DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 07/01/2026.
Claims 1-2, 5, 7-9, 14-15 and 17 have been amended.
Claims 3 and 19 have been cancelled.
Claim 20 has been newly added.
Claims 1-12, 14-18 and 20 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 1-12 and 14-18 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-12 and 14-18 are directed to a system (i.e., a machine). Accordingly, claims 1-12 and 14-18 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 1 includes limitations that recite an abstract idea. Note that independent claim 1 is a method claim.
Specifically, independent claim 1 recites:
A system for generating personalized health-related predictions from measured physiological data, comprising:
a wearable ring device comprising:
a housing having an inner curved surface and an outer curved surface, wherein at least a portion of the inner curved surface is configured to contact a skin surface of a finger of a user;
one or more sensors arranged on the inner curved surface of the housing and configured to interface with the skin surface of the user to acquire physiological data from the user, wherein the one or more sensors comprise one or more temperature sensors and one or more photoplethysmogram (PPG) sensors;
a curved battery disposed at least partially within the housing, wherein the curved battery is electrically coupled with the one or more temperature sensors and the one or more PPG sensors; and
a communication module electrically coupled with one or more processors, the communication module configured to transmit the physiological data;
a user device communicatively coupled with the wearable ring device via the communication module; and
the one or more processors communicatively coupled with the wearable ring device and the user device, wherein the one or more processors are configured to:
receive, from the wearable ring device, first physiological data acquired from the user via the wearable ring device throughout a first time interval;
predict one or more expected user actions to be engaged in by the user subsequent to the first time interval and prior to a second time interval, based at least in part on baseline physiological data acquired from the user prior to the first time interval;
input, into a machine learning model, the first physiological data and the one or more expected user actions;
output, via the machine learning model, one or more health-related predictions associated with the user during the second time interval, wherein the one or more health-related predictions comprise a predicted value of a health-related metric associated with the user during the second time interval, and wherein the one or more health-related predictions are based at least in part on the one or more expected user actions and one or more relationships between the health-related metric and at least the first physiological data;
generate a signal to cause a user interface of the user device associated with the wearable ring device to display, prior to the second time interval, information associated with the one or more health-related predictions and indicative of how the one or more expected user actions are predicted to impact the health-related metric during the second time interval;
receive, from the wearable ring device, second physiological data from the user via the wearable ring device throughout the second time interval;
calculate an actual value of the health-related metric during the second time interval based at least in part on the second physiological data;
validate the one or more relationships based at least in part on a comparison between the actual value of the health-related metric and the predicted value of the health- related metric; and
adjust one or more parameters of the machine learning model based at least in part on the validation of one or more relationships, wherein, to adjust the one or more parameters, the one or more processors are configured to:
input, into the machine learning model, the actual value of the health-related metric and the predicted value of the health-related metric; and
train the machine learning model to recognize a cause of any discrepancy between the actual value of the health-related metric and the predicted value of the health-related metric.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because generating personalized health-related predictions, measuring physiological data with characteristics of a lifestyle routine and adjusting parameters based on a comparison between the actual value of the health-related metric and the predicted value of the health-related metric are recommendations and healthcare prognosis services that a patient receives from a healthcare provider, which relate to managing human behavior/interactions between people. These limitations constitute (b) “a mental process” because predicting expected user actions to be engaged in by the user subsequent to the first time interval and prior to a second time interval, information associating with health-related predictions and predicting change in a health-related during a second time interval, predicting change in a health-related metric during the second time interval from a first predicted value to a second predicted value based on expected user actions engaged in by the user between the first time interval and the second time interval and recognizing a cause of any discrepancy between the actual value of the health-related metric and the predicted value of the health-related metric are observations/evaluations/analysis that can be performed in the human mind or with a pen and paper, especially when the second time interval occurs 24 hours after the first time interval. place Furthermore, these limitations constitute (c) “Mathematical Concepts” because using a machine learning model for outputting a predicted value of a health-related metric associated with the user during the second time interval based at least in part on the one or more expected user actions to be engaged in by the user between the first time interval and the second time interval and calculating an actual value of the health-related metric associated with the user during the second time interval based at least in part on the second physiological data are mathematical relationship using machine learning, predicted values and time intervals. The foregoing underlined limitations also relate to claim 1. Accordingly, the claim describes at least one abstract idea.
