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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/18/2026 has been entered.
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 Objections
Claim 7 is objected to because of the following informalities:
Claim 7, line 2 should be amended to recite, “indicates an onset of the future…”.
Appropriate correction is respectfully requested.
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, 8, 13, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Gu et al (US 2020/0397363) hereinafter Gu.
Regarding claim 1, Gu discloses a method comprising the steps of:
receiving, by the device (implantable device) biometric data related to a patient (physiological signals), the biometric data corresponding to monitored readings collected by sensors associated with the device ([0069] the implantable medical device detects a physiological signal(s) of a patient);
inputting, the received biometric data to a machine learning (ML) engine (Fig. 8: machine learning device 30) that has been trained on other biometric data ([0069] Based on the detected physiological signal, in processing S302, a prediction algorithm can be applied; [0075] performs machine learning based on the training data in processing S311; [0076] generate customized prediction algorithm S312; [0077] upload customized prediction algorithm S315; Fig. 8 shows implantable device 10 inputting the biometric data into external device 20 which inputs data into machine learning device 30), and
determining, via the ML engine, a diagnosis (epilepsy seizure prediction) corresponding to a medical condition of the patient, the diagnosis including prognosis information indicative of a future predicted diagnosable medical condition of the patient ([0069] in processing S303, it is determined whether an epilepsy seizure event is predicted; [0031] implantable device 10 comprises prediction information result; Examiner notes that while the implantable device is determining the diagnosis, it is doing so using the updated algorithm produced by the machine learning device 30);
determining, by the device, based on the determined diagnosis including the prognosis information, a treatment plan for the patient ([0069] nerve stimulation), the treatment plan comprising instructions that:
correspond to the future predicted diagnosable medical condition of the patient ([0069] If a seizure event is predicted, the flow proceeds to processing S304, that is, sending a notification to the external monitoring device); and
indicate one or more parameters of electronic stimuli to be applied to the patient to halt or delay onset of the future predicted medical condition of the patient ([0069] the implantable medical device applies nerve stimulation to the patient to delay or inhibit the predicted epilepsy seizure; Examiner notes this would necessarily include at least one parameter of electronic stimuli);
and
executing, by the device, the treatment plan, the execution comprising automatically communicating electronic stimuli via the sensors to the patient ([0069] Apply nerve stimulation S306).
Gu discloses receiving by an implantable device, biometric data related to a patient, determining by the implantable device a treatment plan, and executing by the implantable device the treatment plan ([0031] and [0069]). Gu fails to expressly disclose the implantable device inputting the received biometric data to a machine learning engine and instead discloses the implantable medical device inputting the received biometric data to an external device which then inputs the biometric data into the machine learning engine (Fig. 8). However, Gu further discloses implementing the external device together with the machine learning device [0029]. Therefore, 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 Gu with inputting, by the device (implantable device), the received biometric data to a machine learning (ML) engine. Such a modification would provide the predictable results of faster data retrieval without network latency.
Regarding claim 3, Gu discloses wherein the diagnosis comprises mapping information indicating a correlation of the biometric data to the prognosis information ([0069] Based on the detected physiological signal, in processing S302, a prediction algorithm can be applied to predict epilepsy seizure events. Next, in processing S303, it is determined whether an epilepsy seizure event is predicted).
Regarding claim 8, Gu 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 ([0069] in processing S305, the implantable medical device can determine whether the user rejects the nerve stimulation; if the user doesn't reject, the flow proceeds to processing S306 where the implantable medical device applies nerve stimulation to the patient; Examiner notes this would provide the user with a schedule for the delivery).
