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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 13, and 17 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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 7-8, 11-13, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kwalwasser et al (US 2023/0218221) hereinafter Kwalwasser in view of Aguilar Domingo (US 2015/0105837).
Regarding claims 1 and 13, Kwalwasser discloses a method comprising the steps of:
receiving, by a device (Fig. 1: brain activity detector 120), biometric data related to a patient ([0027] data may include biometric data), the biometric data corresponding to monitored readings collected by sensors associated with the device ([0042] brain activity detector 120 comprising sensors 124 for recording user brain activity);
inputting, by the device, the received biometric data to a machine learning (ML) engine that has been trained on other biometric data (Fig. 3: step 335), and determining, via the ML engine, a diagnosis corresponding to a medical condition of the patient, the diagnosis including prognosis information indicative of a future predicted medical condition of the patient ([0046] The system 100, for each audio stimulus and each task, applies 335 one or more brain state models to predict brain state value);
determining, by the device, based on the determined diagnosis including the prognosis information, a treatment plan for the patient, the treatment plan comprising instructions that correspond to the medical condition and to the future predicted medical condition of the patient ([0047] system 100 identifies 340 informative audio stimulus features that contribute to optimized brain state);
and executing, by the device, the treatment plan, the execution comprising automatically communicating electronic stimuli to the patient ([0048] system 100 generates 345 an optimal audio stimulus based on the identified informative features).
Kwalwasser fails to disclose communicating electronic stimuli via the sensors to the patient. However, Aguilar Domingo discloses receiving biometric data (EEG data) corresponding to monitored readings collected by sensors associated with a device ([0026] active EEG sensors); and determining a treatment plan ([0031] selecting stimulation protocol) and executing, by the device, the treatment plan, the execution comprising automatically communicating electronic stimuli via the sensors to the patient ([0032] applying the selected electrical stimulation protocol on the active EEG electrodes).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Kwalwasser with communicating electronic stimuli via the sensors to the patient as taught by Aguilar Domingo. Such a modification would provide the predictable results of providing tolerable, portable, and low risk noninvasive brain electro-stimulation based on feedback (Aguilar Domingo, [0006] and [0023]).
Kwalwasser further discloses a non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions [0069].
Regarding claim 3, Kwalwasser discloses wherein the diagnosis comprises mapping information indicating a correlation of the biometric data to the prognosis information ([0046] system 100 decodes a user's brain state over time by applying one or more of the brain state models to the user's brain activity data).
Regarding claim 7, Kwalwasser discloses wherein the diagnosis comprises information related to at least one of a medical disorder, disease related to the medical condition, a prognosis of the medical disease, and a correlation of the biometric data and its mapping to the prognosis ([0046] system 100 decodes a user's brain state over time by applying one or more of the brain state models to the user's brain activity data).
Regarding claim 8, Kwalwasser discloses wherein the treatment plan comprises information related to at least one of a value of the electronic stimuli to output from the device, a schedule for the output, a type of device to use to output the electronic stimuli, and a location of the sensors on the patient to effectuate electronic stimuli ([0049] the audio stimulus can be provided to digital music streaming platforms for use by their users to optimize brain state).
Regarding claim 11, Kwalwasser discloses wherein the steps are performed by the device executing at least one of a support vector machine or logistic regression predictive modelling algorithm ([0045] the brain state models are trained as a plurality of regression models by sampling random users to use as training data for each regression model).
Regarding claim 12, Kwalwasser discloses wherein the device is associated with a wearable neuromodulation device ([0022] band 122 is worn around the head of the user 105).
Regarding claim 17, Kwalwasser discloses a device comprising:
a set of stored computer-executable instructions ([0024] one or more storage media storing computer-readable instructions); and
a processor configured to execute the instructions ([0024] computer-readable instructions for instructing the processor to perform operations) to cause the device to:
receive biometric data related to a patient ([0027] data may include biometric data), the biometric data corresponding to monitored readings collected by sensors associated with the device ([0042] brain activity detector 120 comprising sensors 124 for recording user brain activity);
input the received biometric data to a machine learning (ML) engine that has been trained on other biometric data (Fig. 3: step 335), and determine, via the ML engine, a diagnosis corresponding to a medical condition of the patient, the diagnosis including prognosis information indicative of a future predicted medical condition of the patient ([0046] The system 100, for each audio stimulus and each task, applies 335 one or more brain state models to predict brain state value);
determine, based on the determined diagnosis including the prognosis information, a treatment plan for the patient (informative audio stimulus), the treatment plan comprising instructions that correspond to the medical condition and to the future predicted medical condition of the patient ([0047] system 100 identifies 340 informative audio stimulus features that contribute to optimized brain state); and
execute the treatment plan, the execution comprising automatically communicating electronic stimuli to the patient ([0048] system 100 generates 345 an optimal audio stimulus based on the identified informative features).
