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 Claims
This Final Office Action is in response to the Amendment and Remarks filed 05/26/2026. Claims 1-2 are amended. Claims 1-2, 4-15, 21 and 22 are pending and considered herein.
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 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 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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4, 6-15 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0407684 A1 to Pho et al., hereinafter “Pho,” in view of U.S. 2024/0127078 A1 to Kadkhodaie Elyaderani et al., hereinafter “Kadkhodaie,” in view of U.S. 2016/0302671 A1 to Shariff et al., and further in view of U.S. 2015/0106020 A1 to Chung et al., hereinafter “Chung.”
Regarding claim 1, Pho discloses A system, comprising: a wearable ring device configured to acquire physiological data from a user (See Pho at least at Abstract; Paras. [0032]-[0034]; Figs. 1, 2, 12-19), the wearable ring device comprising: a ring-shaped 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 tissue of the finger of the user (See id. at least at Paras. [0034], [0057]-[0067] (“[T]he ring 104 may be configured to be worn around a user's finger, and may determine one or more user physiological parameters when worn around the user's finger […] The ring 104 may include a housing 205, which may include an inner housing 205-a and an outer housing 205-b. In some aspects, the housing 205 of the ring 104 may store or otherwise include various components of the ring.”); Figs. 1, 2, 12-19); one or more temperature sensors arranged along or within the inner curved surface of the ring-shaped housing (See id. at least at Paras. [0057]-[0067] (“Example measurements and determinations may include, but are not limited to, user skin temperature, pulse waveforms, respiratory rate, heart rate, HRV, blood oxygen levels, and the like.”); Figs. 1-2, 12-10); one or more photoplethysmogram (PPG) sensors arranged on the inner curved surface of the ring-shaped housing (See id. at least at Paras. [0057]-[0067] (“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 […] The inner housing 205-a may be transparent to light emitted by the PPG light emitting diodes (LEDs).”), [0082]-[0091]; Figs. 1-2) the one or more PPG sensors including at least one optical receiver and at least one optical transmitter (See id. at least at Paras. [0085]-[0089] (“The PPG system 235 may include one or more optical transmitters that transmit light. The PPG system 235 may also include one or more optical receivers that receive light transmitted by the one or more optical transmitters. An optical receiver may generate a signal (hereinafter “PPG” signal) that indicates an amount of light received by the optical receiver. The optical transmitters may illuminate a region of the user's finger.”); Figs. 1-2); a curved battery disposed at least partially within the ring-shaped housing (See id. at least at Para. [0074] (“The power source (e.g., battery 210 or capacitor) may have a curved geometry that matches the curve of the ring 104.”); Figs. 1-2), the curved battery electrically coupled with the one or more temperature sensors, and the one or more PPG sensors (See id. at least at Paras. [0073]-[0076] (“The one or more temperature sensors 240 may be electrically coupled to the processing module 230-a.”), [0082]-[0090]; Figs. 1-2); and a communication module, the communication module configured to transmit the physiological data (See id. at least at Paras. [0038]-[0040], [0057]-[0065] (“[T]he 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 […] The device electronics may include device modules (e.g., hardware/software), such as: a processing module 230-a, a memory 215, a communication module 220-a, a power module 225, and the like. The device electronics may also include one or more sensors. Example sensors may include one or more temperature sensors 240, a PPG sensor assembly (e.g., PPG system 235), and one or more motion sensors 245.”); Figs. 1-2; 12-19); a user device communicatively coupled to the wearable ring device (See id.); and one or more processors communicatively coupled with the wearable ring device, the user device, and the communication module (See id. at least at Paras. [0067]-[0073], [0082]-[0090]; Figs. 1-2, 12-19), the one or more processors configured to: acquire, via the wearable ring device during a first time interval, a first set of physiological data of the user (See id. at least at Paras. [0026]-[0029], [0046]-[0049] (“[T]echniques described herein may compare physiological data (and rhythm parameters thereof) collected over different time intervals (e.g., first/reference time interval, second/prediction time interval) to identify a satisfaction of deviation criteria.”); Figs. 1-2, 12-19) wherein the acquisition of the first set of physiological data comprises sampling a PPG signal generated by the at least one optical receiver and determining a pulse waveform of the user based on the PPG signal (See id. at least at Paras. [0085]-[0091] (“Sampling the PPG signal generated by the PPG system 235 may result in a pulse waveform, which may be referred to as a “PPG.” The pulse waveform may indicate blood pressure vs time for multiple cardiac cycles. The pulse waveform may include peaks that indicate cardiac cycles. Additionally, the pulse waveform may include respiratory induced variations that may be used to determine respiration rate. The processing module 230-a may store the pulse waveform in memory 215 in some implementations. The processing module 230-a may process the pulse waveform as it is generated and/or from memory 215 to determine user physiological parameters described herein.”); Figs. 1-3), wherein the PPG signal is indicative of an amount of light received by the at least one optical receiver wherein the light is transmitted from the at least one optical transmitter, and wherein the first set of physiological data is based at least in part on the pulse waveform (See id. at least at Paras. [0085]-[0091] (“An optical receiver may generate a signal (hereinafter “PPG” signal) that indicates an amount of light received by the optical receiver. The optical transmitters may illuminate a region of the user's finger. The PPG signal generated by the PPG system 235 may indicate the perfusion of blood in the illuminated region […] The processing module 230-a may sample the PPG signal and determine a user's pulse waveform based on the PPG signal. The processing module 230-a may determine a variety of physiological parameters based on the user's pulse waveform, such as a user's respiratory rate, heart rate, HRV, oxygen saturation, and other circulatory parameters.”); Figs. 1-3); generate a first physiological metric associated with the user based at least in part on the first set of physiological data (See id. at least at Paras. [0049]-[0052], [0085]-[0091] (“Sampling the PPG signal generated by the PPG system 235 may result in a pulse waveform, which may be referred to as a “PPG.” The pulse waveform may indicate blood pressure