In relation to claims 3-4, 6, 11 and 17-18, these claims merely recite specific kinds of data, such as: claim 3 - the user input further indicates timing information associated with the one or more additional expected user actions, claim 4 - additional expected user actions comprise an expected bedtime, an expected wake time, an expected workout, an expected nap, an expected meal consumption, an expected caffeine consumption, an expected alcohol consumption, or any combination thereof, claim 6 - characteristics of the routine comprise a bedtime, a wake-time, a workout timing, a meditation timing, a workout type, a meal timing, a nap timing, a nap duration, or any combination thereof, claim 11 – recommended actions comprise a recommended modification to the one or more characteristics of the physical environment of the user during the second time interval, claim 17 - the user via the wearable device throughout the first time interval comprises data associated with a Sleep Score of the user, a Readiness Score of the user, or both and claim 18 - the data associated with a Sleep Score of the user comprises data associated with a circadian rhythm of the user.
In relation to claims 2, 5, 7-10, 12 and 15-16, these claims merely recite determining steps such as: claim 2 – outputting based at least in part on inputting the user input to the machine learning model, one or more modifications to the one or more health-related predictions and causing to display additional information associated with the one or more modifications to the one or more health- related predictions, claim 5 - determining one or more characteristics of a routine of the user based at least in part on the baseline physiological data, wherein the one or more expected user actions are based at least in part on the one or more characteristics of the routine of the user, claim 7 - causing to display the one or more expected user actions used to generate the one or more health-related predictions, claim 8 – outputting, from the machine learning model based at least in part on inputting the second physiological data to the machine learning model, one or more modifications to the one or more health-related predictions and causing to display additional information associated with the one or more modifications to the one or more health- related predictions, claim 9 - outputting based at least in part on inputting the one or more tags to the machine learning model, one or more modifications to the one or more health-related predictions and causing to display additional information associated with the one or more modifications to the one or more health- related predictions, claim 10 – determining recommended actions to preempt, adjust, or maintain the predicted change in the health-related metric prior to the second time interval and causing to display the recommended actions to preempt, adjust, or maintain the predicted change in the health-related metric prior to the second time interval, claim 12 - determining recommended actions to achieve the desired value of the health-related metric during the second time interval, wherein the one or more recommended actions are based at least in part on a difference between the second predicted value and the desired value and causing the user device associated with the wearable device to display the one or more recommended actions to achieve the desired value of the health-related metric during the second time interval, claim 15 - the predicted change in the health-related metric during the second time interval is based at least in part on timing information associated with the one or more expected user actions engaged in by the user between the first time interval and the second time interval and claim 16 - the machine learning model is trained based at least in part on data associated with the user, data associated with a set of users, or both.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claim 1, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the human mind but for the recitation of generic computer components. That is, other than reciting a user device and a user interface to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind, mathematically and interactively with humans. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care service done in the human mind and mathematically but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity”, “Mental Process” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the user device and user interface are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and outputting data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitation of “a wearable ring device comprising; a housing having an inner curved surface and an outer curved surface, wherein at least a portion of the inner curved surface is configured to contact a skin surface of a finger of a user; one or more sensors arranged on the inner curved surface of the housing and configured to interface with the skin surface of the user to measure physiological data from the user, the one or more sensors comprising one or more temperature sensors and one or more photoplethysmogram (PPG) sensors,” which the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)), the Examiner further submits that these limitations do no more than generally link use of the abstract idea to a particular field of use because they merely specify the type of input sources which does not alter or affect how the abstract idea is performed (see MPEP § 2106.05(e)).
Regarding the additional limitations “input, into a machine learning model, the first physiological data collected throughout the first time interval and one or more expected user actions to be performed by the user subsequent to the first time interval and prior to a second time interval”, the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation “receive, from the wearable ring device, first physiological data measured from the user via the wearable ring device” the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 1, regarding the additional limitations of the user device and user interface, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-12 and 14-18 are ineligible under 35 USC §101.
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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Tiensuu (US 2024/0385649 A1) in view of Sathaye (US 2023/0238113 A1).