Kwalwasser fails to disclose communicating electronic stimuli via the sensors to the patient. However, Aguilar Domingo discloses receiving biometric data (EEG data) corresponding to monitored readings collected by sensors associated with a device ([0026] active EEG sensors); and determining a treatment plan ([0031] selecting stimulation protocol) and executing, by the device, the treatment plan, the execution comprising automatically communicating electronic stimuli via the sensors to the patient ([0032] applying the selected electrical stimulation protocol on the active EEG electrodes).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Kwalwasser with communicating electronic stimuli via the sensors to the patient as taught by Aguilar Domingo. Such a modification would provide the predictable results of providing tolerable, portable, and low risk noninvasive brain electro-stimulation based on feedback (Aguilar Domingo, [0006] and [0023]).
Claim(s) 2, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kwalwasser (US 2023/0218221) in view of Aguilar Domingo (US 2015/0105837) and further in view of Wiard et al (US 2017/0146387) hereinafter Wiard.
Regarding claims 2, 14, and 18, Kwalwasser discloses training, by the device, the ML engine based on the determined portions of the biometric data ([0045] system 100 trains 330 a brain state model with the brain activity features), but fails to disclose:
identifying, by the device, a set of biometric data related to a set of patients;
identifying, by the device, electronic medical records (EMRs) for each patient in the set of patients;
performing, by the device, comparative analysis of the set of biometric data and the EMRs;
determining, by the device, a medical condition for each patient in the set of patients;
determining, by the device, portions of the set of biometric data that correspond to the determined medical conditions; and
training, by the device, a machine learning (ML) engine based on the determined portions of the biometric data.
However, Wiard discloses identifying, by the device, a set of biometric data related to a set of patients ([0132] The user devices and scales further automatically collect and output various user data to the external circuitry 117, such as physiological data, sleep data, cardiogram data, exercise data, heart rate data, and food/liquid intake data);
identifying, by the device, electronic medical records (EMRs) for each patient in the set of patients ([0132] each scale is configured to monitor signals from a plurality of users, correlate the respective data with the appropriate user using scale-based biometrics and user profiles, and communicate the signals and/or data to the external circuitry);
performing, by the device, comparative analysis of the set of biometric data and the EMRs ([0132]; [0134]; Claim 5: the external circuitry is configured and arranged to identify the correlation and form the social group by comparing demographics, user goals, symptoms, physiological parameter values, diagnosis, prescription drug usage, lifestyle habits, medical history, and family medical history of the user data sets);
determining, by the device, a medical condition for each patient in the set of patients ([0136] The correlation, includes patterns and/or trends, risks, and/or parameter values associated with and/or indicative of particular conditions that are common between different users); and
determining, by the device, portions of the set of biometric data that correspond to the determined medical conditions ([0136] the external circuitry identifies other users that have correlated user data and identify patterns of risks for conditions or diseases based on the correlation).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to further modify the method/system as taught by Kwalwasser with identifying, by the device, a set of biometric data related to a set of patients; identifying, by the device, electronic medical records (EMRs) for each patient in the set of patients; performing, by the device, comparative analysis of the set of biometric data and the EMRs; determining, by the device, a medical condition for each patient in the set of patients; determining, by the device, portions of the set of biometric data that correspond to the determined medical conditions as taught by Wiard. Such a modification would provide the predictable results of placing users in a social group and providing the subset of users of the social group with social group data via a respective scale of the subset of users (Abstract). Examiner notes this would help monitor the user’s progress within their social group.
Claim(s) 4, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kwalwasser (US 2023/0218221) in view of Aguilar Domingo (US 2015/0105837) and further in view of Moffit et al (US 2017/0056642) hereinafter Moffit.
Regarding claims 4, 15, and 19, the modified Kwalwasser discloses the method/system of claims 1, 13, and 17 as discussed above, but fails to disclose identifying, by the device, a diagnosis for a set of patients that corresponds to a medical condition;
identifying, by the device, a treatment for the diagnosis for the set of patients; determining, by the device, a treatment plan for the set of patients; executing, by the device, the treatment plan;
analyzing, by the device, results of the treatment plan on each of the set of patients; and
determining, by the device, whether the treatment plan was effective against the medical condition for the set of patients.