vs time for multiple cardiac cycles. The pulse waveform may include peaks that indicate cardiac cycles. Additionally, the pulse waveform may include respiratory induced variations that may be used to determine respiration rate. The processing module 230-a may store the pulse waveform in memory 215 in some implementations. The processing module 230-a may process the pulse waveform as it is generated and/or from memory 215 to determine user physiological parameters described herein.”), [0104], [0151]-[0152] (generate illness assessment scores); Figs. 1-2, 12-19); generate a set of feature vectors readable by at least one machine learning model, based at least in part on the first set of physiological data (See id. at least at Abstract; Paras. [0026]-[0029] (“[T]o detect a transition from a healthy state to an unhealthy state, the systems and methods of the present disclosure may utilize one or more classifiers (e.g., machine learning classifiers, algorithms, etc.).”), [0042]-[0044], [0048]-[0055] (predictive weighting, machine learning), [0110]-[0111]); predict, via the one or more processors using a first machine learning model, a future physiological metric associated with the user for a second time interval based at least in part on the set of feature vectors (See id. at least at Abstract; Paras. [0022], [0026]-[0029] (“[T]o detect a transition from a healthy state to an unhealthy state, the systems and methods of the present disclosure may utilize one or more classifiers (e.g., machine learning classifiers, algorithms, etc.).”), [0042]-[0044], [0048]-[0055] (predictive weighting, machine learning), [0110]-[0111], [0115]-[0118] (“[I]dentifying illness onset using physiological data indicative of nervous system responses (e.g., HRV, respiratory rate). In particular, techniques described herein may compare HRV data collected from a user throughout a first time interval (reference window) and a second time interval (prediction window), and may determine a satisfaction of deviation criteria based on changes in HRV data from the first time interval to the second time interval. The satisfaction of the deviation criteria may then be used to determine an “illness risk score,” “illness prediction metric,” or some other metric indicative of illness for the user, and may report the determined metrics to the user via the GUI 275 of the user device 106.”), [0149] (“[T]he GUI 275 may provide textual notifications to the user that indicate why there is a prediction that the user may transition to an unhealthy state in the near future.” [0119]-[0121] ( [T]he feature engineering pipeline may be configured to compute features for certain time windows (e.g., rolling 1-10 minute time windows), as well as compute statistics over time windows (e.g., median values over the course of a night), where the computed features and statistics may be fed into classifiers (e.g., machine learning classifiers) to perform the various illness detection techniques.”), [0146]-[0148] “[D]etermine whether one or more parameters of the user's recent (e.g., current) physiological data deviates from one or more of the corresponding healthy baseline values/ranges (e.g., identify a satisfaction of one or more deviation criteria). In some cases, identification of the satisfaction of deviation criteria may be performed by a machine learning classifier. A deviation from one or more of the healthy baseline values/ranges may indicate that a user may transition from the healthy state to an unhealthy state.”), [0280]; Figs. 1-3).
Pho may not specifically describe but Kadkhodaie teaches to generate, via the one or more processors using a second machine learning model separate from the first machine learning model, for each feature vector of the set of feature vectors, a weighting corresponding to the respective feature vector based at least in part on the set of feature vectors and the future physiological metric (See Kadkhodaie at least at Abstract; Paras.[0096]-[0103] (“The samples are transformed into another space based on the representative shapes, where each sample is represented by a vector. In one implementation, the shape that has the least error compared with a sample shape may be selected. In other implementations, a sample shape may be represented by a weighted sum of multiple representative shapes by using various optimization techniques with appropriate regularization. In such an implementation, the weights are the result of the transformation process and result in an N×M matrix [..] The consistency of samples may be a strong indicator of the usability of sensor data. In the example of FIG. 13, hypothetical samples are shown from a wearable device 101 that captures measurement samples representing the breathing of user 102. […] the size of the cluster may also indicate the quality of the set. For example, if the robust covariance of a set is close to zero, it means that the samples in the interval all have similar features and are thus “similar” to each other […] the quality scores of the samples in the data set are used as a weighting factor for determining quality detection algorithms. In some such implementations, samples with lower quality are treated as less “important” during the development phase when compared to samples of higher quality.”), [0110], [0123]-[0124], [0142] (“Computing device 106 may also generate a notification to user 102 identifying occurrences of breathing events over a period of time, ranking triggers (e.g., locations, one or more environmental parameters, user input, or the like), indicate a probability of a future occurrence of a breathing event, or the like.”), [0211]-[0214] (“The measurements-based models 704 work with the measurements/signals and labels/classes assigned to them to train a model. But in the test phase, no label exists for the signals and so the trained model 704 is used to assign labels to the signals. As an example of measurement-based models, machine learning classifiers can be used to classify the signals based on the features extracted from them and assign labels to them […] a linear combination of the results from dynamic-based and measurement-based models is used to determine a probability distribution that is weighted summed and for which the confidences (or training accuracies) are the weights.”), [0239], [0243]; Figs. 1, 10-15, 21-23, 27), wherein the corresponding weighting indicates an impact of the respective feature vector on the future physiological metric relative to one or more other feature vectors of the set of feature vectors (See id. at least at Paras. [0096]-[0103] (“Each sample is represented by a vector. In one implementation, the shape that has the least error compared with a sample shape may be selected. In other implementations, a sample shape may be represented by a weighted sum of multiple representative shapes by using various optimization techniques with appropriate regularization.” Samples and features are classified relative to other samples and features and the classifier is weighted), [0110]-[0112] (“[C]omparing the at least