DETAILED ACTION
Response to Arguments
Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s amendments and arguments with respect to the claim rejections under 35 U.S.C. 112 have been considered and are persuasive. The 35 U.S.C. 112 rejections are withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10, 11, 13-19 and 22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 10, the limitation experiences “the symptoms of the medical scenario” lacks proper antecedent basis. It is unclear if “the symptoms” are the same “combination of symptoms” as previously recited in claim 10. For purposes of examination the indefinite limitation has been deemed to claim the same “combination of symptoms”.
Regarding claim 13, the limitation “a likelihood that the user is experiencing or will experience the medical scenario based on whether the experiencing a combination of symptoms consistent with the medical scenario” grammatically unclear, therefore, the exact limitations of the claim is indefinite.
Regarding claim 22, the limitation “the communications circuitry” lacks proper antecedent basis.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 2, 7-13, 15 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200205746 A1 to Burwinkel et al. (hereinafter, Burwinkel) in view of US 20190374713 A1 to Kiani et al. (hereinafter, Kiani).
Regarding Claims 1, 13 and 20, Burwinkel discloses an ear-wearable device, system and method ([0059] “…the body-worn device 102 can include a hearing assistance device such as behind-the-ear (BTE), in-the-ear (ITE), in-the-canal (ITC), or completely-in-the-canal (CIC) type hearing instrument.”) comprising inter alia:
a sensor configured to sense values of one or more physiological parameters of a user of the ear-wearable device ([0056] “The optional sensor 104 is adapted to detect a physiological characteristic of the wearer of the body-worn device 102 and generate physiological data representative of the physiological characteristic.”);
communications circuitry ([0069] “…the components 220 can also include a transmitter that transmits electromagnetic signals and a radio-frequency receiver that receives electromagnetic signals using any suitable protocol or combination of protocols.”) ([0070] “…the body-worn device 200 (or any other body-worn device described herein) can be connected to one or more external devices using, e.g., Bluetooth, Wi-Fi, magnetic induction, mesh networks, etc.”); and
processing circuitry ([0061] “Any suitable controller 222 can be utilized with the body-worn device 200, e.g., the same controller or controllers described regarding controller 106 of system 100 of FIG. 2.”) configured to:
apply a machine learning (ML) model to the values of the one or more sensed physiological parameters to determine whether the user is experiencing a combination of symptoms of a medical scenario ([0032] “…various machine learning models can be applied to generate the sub-models (e.g., linear regression, decision tree, Bayesian models, neural network, Kalman filtering, etc.).”) ([0055] “The controller 106 can further be adapted to detect disruption to the wearer's postural stability or confidence. For example, the controller 106 can detect a vestibular disturbance of the wearer, an occurrence of a fall, an episode of dizziness, an episode of syncope, a seizure, a stroke, an anaphylactic shock, an aneurysm, a hard sit into a chair, a near fall, a trip, a stumble, a change in gait, a hesitation or a fear of falling, or the like.”) ([0083] “One or more pattern recognition machine learning techniques such as Hidden Markov Models (HMMs), Bayesian models, non-linear regressions, decision trees, support vector machines, neural networks, etc. can be applied to input data 302 to predict future physiological and contextual states based upon, at least in part, one or more of present and historical data.”), wherein the ML model is configured to be trained via a training set comprising a plurality of physiological parameter values and a corresponding plurality of symptoms of the medical scenario ([0083] “During a learning stage, prepopulated group data or laboratory data can be utilized to form the basis of training one or more pre-trained models.”) ([0089] “…when a fall event is detected, the method 300 can send postural data collected before the event by one or more sensors 104, e.g., to a cloud server. In one or more embodiments, data from the wearer and other wearers can be used to train a regression model or deep neural network to estimate the risk of a fall for an individual wearer at 318. One or more of the sub-models 318 can be generated from analytics or machine learning of larger group data using any suitable technique or techniques, e.g., regression, steady-state, Bayesian, classification trees, Volterra, support vector machine, Gaussian mixture, neural network techniques, and combinations thereof.”) (“Likelihood” addressed at [0038]); and