However, Moffit discloses identifying, by the device, a diagnosis for a set of patients that corresponds to a medical condition ([0105] At stage 806, the patient's feedback is received, the patient feedback may be a patient metric, such as a pain score or biomarkers);
identifying, by the device, a treatment for the diagnosis for the set of patients ([0106] At stage 808, the patient's feedback is analyzed to determine whether additional modification to the stimulation parameters is needed);
determining, by the device, a treatment plan for the set of patients ([0106] A genetic algorithm is used at stage 810 to identify one or more stimulation parameters);
executing, by the device, the treatment plan ([0106] then a next set of waveform parameters is identified and tested (stage 814));
analyzing, by the device, results of the treatment plan on each of the set of patients ([0105] At stage 806, the patient's feedback is received); and
determining, by the device, whether the treatment plan was effective against the medical condition for the set of patients ([0106] At stage 808, the patient's feedback is analyzed to determine whether additional modification to the stimulation parameters is needed).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method/system as taught by Kwalwasser with identifying, by the device, a diagnosis for a set of patients that corresponds to a medical condition; identifying, by the device, a treatment for the diagnosis for the set of patients; determining, by the device, a treatment plan for the set of patients; executing, by the device, the treatment plan; analyzing, by the device, results of the treatment plan on each of the set of patients; and determining, by the device, whether the treatment plan was effective against the medical condition for the set of patients as taught by Moffit. Such a modification would provide the predictable results of increasing the efficacy of the delivered stimulus by determining whether additional modification to the stimulation parameters is needed (Moffit, [0106]).
Claim(s) 5-6, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kwalwasser (US 2023/0218221) in view of Aguilar Domingo (US 2015/0105837) and Moffit (US 2017/0056642) and further in view of Ganzer (US 2020/0094040).
Regarding claim 5, the modified Kwalwasser discloses the method of claim 4 as discussed above, but fails to disclose training, by the device, a machine learning (ML) engine based on the treatment plan when the determination indicates the treatment plan was effective, wherein the determination of the treatment plan is performed via the device executing the ML engine.
However Ganzer discloses training, by a device, a machine learning (ML) engine based on the treatment plan when the determination indicates the treatment plan was effective, wherein the determination of the treatment plan is performed via the device executing the ML engine ([0031] The controller may receive signals indicating such physiological parameters of a patient and determine whether the crises or condition is occurring or has ended, or predict whether the condition is about to occur or is about to end; the controller may include machine learning models or be part of a machine learning system trained to determine for example when (e.g., in which physiological states) and how (e.g., particular protocols) to effect stimulation).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to further modify the method/system as taught by Kwalwasser with training, by the device, a machine learning (ML) engine based on the treatment plan when the determination indicates the treatment plan was effective, wherein the determination of the treatment plan is performed via the device executing the ML engine as taught by Ganzer. Such a modification would provide the predictable results of training an MLM to recognize patterns of change in order to predict the effects of a stimulus.
Regarding claim 6, the modified Kwalwasser discloses the system of claim 5 as discussed above, but fails to disclose adjusting, by the device, the treatment plan when the determination indicates that the treatment plan was not effective; and executing, by the device, the adjusted treatment plan, wherein a machine learning (ML) engine is trained when the adjusted treatment plan is determined to be effective against the medical condition for the set of patients.
Moffit discloses adjusting, by the device, the treatment plan when the determination indicates that the treatment plan was not effective ([0106] When the genetic algorithm has not reached termination criteria or convergence state, then a next set of waveform parameters is identified and tested (stage 814)); and
executing, by the device, the adjusted treatment plan ([0106] The parameters are used to construct a waveform (stage 802) and the cycle continues).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to further modify the method/system as taught by Kwalwasser with adjusting, by the device, the treatment plan when the determination indicates that the treatment plan was not effective; and executing, by the device, the adjusted treatment plan as taught by Moffit. Such a modification would provide the predictable results of increasing the efficacy of the delivered stimulation by optimizing the stimulation parameters [0106].