one output representative of the at least one respiratory related parameter to a respective baseline respiratory related parameter includes determining whether the at least one output representative of the at least one respiratory related parameter is within a respective threshold value of the respective baseline respiratory related parameter […] machine learning is used to determine relevant respiratory related parameters and to calculate weights to be associated with such respiratory related parameters when determining the occurrence of a breathing event, or the detection of a breathing state, trigger or trend.”), [0178] (weight of classifier), [0183]-[0185] (“The approach shown in FIG. 21 uses dynamic-based algorithms to capture the dynamics of time series and to be used to improve the accuracy of measurement-based models such as classifiers. The approach shown in FIG. 21 leverages both the information residing in the current time data, such as the signal shape and features (measurement-based model), and the information in the signal history and transitions between consecutive time intervals (dynamic-based model). Then it fuses these two sources of information together intelligently to build more accurate algorithms for detection and prediction of events based on time-series measurement samples of one or more parameters associated with the biological function being monitored by a user device 101.”), [0227]-[0237] (“There are various machine learning methods such as random forest, logistic regression, support vector machines, recurrent neural networks, that can be leveraged to train a model to detect if a sample corresponds to before/after usage class. One may also use different weighting for the samples. In one example approach, for example, system 100 assigning lower weights to samples that are further away from the rescue inhaler time stamp to reduce their impact on the detection of the event.”), [0239]-[0253]; Figs. 1-3, 10-13, 21); generate a signal to cause a graphical user interface (GUI) of the user device to output an indication of the actual physiological metric and an indication of one or more user-recognizable categories, from the plurality of user-recognizable categories, based at least in part on the actual physiological metric relative to the future physiological metric (See Kadkhodaie at least at Paras. [0008] (“The computing device may store these breathing events and display, via a user interface, the breathing events to the user.”), [0043], [0047]-[0052] (“[U]ser interface 108 may receive an indication of the occurrence of a breathing event from computing device 106 and display on a display of user interface 108 information representative of the occurrence of the breathing event to user 102. Similarly, user interface 108 includes one or more input components that receive tactile input, kinetic input, audio input, optical input, or the like from user 102 or another entity 108.”), [0131]-[0132], [0142] (“Computing device 106 may also generate a notification to user 102 identifying occurrences of breathing events over a period of time, ranking triggers (e.g., locations, one or more environmental parameters, user input, or the like), indicate a probability of a future occurrence of a breathing event, or the like.”), [0235]; Figs. 1-3, 10-15, 20-23); wherein the one or more user-recognizable categories are associated with one or more cumulative weightings that exceed a predetermined threshold (See id. at least at Paras. [0088]-[0089] (“An error calculated by error calculator 234 as a function of the comparison of the labels assigned by algorithm 200.K and the labels generated based on domain knowledge 230 is used to adjust the expected outlier ratio at adjust outlier ratio 236. The expected outlier ratio then becomes a mechanism for tuning one or more algorithms 200 based on a comparison of labeled data (the “ground truth”) to labels generated by algorithms 200.”), [0093]-[0103] (“A simple breath quality detection algorithm may, for instance, reject any breath, or any breathing time period, in which the breath parameters exceed the reasonable range […] [S]hapes extracted from measurement samples 240 are compared to representative shapes 246 and one or more representative shapes 246 are selected as representative of the extracted shapes […] [T]he quality scores of the samples in the data set are used as a weighting factor for determining quality detection algorithms.”), [0107]-[0112] (“The respective threshold values may be predetermined based on user input […] the respective threshold values may vary over time. For example, in response to user input, computing device 106 may adjust (e.g., modify or update) one or more baseline respiratory parameters at regular or irregular intervals, in response to user input, determined occurrences of breathing events, data received from modules […] the threshold value used to determine a breathing event may be adjusted according to one or more of a location of user 102 (e.g., whether user 102 is indoors or outdoors), environmental information (e.g., whether the user has been exposed to unusual environmental factors, such as extreme weather or air quality issues), activity of user 102 (e.g., whether the user is physically active or at rest), and breath event history.”), [0124]-[0132] (“[D]etecting module 164 discards from the aggregated feature vector, features that have minimal impact on the asthma severity score, or whose impact falls below a selected threshold. Detecting module 164 then assigns weights representing the contribution of each feature in the aggregated feature vector to the severity score and distributes the aggregated feature vector and its associated weights to each computing device 106.”), [0178], [0211]-[0213] (“The measurements-based models 704 work with the measurements/signals and labels/classes assigned to them to train a model. But in the test phase, no label exists for the signals and so the trained model 704 is used to assign labels to the signals. As an example of measurement-based models, machine learning classifiers can be used to classify the signals based on the features extracted from them and assign labels to them.”), [0219]-[0220], [0235]; Figs. 1-8, 12 (Determine error threshold) 14-19); and determine a difference between the actual physiological metric and the future physiological metric (See id. at least at Paras. [0067]-[0069], [0072]-[0076] (actual physiological metrics), [0097], [0216]-[0220] (physiological signals and metrics), [0226]-[0239]; Figs. 1-8, 14-20).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho to incorporate the teachings of Kadkhodaie and provide indicating changes from physiological metrics and weighted features in relative classifications. Kadkhodaie is directed to learning techniques for sensor devices and patients. Incorporating the learning techniques for sensor devices and patients in Kadkhodaie with the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of the predicting a health event and showing the indications for why the prediction was made.
