not appear to explicitly disclose, but White teaches the limitations in bold as determining a bedtime for the user based at least in part on the baseline heart rate data, wherein causing the graphical user interface to display the health-related insight is based at least in part on the bedtime (Paragraph [0008] of White, Using the monitoring device, a person is monitored over a prolonged period of time that extends across multiple days. The biometric data collected is used to determine a statistical profile for the collected data. This statistical profile can be computed using any number of methods, one such method being the arithmetic mean of the data. This statistical profile can be computed on measured biometric data such as respiration rate (RPM) or heartrate (BPM), as well as derived data such as bedtime, wake time, sleep quality, pattern of sleep cycles, pattern of nightly movement, or other measured or derived data. After the statistical profile for the biometric data is calculated, the person can be actively monitored as he/she sleeps. The monitoring occurs during a sample period of sleep that lasts a predetermined period of time. Statistical analysis is applied to the biometric data collected during the sample period to obtain a statistical value. The statistical value can be computed using a method similar to that used in determining the statistical profile, such as use of the arithmetic mean. The statistical value is compared to the statistical profile of the biometric data. If the statistics computed on the sampled biometric data exceeds the statistical profile by a certain predetermined threshold amount, a warning is produced on the electronic device. The warning informs an observer that the person being monitored has a respiration rate and/or heartrate that is abnormal and may be indicative of an illness.).
Therefore, it would have been obvious to one of ordinary skill in the art of wearable digital health technologies (DHT) at the time of the filing to modify the method of Kinnunen as modified by Robinson, Tran ‘064, and Shaw to include the step of determining a bedtime for the user based at least in part on the baseline heart rate data, wherein causing the graphical user interface to display the health-related insight is based at least in part on the bedtime, as taught by White (Paragraph [0008]), in order to provide a biomonitoring system that can monitor a person by detecting the slight movements of breathing and/or heartbeats and then analyze that data for other purposes, such as determining if a person has some adverse medical condition (Paragraph [0006] of White).
Response to Argument
Applicant's arguments filed January 20, 2026 regarding claims 1 – 20 being rejected under 35 USC §101 have been fully considered and are persuasive. The rejection of claims 1 – 20 under 35 USC §101 has been withdrawn.
Applicant's arguments filed January 20, 2026 regarding claims 1 – 20 being rejected under 35 USC §103 have been fully considered but they are moot in view of the new grounds of rejection as necessitated by the amendment to claims 1, 10, and 20.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT CAESAR ILAGAN whose telephone number is (703) 756-1639. The examiner can normally be reached Monday – Friday, 8:30am – 6:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason B. Dunham, can be reached on (571) 272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/V.C.I./Examiner, Art Unit 3686
/DEVIN C HEIN/Examiner, Art Unit 3686
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