However, Ganzer discloses wherein a machine learning (ML) engine is trained when the adjusted treatment plan is determined to be effective against the medical condition for the set of patients ([0031] The controller may receive signals indicating such physiological parameters of a patient and determine whether the crises or condition is occurring or has ended, or predict whether the condition is about to occur or is about to end; the controller may include machine learning models or be part of a machine learning system trained to determine for example when (e.g., in which physiological states) and how (e.g., particular protocols) to effect stimulation).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to further modify the method/system as taught by Kwalwasser with a machine learning (ML) engine is trained when the adjusted treatment plan is determined to be effective against the medical condition for the set of patients as taught by Ganzer. Such a modification would provide the predictable results of training an MLM to recognize patterns of change in order to predict the effects of a stimulus.
Regarding claims 16 and 20, the modified Kwalwasser discloses the method/system of claims 13 and 17 as discussed above, but fails to disclose when the determination indicates the treatment plan was effective:
training, by the device, a machine learning (ML) engine based on the treatment plan, wherein the determination of the treatment plan is performed via the device executing the ML engine; and
when the determination indicates that the treatment plan was not effective:
adjusting, by the device, the treatment plan; and
executing, by the device, the adjusted treatment plan, wherein a machine learning (ML) engine is trained when the adjusted treatment plan is determined to be effective against the medical condition for the set of patients.
However, Moffit discloses determining and indicating that the treatment plan was effective ([0106] the stimulation parameters for optimized therapy are considered to be reached (stage 812);
when the determination indicates that the treatment plan was not effective:
adjusting, by the device, the treatment plan ([0106] When the genetic algorithm has not reached termination criteria or convergence state, then a next set of waveform parameters is identified and tested (stage 814)); and
executing, by the device, the adjusted treatment plan ([0106] The parameters are used to construct a waveform (stage 802) and the cycle continues).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method/system as taught by Kwalwasser with determining and indicating that the treatment plan was effective; when the determination indicates that the treatment plan was not effective: adjusting, by the device, the treatment plan; and executing, by the device, the adjusted treatment plan as taught by Moffit. Such a modification would provide the predictable results of increasing the efficacy of the delivered stimulation by optimizing the stimulation parameters [0106].
Ganzer discloses training, by a device, a machine learning (ML) engine based on a determination of a treatment plan ([0031] The controller may receive signals indicating such physiological parameters of a patient and determine whether the crises or condition is occurring or has ended, or predict whether the condition is about to occur or is about to end; the controller may include machine learning models or be part of a machine learning system trained to determine for example when (e.g., in which physiological states) and how (e.g., particular protocols) to effect stimulation).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to further modify the system/method as taught by Kwalwasser with training, by the device, a machine learning (ML) engine based on the treatment plan, wherein the determination of the treatment plan is performed via the device executing the ML engine; and wherein a machine learning (ML) engine is trained when the adjusted treatment plan is determined to be effective against the medical condition for the set of patients as taught by Ganzer. Such a modification would provide the predictable results of training an MLM to recognize patterns of change in order to predict the effects of a stimulus.
Claim(s) 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Kwalwasser (US 2023/0218221) in view of Aguilar Domingo (US 2015/0105837) and further in view of Forsland et al (US 2020/0133393) hereinafter Forsland.
Regarding claim 9, Kwalwasser discloses wherein the biometric data is received in response to the transmitted AVS program ([0041-0042] system 100 prompts 310 the user to perform the sequence while providing the audio stimulus to the user and records 315 the user's brain activity while the user is performing the tasks), but fails to expressly disclose communicating, by the device, an adaptive closed loop audio-visual stimulation (AVS) program. However, Forsland discloses communicating, by the device, an adaptive closed loop audio-visual stimulation (AVS) program (Fig. 11), wherein the biometric data is received in response to the transmitted AVS program ([0005] After experiencing feedback from all, or any of these three sensory modalities—audio, visual and haptic, a user may generate new and different bio-signals from the brain, and as such a feedback loop may result in creating and strengthening neural pathways).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to further modify the method as taught by Kwalwasser with communicating, by the device, an adaptive closed loop audio-visual stimulation (AVS) program, wherein the biometric data is received in response to the transmitted AVS program as taught by Forsland. Such a modification would provide the predictable results of strengthening neural pathways based on feedback (Forsland, [0005]).
Regarding claim 10, Kwalwasser discloses wherein the transmitted electronic stimuli correspond to the AVS program ([0048] system 100 generates 345 an optimal audio stimulus based on the identified informative features).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLOW GRACE WELCH whose telephone number is (703)756-1596. The examiner can normally be reached Usually M-F 8:00am - 4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached at 571-270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLOW GRACE WELCH/Examiner, Art Unit 3792 /William J Levicky/Primary Examiner, Art Unit 3796