The references may not specifically describe but Shariff teaches wherein the indication of the one or more user-recognizable categories comprises an explanation for a change from the first physiological metric to the actual physiological metric (See Shariff at least at Paras. [0047]-[0050] ([T]the classification module 212 may select the health state having a highest probability as compared to the other health states as the classification for a given set of results from the physiological data. The classification module 212 may also select the health state by other techniques. This classification may be passed on or reported to another module such as a notification module 216 […] the notification module 216 may cause a display on the mobile electronic device 104 and/or the display 112 on the wearable electronic device 102 […] such as textual descriptions such as “sick,” “healthy,” and “ambiguous,” or “possibly becoming sick.”), [0064]-[0065] (“The probabilistic classification model may use the training data to identify physiological data which is predictive of a patient later developing a fever. For the purposes of the probabilistic classification models, a fever may be classified as a core body temperature above a threshold temperature. In one implementation the threshold temperature for classifying a core body temperature as representing a “fever” is 100° F. (37.8° C.). Thus, for example, following training with an appropriate set of training data, the probabilistic classification model may generate a probability that physiological data other than core temperature (e.g., heart rate and or respiration rate) predicts a future increase in core temperature to a level that is classified as a “fever.”), [0106] (“[D]etecting a change in respiration rate and heart rate that is indicative of an increased likelihood of developing a fever.”), [0125]-[0126]; Figs. 2-7),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho and Kadkhodaie to incorporate the teachings of Shariff and provide metrics and thresholds. Shariff is directed to prediction of health status. Incorporating the prediction of health status and showing changes in heart rate and respiration as in Shariff with the learning techniques for sensor devices and patients in Kadkhodaie and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of the predicting a health event and showing the indications for why the prediction was made.
The references may not specifically describe but Chung teaches to update the first machine learning model based at least in part on the difference between the actual physiological metric and the future physiological metric exceeding a first threshold (See Chung at least at Abstract; Paras [0048]-[0049] (machine learning), [0061]-[0063] (“As in FIG. 7, by using a model that innately includes these characteristics integrated with a continuous scale 704 for feature readings, the algorithm for assessing health and predicting emergent conditions avoids discrete thresholds that may be updated based on discrete boundary crossings of other sensors or attributes […] There is often much biological variability in the physiological quantities measured to assess health or disease. While this variability is often present between patients, much of the variation is consistent within a patient and can be corrected for by use of empiric data from past measurements.”); Figs. 3-7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho, Kadkhodaie and Shariff to incorporate the teachings of Chung and provide updating the machine learning model based on physiological metrics exceeding a threshold. Chung is directed to health monitoring and emergent condition prediction. Incorporating the health monitoring and predictions as in Chung with the prediction of health status and showing changes in heart rate and respiration as in Shariff, the learning techniques for sensor devices and patients in Kadkhodaie and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of the predicting a health event and improve accuracy of the prediction.
Regarding claim 2, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Shariff further teaches wherein causing the user device to output the indication of the one or more user-recognizable categories is based at least in part on the difference between a first physiological metric and the future physiological metric exceeding a second threshold (See Shariff at least at Paras. [0044]-[0047] (“[D]iscriminant function analysis is classification—the act of distributing things into groups, classes or categories of the same type. Discriminant analysis works by creating one or more linear combinations of predictors, creating a new latent variable for each function. These functions are called discriminant functions. The number of functions possible is either Ng−1 where Ng=number of groups, or p (the number of predictors), whichever is smaller. The first function created maximizes the differences between groups on that function. The second function maximizes differences on that function, but also must not be correlated with the previous function […] Upon classifying the physiological data with one or more probabilistic classification model(s) 214 the classification module 212 may return probabilities that the physiological data belongs to one or more classes representing health states (e.g. healthy, ambiguous, or sick).” Clustered groups), [0061]-[0065] (first and second metrics and first and second thresholds), [0071]-[0072] (“If the physiological data does not vary from the baseline by more than a threshold amount, process 500 returns to 502 where additional physiological data is received. Thus, process 500 may continue in a loop until physiological data is received which varies from the baseline by more than a threshold amount. If the physiological data varies from the baseline by more than a threshold amount, process 500 proceeds to 508 […] At 508, the physiological data is provided to a probabilistic classification model that returns probabilities that the physiological data belongs to one or more classes representing different health states. [clustered groups] The probabilistic classification model may be a mixture model, a discriminant analysis model, or a discriminative model. The probabilistic classification model may be the probabilistic classification model 214 shown in FIG. 2. In an implementation, the classes representing health states may be healthy, ambiguous, and sick.”), Physiological data that is received varies from the first physiological metric and future physiological metric by a threshold amount and then determining a probability that the groups are clustered because the metrics show a stronger indication a person will have fever/become sick; Figs. 2-5; Claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho, Kadkhodaie and Shariff to incorporate the teachings of Chung and provide a machine learning model based on physiological metrics exceeding another threshold. Chung is directed to health monitoring and emergent condition prediction. Incorporating the health monitoring and predictions as in Chung with the prediction of health status and showing changes in heart rate and respiration as in Shariff, the learning techniques for sensor devices and patients in Kadkhodaie and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of the predicting a health event and improve accuracy of the prediction.