Claim 1:
Tiensuu discloses a system for generating personalized health-related predictions from measured physiological data (See P0021, P0075-P0076 performing calculations and measured physiological parameters.), comprising:
a wearable ring device comprising (See Fig. 3 wearable ring device in P0095.);
a housing having an inner curved surface and an outer curved surface, wherein at least a portion of the inner curved surface is configured to contact a skin surface of a finger of a user (See Fig. 3, P0096, P0105 inner shell component 335 that would touch the skin surface of a finger.);
one or more sensors arranged on the inner curved surface of the housing and configured to interface with the skin surface of the user to measure physiological data from the user, the one or more sensors comprising one or more temperature sensors and one or more photoplethysmogram (PPG) sensors (See Fig. 2, measured physiological data such as temperature and glucose metrics measuring physiological data as in P0021, P0038.);
a curved battery disposed at least partially within the housing, the curved battery electrically coupled with the one or more temperature sensors and the one or more PPG sensors (See Fig. 3, P0097 battery 315 included in the wearable ring device assembly.); and
a communication module electrically coupled with one or more processors, the communication module configured to transmit the physiological data generated by the one or more processors (See communication module 220-a in Fig. 2 and P0040. Also, see exemplary processors in P0017 such as user devices 106 (e.g., smartphones, laptops, tablets) and P0037-P0039.);
a user device communicatively coupled with the wearable ring device via the communication module (See Fig. 2 where the user devices 106 (e.g., smartphones, laptops, tablets) communicate with the ring 104 mentioned in [P0037-P0039] the ring 104 may send measured and processed data (e.g., temperature data, photoplethysmogram (PPG) data, motion/accelerometer data, ring input data, and the like) to the user device 106.); and
the one or more processors communicatively coupled with the wearable ring device and the user device, wherein the one or more processors are configured (See Fig. 2, P0017, P0037-P0039.) to:
receive, from the wearable ring device, first physiological data measured from the user via the wearable ring device throughout a first time interval (See P0029 where periods of time that the user was asleep serve as a first time interval.);
input, into a machine learning model, the first physiological data collected throughout the first time interval and one or more expected user actions to be performed by the user subsequent to the first time interval and prior to a second time interval (See machine learning classifier in P0029 where classifying periods of time into different sleep stages, including an awake sleep stage, a rapid eye movement (REM) sleep stage, a light sleep stage (non-REM (NREM)), and a deep sleep stage (NREM) serve as second time intervals.);
output, via the machine learning model, one or more health-related predictions associated with the user during the second time interval, wherein the one or more health-related predictions comprise a predicted value of a health-related metric associated with the user during the second time interval (See P0029-P0030 where calculating scores for the respective user, such as Sleep Scores, Readiness Scores, and the like serve as a predicted value of a health-related metric associated with the user during the second time interval based on the expected user actions to be engaged in by the user between the first time interval and the second time interval. Also see a circadian rhythm adjustment model included in the machine learning classifier in P0030.), and wherein the one or more health-related predictions are based at least in part on the one or more expected user actions and one or more relationships between the health-related metric and at least the first physiological data (See P0029-P0030 exemplary temperature readings (P0060) as collected physiological data used for calculating the Sleep Scores (P0076).);
generate a signal to cause a user interface of the user device associated with the wearable ring device to display, prior to the second time interval, information associated with the one or more health-related predictions (Taught as server and a web-based interface to the user device 106 via web browsers mentioned in P0028-P0029 including periods of time into different sleep stages.);
receive, from the wearable ring device, second physiological data measured from the user throughout the second time interval (Besides tracking stages of sleep in P0029, see trends and tracking reediness score, heart rate variability and body temperature mentioned in P0087-P0088.);
calculate an actual value of the health-related metric associated with the user during the second time interval based at least in part on the second physiological data (Besides sleep metric scores in P0029, P0083 see activity metrics and motion counts in P0076.); and
adjust the one or more parameters of the machine learning model based at least in part on a comparison between the actual value of the health-related metric and the predicted value of the health-related metric, wherein the one or more parameters are associated with one or more relationships between the one or more expected user actions and the health-related metric (See modifiable adjustment models and parameters in [P0030-P0032] the circadian rhythm adjustment model may be configured to “weight,” or adjust, physiological data collected throughout a user's natural, approximately 24-hour circadian rhythm.).
