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 .
Election/Restrictions
Applicant’s election without traverse of Group I, claims 1-13, in the reply filed on Sept 08, 2026 is acknowledged.
Claim Status
Claims 1-13 are pending for examination.
Claims 14-20 are withdrawn without traverse.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 3-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Alder (Pub. No.: US 2024/0006052 A1).
Regarding claim 1, Alder teaches a method for predicting disruptive behavior in a subject (abstract, a system and method for predicting mental health deterioration, behavior and/or stress of a user), the method comprising:
receiving by a processor, measurement data recorded with a
wearable device worn by a subject, wherein the measurement data comprise motion data, heart rate data, and sleep data (Fig. 1, para [0111], “As indicated above, participants were mailed a Fitbit Charge 2. The Fitbit device continuously tracked minute-by-minute step counts, heart rate, recorded whether a participant was sleeping, and the type of sleep.”);
accessing a machine learning model with the processor (Fig. 1,
generative adversarial network (GAN) 110), wherein the machine learning model has been trained on training data to monitor signals contained in the measurement data to detect signals that indicate a likelihood of a disruptive behavior for an individual (Para [0119] – [0121], “FIGS. 2A and 2B show respective training and testing processes utilized in the example prediction system. Such a system and its associated training and testing processes are illustratively implemented in the processing platform 102 of FIG. 1, or in other arrangements of one or more processing devices. FIG. 2A more specifically illustrates a training process 200 having steps (i) through (iv) as shown, and FIG. 2B more specifically illustrates a testing process 210 showing how one or more trained models from the training process 200 are deployed to generate predictions in the testing context.”. The GAN is trained based on previous samples.), wherein the machine learning model is a tree-based model, wherein the tree-based model is a decision tree model or a random forest model (para [0136], “Outliers were filtered using an Isolation Forest algorithm. Isolation forests recursively partition data through randomly selected features. A set of partitions can be described as a path to a set of samples, and samples that are partitioned by shorter paths are classified as outliers. We created an Isolation Forest using the scikit-learn library, with 250 trees, and randomly partitioned samples into each tree.”. The GAN uses decision tree model to predict behavior);
applying the measurement data to the machine learning model
with the processor, generating predictive feature data that predict a likelihood of a disruptive behavior in the subject based on the measurement data (Fig. 1, para [0065]- [0071], “Clinician A has a phone consultation with a patient that mentions they are worried about starting school in a few months. Clinician A, with permission from the patient, instructs the patient to install an application to collect data from the patient's smartphone, with that data being input into the system 100. The patient securely sends Clinician A their data after two months, and Clinician A then directs the input of this data into the system 100, which predicts that the patient might experience an increase in their average hourly heart rate when they start school, and shows that an increased average hourly heart rate is associated with an increase in depression and anxiety symptoms. With automated assistance from the system 100, the clinician follows up with the patient, and discusses possible mindfulness treatments to relax heart rate prior to the patient beginning school.”. The processing platform 102 receives the data sources 105 from a patient and inputs the data source to the GAN 11 to predict behavioral deterioration or stress.);
outputting the predictive feature data with the processor,
wherein the predictive feature data indicate predictive scores or probabilities of different behavioral phenotypes (Figs. 7 - 8, and para [0075], “Illustrative embodiments utilize GANs to predict indicators of resilience using data collected from smartphone and wearable sensors.” para [0208], “FIG. 7 summarizes indicators used in illustrative embodiments herein. In this example, there are 37 different indicators. Values are either specific to the period before (BL), during (INTERN) the internship, or captured a difference in a specific metric between the INTERN and BL periods (Cohen's d).” and para [0216], “FIG. 8 shows the depression symptom change (ΔPHQ-9) trajectories from the 4-class quadratic GMM model. Each curve represents the change in depression symptom trajectory for the subset of the population within that class. Points represent the mean ΔPHQ-9 (change in depression symptoms) for that period and population subset represented by that trajectory, and error bars are a 95% confidence interval around the mean.”. The GAN outputs the indicators / scores of a patient’s behavioral change away from the baseline (BL) over a period of time).
Regarding claim 3, Alder teaches the method of claim 1, wherein the
measurement data further comprise a time of day (para [0127], “If a mood EMA was completed on a day, we filled the hours of that day with the EMA value, from the time the participant woke up from a previous sleep cycle, up to the time when the participant woke up following the next sleep cycle that was greater than two hours.”).
Regarding claim 4, Alder teaches the method of claim 1, wherein the motion data comprise duration of time measured in at least one of a plurality of activity levels (Fig. 7, para [0112], “The classic categorization uses the accelerometer to infer general sleep categories (e.g., asleep, restless),”. The Fitbit device measures the amount of time on bed by using motion signal obtained from the accelerometer.).
Regarding claim 5, Alder teaches the method of claim 4, wherein the plurality of activity levels comprises a sedentary activity level, an active activity level, and a highly active activity level (para [0112], “The classic categorization uses the accelerometer to infer general sleep categories (e.g., asleep, restless), and the newer stages categorization uses the accelerometer and heart rate monitor to infer sleep stages (e.g., deep, light, rapid eye movement).”. The deep sleep, light sleep or rapid eye movement while on bed.).
Regarding claim 6, Alder teaches the method of claim 1, wherein the heart rate data comprise at least one of an active heart rate or a resting heart rate (para [0112], “Lastly, Fitbit combines the accelerometer and heart rate information to infer when an individual is sleeping, by measuring when an individual has stopped moving for one hour, and then measuring changes in heart rate to infer the sleep stage.”).