Regarding claim 4, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Shariff further teaches updating the first machine learning model based at least in part on the difference between the actual physiological metric and the future physiological metric (See Shariff at least at Abstract; Paras. [0016]-[0018], [0028], [0038]-[0046], [0054]-[0065], [0071]-[0072] (“Thus, process 500 may continue in a loop until physiological data is received which varies from the baseline by more than a threshold amount. If the physiological data varies from the baseline by more than a threshold amount, process 500 proceeds to 508 […] At 508, the physiological data is provided to a probabilistic classification model that returns probabilities that the physiological data belongs to one or more classes representing different health states. [clustered groups] The probabilistic classification model may be a mixture model, a discriminant analysis model, or a discriminative model. The probabilistic classification model may be the probabilistic classification model 214 shown in FIG. 2. In an implementation, the classes representing health states may be healthy, ambiguous, and sick.”), [0079]-[0087]; Figs. 1-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho, Kadkhodaie and Chung to incorporate the teachings of Shariff and provide updating machine learning models. Shariff is directed to predicting health status in a patient. Incorporating the prediction of health status and showing changes in heart rate and respiration as in Shariff with the health monitoring and predictions as in Chung, the learning techniques for sensor devices and patients in Kadkhodaie and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of the predicting a health event and improve accuracy of the prediction.
Regarding claim 6, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Shariff further teaches wherein the one or more user-recognizable categories comprises a greatest cumulative weighting of the plurality of user-recognizable categories (The current Specification at Para. [0013] states “[T]he system may identify a cumulative correlation value associated with each category and may identify one or more categories with the greatest cumulative correlation values, or a cumulative correlation value satisfying (e.g., exceeding) a threshold.” Shariff teaches this limitation. See Shariff at least at Paras. [0038]-[0042] (“[T]he system may cluster the features according to user- understandable categories, such as sleep, activity, tags, etc. […] The variance detection module 210 may determine if a given physiological datum varies from the corresponding baseline value by more than a threshold amount. The threshold amount may be a fixed amount (e.g., beats per minute for heart rate, 15 breaths per minute for respiration rate, etc.) or a variable amount that depends on the value of the baseline (e.g., 5%, 10%, 15%, 25%, etc. of the baseline). Thus, the variance detection module 210 may flag physiological data that is “abnormal” in that it differs from the baseline value for a given physiological data descriptor.” Deviation from the cumulative correlation value exceeding a threshold), [0065] (“In one implementation, someone who will not get a fever may be equated to the classification of “healthy,” someone who will get a fever in the future may be equated to the classification of “sick,” and someone who might get a fever in the future may be equated to the classification of “ambiguous.” Of course, classification into other groups is also possible such as, for example, a group that will develop a high fever (e.g., above 103° F. (39.4° C.)) and a group that will develop a low fever (e.g., between 100° F. and 103° F. (37-39.4° C.)). The second threshold length of time may represent some period of time in the near future or the idea of “soon.” In an implementation, the second threshold length of time is between about 12 hours and about 72 hours.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho, Kadkhodaie and Chung to incorporate the teachings of Shariff and provide cumulative weighting. Shariff is directed to predicting health status in a patient. Incorporating the prediction of health status and showing changes in heart rate and respiration as in Shariff with the health monitoring and predictions as in Chung, the learning techniques for sensor devices and patients in Kadkhodaie and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of the predicting a health event and improve accuracy of the prediction.
Regarding claim 7, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Shariff further teaches wherein the predetermined threshold is based at least in part on a deviation from a greatest cumulative weighting of the plurality of user-recognizable categories (See Shariff at least at Paras. [0038]-[0042] (“[T]he system may cluster the features according to user- understandable categories, such as sleep, activity, tags, etc. […] The variance detection module 210 may determine if a given physiological datum varies from the corresponding baseline value by more than a threshold amount. The threshold amount may be a fixed amount (e.g., beats per minute for heart rate, 15 breaths per minute for respiration rate, etc.) or a variable amount that depends on the value of the baseline (e.g., 5%, 10%, 15%, 25%, etc. of the baseline). Thus, the variance detection module 210 may flag physiological data that is “abnormal” in that it differs from the baseline value for a given physiological data descriptor.” Deviation from the cumulative correlation value exceeding a threshold or not exceeding a threshold.), [0065]; See also Khanna at least at Paras. [0032]-[0033] (“To further improve the performance of the machine learning model, in some embodiments, the system may perform inductive learning. Inductive learning may be performed by selecting an un-labeled feature vector from the first set of feature vectors, classifying the un-labeled feature vector using the machine learning model to get a model classified cluster with a confidence score [cumulative weighting], determining whether the confidence score is greater than a threshold, determining a distance of the un-labeled feature vector with respect to each labeled feature vector of the first set of feature vectors, when the confidence score is greater than the threshold, determining a statistically significant matching cluster of labeled feature vectors to which the un-labeled feature vector is closest based on the determined distance [deviation], determining whether the model classified cluster and the statistically matching cluster are the same.”), [0071]-[0075] (“The module 114 selects each feature vector from a set of feature vectors that have been labeled through the inductive learning, classifies the feature vector using the machine learning model to get a model classified cluster with a confidence score.”) Clustered groups with cumulative weighting and correlation values for respective feature vectors; Figs. 1, 2, 8)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho, Kadkhodaie and Chung to incorporate the teachings of Shariff and provide machine learning models and particular threshold deviations. Shariff is directed to predicting health status in a patient. Incorporating the prediction of health status and showing changes in heart rate and respiration as in Shariff with the health monitoring and predictions as in Chung, the learning techniques for sensor devices and patients in Kadkhodaie and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of the predicting a health event and improve accuracy of the prediction.