Although Tiensuu discloses the system for generating personalized health-related predictions from measured physiological data for display mentioned above, Tiensuu does not explicitly teach predicting expected user actions to be engaged with the user subsequent to the first time interval and prior to a second time interval, based on baseline physiological data acquired from the user prior to the first time interval, input into a machine learning model, the first physiological data and the expected user actions and outputting health- related predictions associated with the user during the second time interval, where the health-related predictions comprise a predicted value of a health-related metric associated with the user during the second time interval. Sathaye teaches:
predict one or more expected user actions to be engaged in by the user subsequent to the first time interval and prior to a second time interval, based at least in part on baseline physiological data acquired from the user prior to the first time interval (See Figs. 4- 5, P0042, P0051, P0091-P0092 where pre-sleep parasomnia episode and P0106 pre-sleep target substance intake and medication intake serve as engaged actions having baseline physiological data acquired from the user prior to the first time interval.);
validate the one or more relationships based at least in part on a comparison between the actual value of the health-related metric and the predicted value of the health- related metric (Taught in Fig. 4 New in-sleep score satisfies thresholds 408 in P0099- P0104. See [P0056] when a set of dynamic deployment conditions are satisfied, where the set of dynamic deployment conditions may comprise a first condition requiring that a training data entry count of a training data entry set that is used to generate the dynamically-deployed parasomnia episode likelihood prediction machine learning model satisfies (e.g., exceeds) a threshold.); and
adjust one or more parameters of the machine learning model based at least in part on the validation of one or more relationships (See 406-407 in Fig. 4 when the New in-sleep score does not satisfies thresholds 408 mentioned in P0099-P0103.), wherein, to adjust the one or more parameters, the one or more processors are configured to:
input, into the machine learning model, the actual value of the health-related metric and the predicted value of the health-related metric (See predictive data analysis in P0098-P0099, [P0103-P0104] after performance of the recommended parasomnia reduction intervention satisfies (e.g., falls below) the in-sleep parasomnia episode likelihood score threshold. If so, the predictive data analysis computing entity 106 proceeds to, at step/operation 409, record this observation about effectiveness of the recommended parasomnia reduction intervention among the feedback data that may then be used to update/retrain/regenerate models.); and
train the machine learning model to recognize a cause of any discrepancy between the actual value of the health-related metric and the predicted value of the health-related metric (Taught in P0099-P0103, 406-407 in Fig. 4 when the New in-sleep score does not satisfies thresholds. Also, see parasomnia likelihood score 911 and dynamically deployed model 921 shown in Fig. 9, P0177-P0178.).
Therefore, it would have been obvious to one of ordinary skill in the art of machine learning for parasomnia management before the effective filing date of the claimed invention to modify the system of Tiensuu to include predicting expected user actions to be engaged with the user subsequent to the first time interval and prior to a second time interval, based on baseline physiological data acquired from the user prior to the first time interval, input into a machine learning model, the first physiological data and the expected user actions and outputting health- related predictions associated with the user during the second time interval, where the health-related predictions comprise a predicted value of a health-related metric associated with the user during the second time interval as taught by Sathaye to address efficiency and reliability shortcomings using statically developed values as mentioned in Sathaye’s P001, P0021.
Claims 2-12, 14-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tiensuu (US 2024/0385649 A1) in view of Sathaye (US 2023/0238113 A1) further in view of Youngblood (US 2022/0105308 A1).
Regarding claim 2, although Tiensuu and Sathaye teach the system of claim 1 wherein the one or more processors mentioned above, Tiensuu and Sathaye do not explicitly teach causing display additional information associated with the modifications to the health- related predictions. Youngblood teaches when processors are further configured to:
receive, via the user device, a user input indicating one or more additional expected user actions to be engaged by the user subsequent to the first time interval and prior to the second time interval; output, from the machine learning model based at least in part on inputting the user input into the machine learning model, one or more modifications to the one or more health-related predictions (Besides cognitive behavioral therapy (CBT) with artificial intelligence (AI) to help a user make incremental changes to improve sleep and health in P0187, see [P0172-P0176] The global analytics engine 754 generates predicted values for a monitored stress reduction and sleep promotion system using a virtual model of the stress reduction and sleep promotion system based on real-time data. The calibration engine 756 modifies and updates the virtual model based on the real-time data. Any operational parameter of the virtual model is able to be modified by the calibration engine 756 as long as the resulting modification is operable to be processed by the virtual model.); and
cause the user interface of the user device to display additional information associated with the one or more modifications to the one or more health- related predictions (See P0189 where machine learning is used to determine when to push notifications to display on the user’s device when a user is likely to be looking at their phone (e.g., before work, during lunch, after work).).
Therefore, it would have been obvious to one of ordinary skill in the art of stress reduction and sleep management before the effective filing date of the claimed invention to modify the system of Tiensuu and Sathaye to include causing display additional information associated with the modifications to the health- related predictions as taught by Youngblood to help individuals shield against outside interferences when know exercising, biofeedback and meditation have already been considered as mentioned in Youngblood’s P0003-P0005.