based on a determination that the user is experiencing the combination of symptoms consistent with the medical scenario, cause the communications circuitry to transmit a notification indicating that the user is experiencing the combination of symptoms consistent with the medical scenario ([0044] “The controller 106 is also adapted to determine, for a future time, whether a fall condition is satisfied based upon the one or more future physiological states or contextual states and generate a fall prevention output responsive to satisfaction of the fall condition.”) ([0052] “…the fall prevention output can include transmission of one or more of the physiological data and the contextual data to one or more of a caregiver, a medical professional, a database, or the wearer.”) ([0106] “If the fall risk value or statistics crosses a fall risk threshold, then an alert can be sent to one or more of the wearer, caregiver, and medical professional. Such alerts can include instructions for how to prevent a fall from occurring.”);
(Claim 2) wherein the ML model comprises a decision tree model ([0032] “…various machine learning models can be applied to generate the sub-models (e.g., linear regression, decision tree, Bayesian models, neural network, Kalman filtering, etc.).”) ([0083] “One or more pattern recognition machine learning techniques such as Hidden Markov Models (HMMs), Bayesian models, non-linear regressions, decision trees, support vector machines, neural networks, etc. can be applied to input data 302 to predict future physiological and contextual states…”);
(Claims 7 and 19) wherein the processing circuitry is further configured to: receive, via the communications circuitry, user input indicating a medicinal history of the user ([0090] “Inputs for generating the sub-models 318 can either be obtained based on clinical evaluations and medical history or be learned by the predictive fall event management system 100 from one or more inputs provided by various types of sensors, e.g., sensor 104 and responses to queries.”); and select, based on the received user input, the one or more physiological parameters from a plurality of available physiological parameters ([0084] “…a particular model at 306 can be trained or continuously adapted at 314 to learn the effects that a specific medication has on an individual, over time, based on data from a group of individuals.”), wherein the one or more physiological parameters are indicative of the combination of symptoms consisten with the medical scenario from a medical substance listed in the medicinal history of the user ([0035] “…the historical data relating to dizziness level or imbalance, associated with previous occurrences of taking the medicine, can be used to customize the medicine's effects on the user, thereby improving the accuracy of the individual's fall risk estimation.”);
(Claim 9) a memory configured to store a plurality of ML models ([0030] “ These relationships can be determined, and the disclosed fall risk model can be written as the sum of many sub-models…), and wherein the processing circuitry is configured to: select, based on the medical substance listed in the medicinal history of the user, the ML model from the plurality of ML models ([0084] “…a particular model at 306 can be trained or continuously adapted at 314 to learn the effects that a specific medication has on an individual, over time, based on data from a group of individuals.”), wherein each ML model of the plurality of ML models is configured to determine a likelihood that the user is experiencing the medical scenario from a corresponding medical substance ([0035] “…the historical data relating to dizziness level or imbalance, associated with previous occurrences of taking the medicine, can be used to customize the medicine's effects on the user, thereby improving the accuracy of the individual's fall risk estimation.”);
(Claim 10) wherein to cause the communications circuitry to transmit the notification indicating that the user is experiencing the combination of symptoms consistent with the medical scenario, the processing circuitry is configured to: receive, via the communications circuitry, sensor data confirming that the user is experiencing the symptoms of the medical scenario; and cause the communications circuitry to transmit the notification based on receipt of the sensor data ([0078] “…the system 100 can be utilized to detect head impact, check with the wearer for consciousness, and inform one or more of the wearer, caregiver, and medical professional of the detection of a head impact and level of consciousness of the wearer.”);
(Claim 11) wherein the sensor data comprises one or more of: a body temperature of the user; a change in the body temperature of the user; a posture of the user; a change in the posture of the user; a heart rate of the user; or a change in the heart rate of the user ([0040]);
(Claims 12 and 18) wherein the sensor comprises one or more of an accelerometer, an inertial measurement unit (IMU), a microphone, a photoplethysmography (PPG) sensor, or an oximeter ([0074], [0091], [0094]); and