Regarding claim 7, Alder teaches the method of claim 1, wherein the sleep data comprise at least one of duration of time measured in at least one of a plurality of sleep stages or a sequence of sleep stages (Fig. 7, the patient data includes the total amount of time the patient spent sleeping).
Regarding claim 8, Alder teaches the method of claim 7, wherein the plurality of sleep stages comprises a light sleep stage, a deep sleep stage, a rapid eye movement (REM) stage, and an awake stage (para [0112], “The classic categorization uses the accelerometer to infer general sleep categories (e.g., asleep, restless), and the newer stages categorization uses the accelerometer and heart rate monitor to infer sleep stages (e.g., deep, light, rapid eye movement).”. The deep sleep, light sleep or rapid eye movement while on bed.).
Regarding claim 9, Alder teaches the method of claim 1, further comprising generating a report with the processor and outputting the report to a user when the predictive feature data indicate that a disruptive behavior is predicted in the measurement data (para [0065], “Individual A is about to start a new job. The system 100 uses data collected off of Individual A's smartphone to predict that Individual A is likely going to experience a decrease in the amount of daily sleep after beginning the job, and shows that sleep decreases are linked to increased anxiety symptoms.”. The system reports to patient about a prediction of an increase of anxiety.).
Regarding claim 10, Alder teaches the method of claim 1, wherein the plurality of behavioral phenotypes comprises a calm phenotype, a playful phenotype, and a disruptive phenotype (para [0026], “The generated prediction is illustratively associated with one or more predicted changes in mental health of the given subject, so as to permit interpretation of the generated prediction in the context of the mental health of the given subject. Numerous other arrangements of features and generated predictions are possible. For example, in other embodiments, the changes predicted in the system 100 could involve any life event, whether stressful or non-stressful.”. Stress is considered as a disruptive phenotype because stressful trait of the patient disrupts normal life).
Regarding claim 11, Alder teaches the method of claim 1, wherein outputting the predictive feature data comprises generating an alert based on the predictive feature data and sending the alert via the processor to at least one of a parent of the subject, a guardian of the subject, or a health team member, wherein the alert comprises a text alert that indicates that a disruptive behavior is predicted for the subject based on the measurement data (para [0071], “The patient securely sends Clinician A their data after two months, and Clinician A then directs the input of this data into the system 100, which predicts that the patient might experience an increase in their average hourly heart rate when they start school, and shows that an increased average hourly heart rate is associated with an increase in depression and anxiety symptoms.”. The system alerts the clinician of the patient about a depression or anxiety symptoms.).
Regarding claim 12, Alder teaches the method of claim 1, further comprising accessing, with the processor, the training data;
training, using the training data, with the processor, the machine learning model to monitor signals contained in the measurement data to detect signals that indicate the likelihood of the disruptive behavior for the individual (para [0104], “We used a GAN to model changes in multivariate data, where not only the individual features within the multivariate data come from different distributions, but feature distributions collected from different participants can vary. Within classical machine learning, MTL is a popular method for training a single machine learning model to learn multiple objectives. Traditionally, MTL is viewed as a regularization technique that constrains multiple machine learning objectives by a shared set of model parameters. Yet, when the machine learning objectives are similar and data is scarce, MTL can be used to share meaningful information across multiple learning objectives. This approach to MTL has been applied to problems with features derived from sensor data, specifically to train separate objectives that relate to separate feature outcomes (e.g., predicting mood versus stress), or to train separate models for participants that followed different behavioral patterns within the same prediction task.”. The GAN model is trained from data collected from different participants).
Regarding claim 13, Alder teaches the method of claim 12, further comprising: retraining, with the processor, the machine learning model using data collected by another wearable device worn by another subject (para [0108], “Interns starting their residency at a participating site were eligible to enroll online. After consenting to the study, participants were mailed a Fitbit Charge 2 for passive behavioral and physiology tracking, and completed a baseline assessment via an installed smartphone study application 1-2 months prior to the commencement of the internship. In addition, the study application sent notifications to complete daily mood EMAs, and facilitated data transfer from the Fitbit to a secure storage platform. Participants were asked to participate in Fitbit tracking and complete daily EMAs beginning 1-2 months prior to their internship through the end of the internship (˜14 months total). Lastly, participants completed quarterly mental health and well-being assessments at internship months 3, 6, 9, and 12, to further gauge how they were adapting to their new work.”. each participant wears a Fitbit.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Alder (Pub. No.: US 2024/0006052 A1).
Regarding claim 2, Alder teaches a method of claim 1, wherein the heart rate data comprise a moving average of heart rate, wherein the moving average comprises a 60-minute moving average instead of a 15-minute moving average (para [0071], “The patient securely sends Clinician A their data after two months, and Clinician A then directs the input of this data into the system 100, which predicts that the patient might experience an increase in their average hourly heart rate when they start school, and shows that an increased average hourly heart rate is associated with an increase in depression and anxiety symptoms.”).
However, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the 60-minute moving average to a 15-minute moving average because it has held that where general conditions of a claim are discloses in the prior art, discovering the optimum or workable range involves only routine skill in the art. In re Aller, 105 USPQ 233.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wexler (Pub. No.: US 2021/0383925 A1) teaches a system and method to obtain new and historical data of a user to estimate the behavior of the user.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHEN Y WU whose telephone number is (571)272-5711. The examiner can normally be reached Monday-Friday, 10AM-6PM, EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Quan-Zhen Wang can be reached at 571-272-3114. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZHEN Y WU/Primary Examiner, Art Unit 2685
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