Regarding claim 3, although Tiensuu and Sathaye teach the system of claim 2 mentioned above, Youngblood further teaches wherein the user input further indicates timing information associated with the one or more additional expected user actions (Besides determining customized and optimized sleep settings for the user based on personal preferences (e.g., a target number of hours of sleep, a preferred bed time, a preferred wake time, a faster time to fall asleep, fewer awakenings during the sleeping period, more REM sleep, more deep sleep, and/or a higher sleep efficiency) in P0175, see P0227 lifestyle assessment questions include alarm clock usage for bedtime.).
Therefore, it would have been obvious to one of ordinary skill in the art of stress reduction and sleep management before the effective filing date of the claimed invention to modify the system of Tiensuu and Sathaye to include indicating timing information associated with the additional expected user actions as taught by Youngblood to help individuals shield against outside interferences when know exercising, biofeedback and meditation have already been considered as mentioned in Youngblood’s P0003-P0005.
Regarding claim 4, although Tiensuu and Sathaye teach the system of claim 2 mentioned above, Youngblood further teaches wherein the one or more additional expected user actions comprise an expected bedtime, an expected wake time, an expected workout, an expected nap, an expected meal consumption, an expected caffeine consumption, an expected alcohol consumption, or any combination thereof (See scheduled workout, meal, bedtime, wakeup time and not consuming caffeine during a time period in P0226, see sleep setting preferences in P0175 and challenge program as preferably predetermined period of time and not consuming alcohol in P0232.).
Therefore, it would have been obvious to one of ordinary skill in the art of stress reduction and sleep management before the effective filing date of the claimed invention to modify the system of Tiensuu and Sathaye to include additional expected user actions as taught by Youngblood to help individuals shield against outside interferences when know exercising, biofeedback and meditation have already been considered as mentioned in Youngblood’s P0003-P0005.
Regarding claim 5, although Tiensuu and Sathaye teach the system of claim 2 mentioned above, Youngblood further teaches wherein the one or more processors are further configured to: receive, from the wearable device, baseline physiological data acquired from the user via the wearable device prior to the first time interval; and determine one or more characteristics of a routine of the user based at least in part on the baseline physiological data, wherein the one or more expected user actions are based at least in part on the one or more characteristics of the routine of the user (Taught in P0141 as a wearable device include heart sensor that measures heart rate variability (HRV) in time intervals between heartbeats and high frequency peak patterns during deep sleep in P0151. Also, see P0248.).
Therefore, it would have been obvious to one of ordinary skill in the art of stress reduction and sleep management before the effective filing date of the claimed invention to modify the system of Tiensuu and Sathaye to include determining characteristics of a routine of the user based on a baseline physiological data and expected user actions are based the characteristics of the routine of the user as taught by Youngblood to help individuals shield against outside interferences when know exercising, biofeedback and meditation have already been considered as mentioned in Youngblood’s P0003-P0005.
Regarding claim 6, although Tiensuu, Sathaye and Youngblood teach the system of claim 5 mentioned above, Tiensuu further teaches wherein the one or more characteristics of the routine comprise a bedtime, a wake-time, a workout timing, a meditation timing, a workout type, a meal timing, a nap timing, a nap duration, or any combination thereof (See different steep stages in P0029-P0030, heart rate analysis correlation with naps in P0187-P0088.).
Regarding claim 7, although Tiensuu, Sathaye and Youngblood teach the system of claim 1 mentioned above, Youngblood further discloses wherein the one or more processors are further configured to: cause the user interface of the user device to display the one or more expected user actions (See Fig. 31-32, where management of Sleep, Stress, Fitness and Nutrition predict adding 50 or 65 seconds to the user’s lifespan as information associated with health-related predictions mentioned in P0203.).
Therefore, it would have been obvious to one of ordinary skill in the art of stress reduction and sleep management before the effective filing date of the claimed invention to modify the system of Tiensuu and Sathaye to include displaying expected user actions used to generate the one or more health-related predictions as taught by Youngblood to help individuals shield against outside interferences when know exercising, biofeedback and meditation have already been considered as mentioned in Youngblood’s P0003-P0005.