Regarding claim 8, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Pho further discloses inputting, into the first machine learning model, an age of the user, a sex of the user, a blood pressure of the user, a body mass index of the user, a skin tone of the user, a medical condition of the user, a physical state of the user, or any combination thereof, wherein predicting the future physiological metric is based at least in part on the age of the user, the sex of the user, the blood pressure of the user, the body mass index of the user, the skin tone of the user, the medical condition of the user, the physical state of the user, or any combination thereof (See Pho at least at Paras. [0118], [0153] (“[T]he system 200 may be configured to receive user inputs regarding detected/predicted illness in order to train classifiers (e.g., supervised learning for a machine learning classifier) and improve illness detection techniques. For example, the user device 106 may display an illness risk metric indicating a relative likelihood that the user will become ill. Subsequently, the user may input one or more user inputs, such as an onset of symptoms, a positive illness test, and the like. These user inputs may then be input into the classifier to train the classifier. In other words, the user inputs may be used to validate, or confirm, the determined illness risk metrics.”), (“[0185]-[0186] (“[T]he server 110 may select detection/prediction parameters (e.g., scoring features) based on other users that are similar (e.g., similar age, sex, underlying conditions, etc.).”), [0282]-[0284]); See also Shariff at least at Paras. [0023]-[0025], [0036]-[0038], [0054], [0075]).
Regarding claim 9, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Pho further discloses wherein the set of feature vectors are associated with sleep of the user, an activity the user engaged in, a readiness of the user, a menstrual cycle of the user, a tag input by the user, a health metric associated with the user, a characteristic of an environment associated with the user, or any combination thereof (See Pho at least at Paras. [0042]-[0045] (“[S]leep stage classification based on data collected by a wearable device. In particular, the system 100 detect periods of time during which a user 102 is asleep, and classify periods of time during which the user 102 is asleep into one or more sleep stages.”), [0054]-[0055] (menstrual cycle), [0104]-[0106], [0131]-[0141], [0216]-[0217] (environmental factors); Figs. 1-5); See also Shariff at least at Paras. [0014]-[0017], [0036]-[0038], [0044]-[0046], [0106]-[0117]).
Regarding claim 10, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Kadkhodaie further teaches wherein a value for each feature vector of a subset of the set of feature vectors is based at least in part on an average value of each feature vector of the subset over a period of time (See Kadkhodaie at least at Abstract; Paras.[0096]-[0103] (“The samples are transformed into another space based on the representative shapes, where each sample is represented by a vector. In one implementation, the shape that has the least error compared with a sample shape may be selected. In other implementations, a sample shape may be represented by a weighted sum of multiple representative shapes by using various optimization techniques with appropriate regularization. In such an implementation, the weights are the result of the transformation process and result in an N×M matrix [..] The consistency of samples may be a strong indicator of the usability of sensor data. In the example of FIG. 13, hypothetical samples are shown from a wearable device 101 that captures measurement samples representing the breathing of user 102. […] the size of the cluster may also indicate the quality of the set. For example, if the robust covariance of a set is close to zero, it means that the samples in the interval all have similar features and are thus “similar” to each other […] the quality scores of the samples in the data set are used as a weighting factor for determining quality detection algorithms. In some such implementations, samples with lower quality are treated as less “important” during the development phase when compared to samples of higher quality.”), [0110], [0123]-[0124], [0127], [0142] (“Computing device 106 may also generate a notification to user 102 identifying occurrences of breathing events over a period of time, ranking triggers (e.g., locations, one or more environmental parameters, user input, or the like), indicate a probability of a future occurrence of a breathing event, or the like.”), [0211]-[0214]; See also Shariff at least at Paras. [0067]-[0071]; Fig. 5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho, Shariff and Chung to incorporate the teachings of Kadkhodaie and provide features and averages and weighted sums. Kadkhodaie relates to learning techniques for patient sensor devices. Incorporating the learning techniques for sensor devices and patients as in Kadkhodaie with the prediction of health status and showing changes in heart rate and respiration as in Shariff, the health monitoring and predictions as in Chung and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of classifying samples and predicting a health event.
Regarding claim 11, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Shariff further teaches wherein the set of feature vectors includes one or more lagged features associated with a third set of physiological data collected during a third time interval prior to the first time interval (See Shariff at least at Paras. [0036], [0061]-[0065], [0067]-[0071], [0106]-[0117]; Figs. 1-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho, Kadkhodaie and Chung to incorporate the teachings of Shariff and provide machine learning models and particular threshold deviations. Shariff is directed to predicting health status in a patient. Incorporating the prediction of health status and showing changes in heart rate and respiration as in Shariff with the health monitoring and predictions as in Chung, the learning techniques for sensor devices and patients in Kadkhodaie and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of incorporating physiological data and predicting a health event.
Regarding claim 12, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Shariff further teaches wherein the plurality of user-recognizable categories comprise an activity-related group, a sleep-related group, a menstrual cycle-related group, a tag-related group, or any combination thereof (See Shariff at least at Paras. [0014]-[0017], [0036]-[0038], [0044]-[0046], [0106]-[0117]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho, Kadkhodaie and Chung to incorporate the teachings of Shariff and provide user-recognizable categories. Shariff is directed to predicting health status in a patient. Incorporating the prediction of health status and showing changes in heart rate and respiration as in Shariff with the health monitoring and predictions as in Chung, the learning techniques for sensor devices and patients in Kadkhodaie and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of incorporating physiological data and predicting a health event.