(Claim 15) wherein the processing system is configured to: receive, from the ML model, a value corresponding to the likelihood that the user is experiencing the combination of symptoms consistent with the medical scenario; compare the received value against a threshold value stored in the memory, wherein the threshold value corresponds to a threshold likelihood that the user is experiencing the combination of symptoms consistent with the medical scenario; and based on a determination that the received value satisfies the threshold value, transmit the notification to the computing device ([0106] “If the fall risk value or statistics crosses a fall risk threshold, then an alert can be sent to one or more of the wearer, caregiver, and medical professional. Such alerts can include instructions for how to prevent a fall from occurring.”) ([0038] “Conditions or thresholds can be set for individual composite risk estimation values, the corresponding time in the future, and their respective statistics, e.g., the size of the confidence intervals. These conditions may be stepped in any appropriate manner to indicate degrees of severity (e.g., high, medium, low), and different actions and interventions may be triggered at different condition levels.”); and
Burwinkel discloses the claimed invention except for expressly disclosing wherein the medicinal history comprises type of a medical substance used by the user within a period of time; and a dosage of the medical substance used by the user and that the medical scenario comprises at least one of a medical overdose, a medical substance withdrawal, or a medical substance addiction, such that the combination of symptoms, the corresponding symptoms of the training set, and the notification are those of the medical overdoes
However, Kiani teaches sensing physiological parameters of a user with an ear sensor ([0109] “The sensor 102 can be an ear sensor that is not readily visible.”), wherein the medicinal history comprises a type of a medical substance used by the user within a period of time and a dosage of the medical substance used by the user ([0142]-[0145]), determining whether, or a likelihood that, the user is experience or will experience a combination of symptoms consistent with a medical scenario comprising a medical overdoes ([0003] “ Symptoms of an opioid overdose include marked confusion, delirium, or acting drunk; frequent vomiting; pinpoint pupils; extreme sleepiness, or the inability to wake up; intermittent loss of consciousness; breathing problems, including slowed or irregular breathing; respiratory arrest (absence of breathing); respiratory depression (a breathing disorder characterized by slow and ineffective breathing); and cold, clammy skin, or bluish skin around the lips or under the fingernails.”) ([0151] “a combination of the PI, respiration, and SpO.sub.2 together; a combination of PI and respiration; a combination of PI and SpO.sub.2; or a combination of respiration and SpO.sub.2 to determine the condition of the user”) ([0149] “compare the physiological parameters 118 against a threshold to determine is an overdose event is occurring or will soon occur”), a model trained via a training set comprising a plurality of physiological parameter values and a corresponding plurality of symptoms of the medical scenario ([0145] “A “near miss” is an event that provided indications of an overdose, such as an indication of respiration below a threshold, but did not result in an overdose event.”) ([0146] “By using the history of the physiological parameters 118 including the near-misses, and the user input, if available, the application 130 can learn which combination of events and parameter values indicate an overdose event may be imminent.”), and transmitting a notification indicating the combination of symptoms consistent with the medical scenario ([0161] “Once the application 130 has determined that an overdose condition is imminent, is occurring, or has occurred, the application 130 can provide notification of the overdose to selected family, friends, caregivers, clinicians, and medical personnel.”);
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to configure the ML model of Burwinkle, which already monitors the effects of medications (Burwinkle, [0124] “drug concentration information”), to determine and notify of a combination of symptoms consistent with a medical overdose because Kiani teaches that the detection of an early overdose by using indicators captured at the ear would have allowed for early enough time for an emergency treatment (Kiani, [0109] “The sensor 102 can be an ear sensor that is not readily visible” and [0007] “If the overdoes is caught early, emergency treatment can be provided before irreparable harm occurs”).
Claim(s) 4, 16, 21 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burwinkel in view Kiani, and further in view of US 20200268315 A1 to Burwinkel et al. (hereinafter, ‘315).
Regarding Claims 4 and 16, Burwinkel in view of Kiani teach the ear wearable device where the physiological parameters are indicative of a combination of symptoms consistent with a medical overdoes (Kiani, [0003] “Symptoms of an opioid overdoes include marked confusion, delirium, or acting drink… frequent vomiting”). Burwinkel in view of Kiani teach the claimed invention except for expressly disclosing where the physiological parameters comprise at least one of instances of vomiting by the user or instances of choking by the user.