Regarding claim 8, Tiensuu and Sathaye teach the system of claim 1, and Tiensuu teaches wherein the one or more processors are further configured to: receive, from the wearable ring device, additional physiological data acquired from the user via the wearable ring device subsequent to the first time interval and prior to the second time interval (See Fig. 3 wearable ring device in P0095, P0029 where periods of time that the user was asleep serve as a first time interval.); output, from the machine learning model based at least in part on inputting the additional physiological data to the machine learning model, one or more modifications to the one or more health-related predictions (See modifiable adjustment models and parameters in [P0030-P0032] the circadian rhythm adjustment model may be configured to “weight,” or adjust, physiological data collected throughout a user's natural, approximately 24-hour circadian rhythm.); and cause the user interface of the user device to display additional information associated with the one or more modifications to the one or more health-related predictions (Taught as server and a web-based interface to the user device 106 via web browsers mentioned in P0028-P0029 including periods of time into different sleep stages.).
Regarding claim 9, Tiensuu and Sathaye teach the system of claim 1, and Tiensuu teaches wherein the one or more processors are further configured to: receive, via the user device, a user input indicating one or more tags associated with the user subsequent to the first time interval and prior to the second time interval (See exemplary processors in P0017 such as user devices 106 (e.g., smartphones, laptops, tablets) and P0037-P0039.); output, from the machine learning model based at least in part on inputting the one or more tags into the machine learning model, one or more modifications to the one or more health-related predictions (See modifiable adjustment models and parameters in [P0030-P0032] the circadian rhythm adjustment model may be configured to “weight,” or adjust, physiological data collected throughout a user's natural, approximately 24-hour circadian rhythm.); and cause the user interface of the user device to display additional information associated with the one or more modifications to the one or more health-related predictions (Taught as server and a web-based interface to the user device 106 via web browsers mentioned in P0028-P0029 including periods of time into different sleep stages.).
Regarding claim 10, Tiensuu and Sathaye teach the system of claim 1 wherein the one or more processors mentioned above, Tiensuu does not explicitly teach displaying recommended actions to preempt, adjust, or maintain the predicted change in the health-related metric prior to the second time interval. Youngblood teaches when the processors are further configured to: determine one or more recommended actions to preempt, adjust, or maintain a predicted change in the health-related metric prior to the second time interval (See P0177 recommendations of activities based on predictions by evaluating stress and sleep.); and cause the user interface of the user device associated with the wearable ring device to display the one or more recommended actions to preempt, adjust, or maintain the predicted change in the health-related metric prior to the second time interval (See Fig. 31-32, where management of Sleep, Stress, Fitness and Nutrition predict adding 50 or 65 seconds to the user’s lifespan as information associated with health-related predictions mentioned in P0203.).
Therefore, it would have been obvious to one of ordinary skill in the art of stress reduction and sleep management before the effective filing date of the claimed invention to modify the system of Tiensuu and Sathaye to include displaying recommended actions to preempt, adjust, or maintain the predicted change in the health-related metric prior to the second time interval as taught by Youngblood to help individuals shield against outside interferences when know exercising, biofeedback and meditation have already been considered as mentioned in Youngblood’s P0003-P0005.
Regarding claim 11, although Tiensuu, Sathaye and Youngblood teach the system of claim 10 mentioned above, Tiensuu further teaches wherein the one or more processors are further configured to: receive sensor data from the wearable ring device, the user device, or both, the sensor data associated with one or more characteristics of a physical environment of the user during the first time interval, wherein the one or more recommended actions comprise a recommended modification to the one or more characteristics of the physical environment of the user during the second time interval (See feedback to use in P0029 as recommended bedtimes and wake-up times.).
Regarding claim 12, although Tiensuu and Sathaye teach the system of claim 1, wherein the one or more processors mentioned above, Tiensuu and Sathaye do not explicitly teach determining recommended actions to achieve the desired value of the health-related metric during the second time interval and causing the user interface of the user device associated with the wearable device to display the recommended actions to achieve the desired value of the health-related metric during the second time interval.
Youngblood teaches when the processors are further configured to: receive, via the user device, a user input indicating a desired value of the health- related metric during the second time interval (Taught in P0177 as optimized vales and predictive values.); determine one or more recommended actions to achieve the desired value of the health-related metric during the second time interval, wherein the one or more recommended actions are based at least in part on a difference between the predicted value and the desired value (Taught in P0177 as threshold difference between the optimized vales and predictive values.); and cause the user interface of the user device associated with the wearable ring device to display the one or more recommended actions to achieve the desired value of the health-related metric during the second time interval (Besides exemplary sleep score of the user in P0032, P0127, see [P0176] Based on the data from the body sensors and the environmental sensors, the virtual model generates predicted values for the stress reduction and sleep promotion system. A sleep stage (e.g., awake, Stage N1, Stage N2, Stage N3, REM sleep) for the user is determined from the data from the body sensors.).