Regarding claim 13, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Shariff further teaches determining at least one trend over time based at least in part on the set of feature vectors and the future physiological metric; and updating the first machine learning model based at least in part on the at least one trend (See Shariff at least at Paras. [0033]-[0035], [0038]-[0042] (“The variance detection module 210 may determine if a given physiological datum [heart beat, respiratory rate, etc. are a trend over time] varies from the corresponding baseline value by more than a threshold amount […] that it differs from the baseline value for a given physiological data descriptor […] given a patient's heart rate and respiration rate a probabilistic classifier can determine a probability that the patient is healthy and a probability that the patient is sick […] Probabilistic classification includes supervised learning which is the machine learning task of inferring a function from labeled training data. The labeled training data may be the training data 116 from FIG. 1. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.” The models/classification can use new information input to the model and looks at trends of physiological data over time), [0063] (“The probabilistic classification model is created by supervised machine learning from a set of training data. The set of training data may be the training data 116 shown in FIG. 1. In an implementation the set of training data may include physiological data from a plurality of individuals within the patient who are classified as healthy and a plurality of individuals other than the patient who are classified as sick.”), [0065] (“Upon providing the plurality of time points of physiological data to the probabilistic classification model, the probabilistic classification model returns probabilities that the patient belongs to one of a number of different groups. In an implementation, the groups comprise two groups: a group that will develop a fever within a second threshold length of time and a group that will not develop a fever within the second threshold length of time.” Trends and thresholds), [0068]-[0073]; Claim 1; Figs. 1-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho, Kadkhodaie and Chung to incorporate the teachings of Shariff and provide machine learning models and particular threshold deviations. Shariff is directed to predicting health status in a patient. Incorporating the prediction of health status and showing changes in heart rate and respiration as in Shariff with the health monitoring and predictions as in Chung, the learning techniques for sensor devices and patients in Kadkhodaie and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of incorporating physiological data and predicting a health event.
Regarding claim 14, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Kadkhodaie further teaches wherein the first set of physiological data is associated with the first time interval further comprising: receiving, from the wearable device, a measured set of baseline physiological data associated with the user prior to the first time interval, wherein the set of feature vectors are based at least in part on the measured set of baseline physiological data (See Kadkhodaie at least at Paras. [0030], [0073]-[0077] (“[D]etecting module 164 uses one or more algorithms 200.1-200.k to determine if a time interval includes a usable breath. In some example approaches, detecting module 164 uses one or more algorithms 200.1-200.k to determine if one of the two or more breaths detected in a given time interval include a usable breath.”), [0106]-[0111] (“[C]omparing the at least one output representative of the at least one respiratory related parameter to a respective baseline respiratory related parameter includes determining whether the at least one output representative of the at least one respiratory related parameter is within a respective threshold value of the respective baseline respiratory related parameter.”), [0127]; Figs. 1-5, 11-19, 21; See also Shariff at least at Paras. [0015]-[0017], [0036], [0061]-[0065], [0067]-[0071], [0106]-[0117]; Figs. 1-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho, Shariff and Chung to incorporate the teachings of Kadkhodaie and provide features vectors and related data. Kadkhodaie relates to learning techniques for patient sensor devices. Incorporating the learning techniques for sensor devices and patients as in Kadkhodaie with the prediction of health status and showing changes in heart rate and respiration as in Shariff, the health monitoring and predictions as in Chung and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of classifying samples and predicting a health event.
Regarding claim 15, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Pho further discloses wherein the future physiological metric comprises at least a heart rate variability (See Pho at least at Paras. [0025], [0037], [0042], [0247]; Figs. 1-3; See also Shariff at least at Paras. [0015]-[0017], [0023]-[0024], [0036], [0042], [0059]-[0065], [0067]-[0071], [0106]-[0117]; Figs. 1-5).
Regarding claim 21, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Kadkhodaie further teaches wherein each user-recognizable category corresponds to one or more common characteristics of a respective group of feature vectors, the one or more common characteristics understandable by the user (See Kadkhodaie at least at Paras. [0106]-[0107] (weights, sub-groups of user-recognizable categories), [0117]-[0131] (feature vectors and aggregating and weights for categories a user can recognize), [0239]-[0253]; Figs. 1-8, 14-21).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho, Shariff and Chung to incorporate the teachings of Kadkhodaie and provide features vectors and related data. Kadkhodaie relates to learning techniques for patient sensor devices. Incorporating the learning techniques for sensor devices and patients as in Kadkhodaie with the prediction of health status and showing changes in heart rate and respiration as in Shariff, the health monitoring and predictions as in Chung and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of classifying samples and characteristics for a user to recognize.
Regarding claim 22, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1 and Kadkhodaie further teaches wherein each cumulative weighting indicates a cumulative impact of each respective user-recognizable category on the future physiological metric (See Kadkhodaie at least at Paras. [0106]-[0107] (weights, sub-groups of user-recognizable categories), [0117]-[0131] (feature vectors and aggregating and weights for categories a user could recognize), [0227]-[0237] (“There are various machine learning methods such as random forest, logistic regression, support vector machines, recurrent neural networks, that can be leveraged to train a model to detect if a sample corresponds to before/after usage class. One may also use different weighting for the samples. In one example approach, for example, system 100 assigning lower weights to samples that are further away from the rescue inhaler time stamp to reduce their impact on the detection of the event.”), [0239]-[0253]; Figs. 1-8, 14-21).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho, Shariff and Chung to incorporate the teachings of Kadkhodaie and provide weighting particular impacts on a predicted health event. Kadkhodaie relates to learning techniques for patient sensor devices. Incorporating the learning techniques for sensor devices and patients as in Kadkhodaie with the prediction of health status and showing changes in heart rate and respiration as in Shariff, the health monitoring and predictions as in Chung and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of classifying physiological samples and predicting a future health event.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Pho, in view of Kadkhodaie, in view of Shariff, in view of Chung and further in view of U.S. 2022/0083815 A1 to Khanna et al., hereinafter “Khanna.”