However, ‘315 teaches an ear-wearable device ([0029]) where the physiological parameters comprise instances of vomiting by the user ([0092] “the system can detect emesis of the patient. Emesis can be detected based on characteristic acoustic patterns and/or characteristic postural and diaphragm/abdominal contraction patterns. In various embodiments, emesis can be detected by analyzing signals from at least one of an IMU and a microphone.”) ([0108] “a plurality of detected instances of medication administration events, occurrences of side effects, social interactions, nutrition events, hydration events, emesis events, excretion events”).
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to include the instances of vomiting by the user as taught by ‘315, in the one or my physiological parameters of Burwinkel, because Kiani already teaches that vomiting is a known symptom of a medical overdoes ([0003]) and ‘315 teaches that their ear wearable device monitors medications for an overdoes (‘315, [0067]) to alert care providers to adverse medicals effects (‘315, [0027]), which is something that Burwinkle already contemplates ([0034], [0084] and [0124]).
Regarding Claims 21 and 22, Burwinkle in view of Kiani teach the ear-wearable device and further teach where the processing circuitry or processing system is configured to apply the ML model to determine the likelihood that the user is experiencing the combination of symptoms consistent with the medical scenario (Burwinkle [0083] “The application 130 can use the user’s perfusion index to determine the likelihood of an overdose event”) and based on the likelihood, cause the communications circuitry to transmit the notification (Kiani, [0161] “the application 130 can provide notification of the overdose to selected family, friends, caregivers, clinicians, and medical personnel”).
Burwinkel in view of Kiani does not expressly teach a period of time between the user ingesting a medical substance and start of physiological effects indicated in the one or more sensed physiological parameters, or verifying the determined likelihood based on determination that the period of time is consistent with an onset of action period of the medical substance.
However, ‘315 teaches determining a period of time between the user ingesting a medical substance and start of physiological effects indicated in the one or more sensed physiological parameters ([0084] “Sound data consistent with a swallowing action on the part of the patient can be used to detect a medication administration event.”) ([0006] “record an instance of a detected medication administration event along with a timestamp”) ([0039] “In some cases, the correlation can include a time offset “X””) and verifying a determination based on the period of time being consistent with an onset of action period of the medical substance ([0039] “Time offset “X” can depend on the particular therapeutic agent.”) ([0090] “the system can detect that administration of a therapy has taken place by detecting the effects of administration of a therapy… over time spans of seconds, minutes, or hours”).
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to determine the period of time between ingestion of the medical substance and the start of the physiological effects, and to verify the likelihood of Burwinkel in view of Kiani on that period being consistent with the substance’s onset action, as taught by ‘315, because ‘315 teaches that the physiological effects of a substance follow an agent-specific time course after ingestion (‘315, [0039])
Claim(s) 5, 6 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burwinkel in view Kiani, and further in view of US 20190231197 A1 to Kirszenblat et al. (hereinafter, Kirszenblat).
Burwinkel in view Kiani teach the ear-wearable device except for expressly disclosing wherein the physiological parameter is a respiration pattern of the user, wherein the respiration pattern comprises one or more of a delay between an exhalation and an inhalation by the user or an intra-breath spacing between temporally adjacent breaths by the user. It is noted, however, that Burwinkel in view Kiani does teach accelerometers, gyroscopes, IMUs and PPGs (Burwinkle [0022]) and microphones (Burwinkle [0024]) capable of detecting respiration. Kirszenblat teaches an ear-wearable device that has a microphone and a processor that receives input audio input signals from a microphone to computer a rate of respiration (Abstract). Kirszenblat teaches the delay and/or intra-breath spacing between breaths of the user [0024]-[0025]. One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify the sensors and processors of Burwinkel in view Kiani to detect delay between an exhalation and an inhalation by the user or an intra-breath spacing between temporally adjacent breaths by the user of Kirszenblat, as Kirszenblat teaches at [0010] that the sensing of respiration rate in the ear without interference from audio signals being generated by the earphone, without the need to seal the ear interface, would have been an advantage.
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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/SEAN P DOUGHERTY/ Primary Examiner, Art Unit 3791