Therefore, it would have been obvious to one of ordinary skill in the art of stress reduction and sleep management before the effective filing date of the claimed invention to modify the system of Tiensuu and Sathaye to include determining recommended actions to achieve the desired value of the health-related metric during the second time interval and causing the user interface of the user device associated with the wearable device to display the recommended actions to achieve the desired value of the health-related metric during the second time interval as taught by Youngblood to help individuals shield against outside interferences when know exercising, biofeedback and meditation have already been considered as mentioned in Youngblood’s P0003-P0005.
Regarding claim 14, although Tiensuu and Sathaye teach the system of claim 1 wherein the one or more processors mentioned above, Tiensuu and Sathaye do not explicitly teach associated applications comprising a lifestyle application, a social media application, a utility application, an entertainment application, a productivity application, an information outlet application, or any combination. Youngblood teaches when the processors are further configured to: receive, prior to the second time interval, user data associated with the user from one or more applications associated with the wearable ring device, the user device, or both, the one or more applications comprising a lifestyle application, a social media application, a utility application, an entertainment application, a productivity application, an information outlet application, or any combination thereof; and input the user data into the machine learning model, wherein the one or more health-related predictions associated with the user during the second time interval are based at least in part on inputting the user data into the machine learning model (See P0186 where the wearable watch, nutrition trackers and GPS construe a utility application to reach goals pertaining to calorie and weight.); and inputting the user data into the machine learning model, wherein the one or more health-related predictions associated with the user during the second time interval are based at least in part on inputting the user data to the machine learning model (See P0187 cognitive behavioral therapy (CBT) with artificial intelligence (AI) to help a user make incremental changes to improve sleep and health based on social interaction.).
Therefore, it would have been obvious to one of ordinary skill in the art of stress reduction and sleep management before the effective filing date of the claimed invention to modify the system of Tiensuu and Sathaye to include associated applications comprising a lifestyle application, a social media application, a utility application, an entertainment application, a productivity application, an information outlet application, or any combination as taught by Youngblood to help individuals shield against outside interferences when know exercising, biofeedback and meditation have already been considered as mentioned in Youngblood’s P0003-P0005.
Regarding claim 15, although Tiensuu and Sathaye teach the system of claim 1 mentioned above, Tiensuu and Sathaye do not explicitly teach predicted change in the health-related metric during the second time interval is based on timing information associated with expected user actions engaged in by the user between the first time interval and the second time interval. Youngblood teaches wherein a predicted change in the health-related metric during the second time interval is based at least in part on timing information associated with the one or more expected user actions (Besides chatbot interactions in P0186, [P0199-P0200] The chatbot is operable to provide a suggestion based on the user's response. For example, if the user selects “no place to do it”, the chatbot provides suggestions of gyms and/or free recreational facilities near the user's work or home. As the mobile application learns more about a user's preferences and health, the mobile application is able to use machine learning (e.g., via the reasoning engine) to make better predictions about what is helpful to the user.).
Therefore, it would have been obvious to one of ordinary skill in the art of stress reduction and sleep management before the effective filing date of the claimed invention to modify the system of Tiensuu and Sathaye to include the predicted change in the health-related metric during the second time interval is based on timing information associated with expected user actions engaged in by the user between the first time interval and the second time interval as taught by Youngblood to help classify a user into a group based on a user profile while reporting the user’s sleep as mentioned in Youngblood’s P0034.
Regarding claim 16, Tiensuu discloses the method of claim 1, wherein the machine learning model is trained based at least in part on data associated with the user, data associated with a set of users, or both (See P0188-P0190 machine learning identifying habits and behaviors.).
Regarding claim 17, Tiensuu discloses the method of claim 1, wherein the first physiological data acquired from the user via the wearable ring device throughout the first time interval comprises data associated with a Sleep Score of the user, a Readiness Score of the user, or both (See user sleep score and readiness score in P0029, P0076.).
Regarding claim 18, Tiensuu discloses the method of claim 17, wherein the data associated with a Sleep Score of the user comprises data associated with a circadian rhythm of the user (See user sleep score associated with a circadian rhythm adjustment model included in the machine learning classifier mentioned in P0030.).