Regarding claim 5, Pho as modified by Kadkhodaie, Shariff and Chung discloses all the limitations of claim 1. The references may not specifically describe but Khanna teaches receiving, via the user device, one or more user inputs indicating one or more tags associated with the user (See Khanna at least at Para. [0051] (“The ground truth vector identification module 108 identifies ground truth representative vectors for labeling through oracle identification, for example, in which a user, automated process, or other means (e.g., a database lookup) can tag ground truth representative vectors or use other means for tagging. As one may appreciate, the system 102 uses a set of ground truth representative feature vectors for training the ML model.”), [0063]-[0064]). While Shariff further discloses wherein predicting the future physiological metric is based at least in part on a second set of feature vectors of the one or more user inputs (See Shariff at least at Paras. [0061]-[0065] (“Upon providing the plurality of time points of physiological data to the probabilistic classification model, the probabilistic classification model returns probabilities that the patient belongs to one of a number of different groups. In an implementation, the groups comprise two groups: a group that will develop a fever within a second threshold length of time and a group that will not develop a fever within the second threshold length of time.” (first and second set of feature vectors and first and second thresholds), [0071]-[0072] (“In an implementation, the classes representing health states may be healthy, ambiguous, and sick.”), Physiological data that is received [one or more user inputs] varies from the first physiological metric by a threshold and a future physiological metric is predicted; Figs. 2-5; Claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho and Kadkhodaie to incorporate the teachings of Khanna and provide user inputs indicating tags. Khanna is directed to vector labeling for machine learning. Incorporating the feature vectors, clusters and tagging as in Khanna with the learning techniques for sensor devices and patients as in Kadkhodaie with the prediction of health status and showing changes in heart rate and respiration as in Shariff, the health monitoring and predictions as in Chung and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of classifying physiological samples and feature vectors and predicting a future health event.
Response to Arguments
Applicant’s remarks filed May 26, 2026 have been fully considered, but they are not entirely persuasive. The following explains why:
Applicant’s arguments pertaining to prior art rejections are not persuasive. The amended claims have been addressed with regard to the 35 U.S.C. §103 rejection discussed above. The arguments at Pages 17-18 are not persuasive or moot in light of at least new citation to references Pho, Kadkhodaie and Shariff and new reference Chung. The Examiner disagrees at Pages 17-18 that Kadkhodaie fails to disclose to “generate, via the one or more processors using a second machine learning model separate from the first machine learning model, for each feature vector of the set of feature vectors, a weighting corresponding to the respective feature vector based at least in part on the set of feature vectors and the future physiological metric.” (See Kadkhodaie at least at Abstract; Paras.[0096]-[0103] (“The samples are transformed into another space based on the representative shapes, where each sample is represented by a vector. In one implementation, the shape that has the least error compared with a sample shape may be selected. In other implementations, a sample shape may be represented by a weighted sum of multiple representative shapes by using various optimization techniques with appropriate regularization. In such an implementation, the weights are the result of the transformation process and result in an N×M matrix [..] The consistency of samples may be a strong indicator of the usability of sensor data. In the example of FIG. 13, hypothetical samples are shown from a wearable device 101 that captures measurement samples representing the breathing of user 102. […] the size of the cluster may also indicate the quality of the set. For example, if the robust covariance of a set is close to zero, it means that the samples in the interval all have similar features and are thus “similar” to each other […] the quality scores of the samples in the data set are used as a weighting factor for determining quality detection algorithms. In some such implementations, samples with lower quality are treated as less “important” during the development phase when compared to samples of higher quality.”), [0110], [0123]-[0124], [0142], [0211]-[0214] (“The measurements-based models 704 work with the measurements/signals and labels/classes assigned to them to train a model. But in the test phase, no label exists for the signals and so the trained model 704 is used to assign labels to the signals. As an example of measurement-based models, machine learning classifiers can be used to classify the signals based on the features extracted from them and assign labels to them […] a linear combination of the results from dynamic-based and measurement-based models is used to determine a probability distribution that is weighted summed and for which the confidences (or training accuracies) are the weights.”), [0235]; Figs. 1, 10-13, 21-23, 27). The Examiner disagrees at Pages 17-18 that Shariff fails to disclose “the indication of the one or more user-recognizable categories comprises an explanation for a change from the first physiological metric to the actual physiological metric.” (See Shariff at least at Paras. [0064]-[0065] (“The probabilistic classification model may use the training data to identify physiological data which is predictive of a patient later developing a fever. For the purposes of the probabilistic classification models, a fever may be classified as a core body temperature above a threshold temperature. In one implementation the threshold temperature for classifying a core body temperature as representing a “fever” is 100° F. (37.8° C.). Thus, for example, following training with an appropriate set of training data, the probabilistic classification model may generate a probability that physiological data other than core temperature (e.g., heart rate and or respiration rate) predicts a future increase in core temperature to a level that is classified as a “fever.”); Figs. 2-4, 6-7). As such, it is submitted that the cited prior art, including those identified by Applicant, in the same field of endeavor, i.e., techniques for clinical administration and assessment, teaches and/or suggests all of the limitations of the pending claims under a broad and reasonable interpretation thereof.
Applicant’s amended claims and arguments pertaining to subject matter eligibility are persuasive. The rejection under 35 U.S.C. §101 has been withdrawn. There is not sufficient evidence that the additional element of a wearable ring device with PPG sensor and temperature sensor to measure physiological data is well-understood, routine or conventional in the art, and does not comprise insignificant extra-solution activity or merely applying the abstract idea using computer components. Accordingly, there is significantly more than the abstract idea. The claims are thus patent eligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/WILLIAM T. MONTICELLO/ Examiner, Art Unit 3682
/FONYA M LONG/ Supervisory Patent Examiner, Art Unit 3682
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