Regarding claim 20, although Tiensuu and Sathaye teach the system of claim 1, wherein, for each of the first physiological data and the second physiological data, the acquisition of the respective physiological data comprises sampling from PPG sensors mentioned above, Tiensuu further teaches:
sampling a respective PPG signal generated by at least one optical receiver of the one or more PPG sensors, the respective PPG signal indicative of a respective amount of light received by the at least one optical receiver, and the respective amount of light transmitted from at least one optical transmitter of the one or more PPG sensors (See P0056 PPG system include optical receivers.); and
determining a respective pulse waveform of the user based on the respective PPG signal, wherein a portion of the respective physiological data is based at least in part on the respective pulse waveform (See P0021, P0038 wearable device measure pulse waveform.).
Response to Arguments
Applicant argues that the claims do not recite a judicial exception under step 2A, Prong 1, according to the USPTO issued memorandum titled Reminders on evaluating subject matter eligibility of claims under U.S.C. 101. See pgs. 10-12 of Remarks – Examiner disagrees.
Even if the claimed invention uses a AI to, “input, into a machine learning model, the first physiological data and the one or more expected user actions; output, via the machine learning model, one or more health-related predictions associated with the user during the second time interval, wherein the one or more health-related predictions comprise a predicted value of a health-related metric associated with the user during the second time interval, and wherein the one or more health-related predictions are based at least in part on the one or more expected user actions and one or more relationships between the health-related metric and at least the first physiological data", "generate a signal to cause a user interface of the user device associated with the wearable ring device to display, prior to the second time interval, information associated with the one or more health-related predictions and indicative of how the one or more expected user actions are predicted to impact the health- related metric during the second time interval" and "adjust one or more parameters of the machine learning model based at least in part on the validation of one or more relationships, wherein, to adjust the one or more parameters, the one or more processors are configured to: input, into the machine learning model, the actual value of the health-related metric and the predicted value of the health-related metric; and train the machine learning model to recognize a cause of any discrepancy between the actual value of the health-related metric and the predicted value of the health-related metric” recited in claim 1, these are mostly addressed under the Mental Processes grouping, according to Prong One’s groupings of Abstract Ideas, Certain Methods of Organizing Human Activity and Mathematical Concepts also apply to the instant case as having mathematical relationships. For instance, a human predicting a sleep score could observe reported minutes asleep verses time awake tossing and turning, further formulates a ratio of the minutes asleep verses time awake.
Applicant argues that the claims integrate any judicial exception into a practical application, and satisfies the analysis of Step 2A, Prong Two, in Light of Ex Parte Desjardins. See pgs. 12-16 of Remarks – Examiner disagrees.
With the health-related metric as a sleep score utilizing acquired physiological data, which is broadly claimed, recognizing a discrepancy between the predicted and actual sleep score doesn’t need machine learning to predict. For example, whatever physiological data acquired is manipulated according to time interval and baseline thresholds, which is basic data processing and not training metadata associated with a machine learning process. The claimed invention is directed to an abstract idea without significantly more.
Applicant argues that the claims recite an inventive concept under step 2B. See pgs. 16-17 of Remarks – Examiner disagrees.
The forward-looking health predictions, regarding a user’s physiological data in the future that Applicant is talking about are features not claimed with a technology to implement them. For example, technologies such as artificial intelligence or sensory recognition software could determine vital data pertaining to hypnotical suggestions, emotional states (e.g. relaxation, stress, anxiety), but the technologies are neither claimed nor supported by the Applicant’s specification. The claimed invention is directed to an abstract idea without significantly more. Furthermore, as claimed, the wearable ring is just a sensor device, which is an additional element. Regardless of the ring curved surface design and coupled battery of the wearable ring, it is merely an additional element and not a particular machine. The steps of independent claim 1 that “receive..., input..., output... generate..., calculate..., adjust...” steps starting “receive..., input..., output... generate..., calculate..., adjust...” cited, are steps using a computer to perform the abstract idea.
Applicant’s arguments have been fully considered, but are now moot in view of the new grounds of rejection. The Examiner has entered a new rejection under 35 USC § 103 and applied new art and art already of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Addison (US 2013/0172686 A1) and Bandyopadhyay (US 10,321,871 B2).
THIS ACTION IS MADE FINAL. 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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/T.S.W./Examiner, Art Unit 3687 00/03/206
/Anita Y Coupe/Supervisory Patent Examiner, Art Unit 3619
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