Regarding claim 13, Gu discloses a non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions ([0043] seizure prediction algorithm stored in memory unit 122), that when executed by a device ([0040] implantable device 100), perform a method comprising steps of:
receiving, by the device (implantable device) biometric data related to a patient (physiological signals), the biometric data corresponding to monitored readings collected by sensors associated with the device ([0069] the implantable medical device detects a physiological signal(s) of a patient);
inputting, the received biometric data to a machine learning (ML) engine (Fig. 8: machine learning device 30) that has been trained on other biometric data ([0069] Based on the detected physiological signal, in processing S302, a prediction algorithm can be applied; [0075] performs machine learning based on the training data in processing S311; [0076] generate customized prediction algorithm S312; [0077] upload customized prediction algorithm S315; Fig. 8 shows implantable device 10 inputting the biometric data into external device 20 which inputs data into machine learning device 30), and
determining, via the ML engine, a diagnosis (epilepsy seizure prediction) corresponding to a medical condition of the patient, the diagnosis including prognosis information indicative of a future predicted diagnosable medical condition of the patient ([0069] in processing S303, it is determined whether an epilepsy seizure event is predicted; [0031] implantable device 10 comprises prediction information result; Examiner notes that while the implantable device is determining the diagnosis, it is doing so using the updated algorithm produced by the machine learning device 30);
determining, by the device, based on the determined diagnosis including the prognosis information, a treatment plan for the patient ([0069] nerve stimulation), the treatment plan comprising instructions that:
correspond to the medical condition and to the future predicted diagnosable medical condition of the patient ([0069] If a seizure event is predicted, the flow proceeds to processing S304, that is, sending a notification to the external monitoring device); and
indicate one or more parameters of electronic stimuli to be applied to the patient to halt or delay onset of the future predicted medical condition of the patient ([0069] the implantable medical device applies nerve stimulation to the patient to delay or inhibit the predicted epilepsy seizure; Examiner notes this would necessarily include at least one parameter of electronic stimuli); and
executing, by the device, the treatment plan, the execution comprising automatically communicating electronic stimuli via the sensors to the patient ([0069] Apply nerve stimulation S306).
Gu discloses receiving by an implantable device, biometric data related to a patient, determining by the implantable device a treatment plan, and executing by the implantable device the treatment plan ([0031] and [0069]). Gu fails to expressly disclose the implantable device inputting the received biometric data to a machine learning engine and instead discloses the implantable medical device inputting the received biometric data to an external device which then inputs the biometric data into the machine learning engine (Fig. 8). However, Gu further discloses implementing the external device together with the machine learning device [0029]. Therefore, 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 Gu with inputting, by the device (implantable device), the received biometric data to a machine learning (ML) engine. Such a modification would provide the predictable results of faster data retrieval without network latency.
Regarding claim 17, Gu discloses a device ([0040] implantable medical device 1000) comprising:
a set of stored computer-executable instructions ([0043] seizure prediction algorithm stored in memory unit 122); and
a processor ([0043] control unit 120) configured to execute the instructions to cause the device to:
receive biometric data related to a patient (physiological signals), the biometric data corresponding to monitored readings collected by sensors associated with the device ([0069] the implantable medical device detects a physiological signal(s) of a patient);
determine, via a ML engine, a diagnosis (epilepsy seizure prediction) corresponding to a medical condition of the patient, the diagnosis including prognosis information indicative of a future predicted diagnosable medical condition of the patient ([0069] in processing S303, it is determined whether an epilepsy seizure event is predicted; [0031] implantable device 10 comprises prediction information result; Examiner notes that while the implantable device is determining the diagnosis, it is doing so using the updated algorithm produced by the machine learning device 30);
determine, based on the determined diagnosis including the prognosis information, a treatment plan for the patient ([0069] nerve stimulation), the treatment plan comprising instructions that:
correspond to the future predicted diagnosable medical condition of the patient ([0069] If a seizure event is predicted, the flow proceeds to processing S304, that is, sending a notification to the external monitoring device); and
indicate one or more parameters of electronic stimuli to be applied to the patient to halt or delay onset of the future predicted medical condition of the patient ([0069] the implantable medical device applies nerve stimulation to the patient to delay or inhibit the predicted epilepsy seizure; Examiner notes this would necessarily include at least one parameter of electronic stimuli); and
execute the treatment plan, the execution comprising automatically communicating electronic stimuli via the sensors to the patient ([0069] Apply nerve stimulation S306).
Gu discloses receiving by an implantable device, biometric data related to a patient, determining by the implantable device a treatment plan, and executing by the implantable device the treatment plan ([0031] and [0069]). Gu fails to expressly disclose the implantable device inputting the received biometric data to a machine learning engine and instead discloses the implantable medical device inputting the received biometric data to an external device which then inputs the biometric data into the machine learning engine (Fig. 8). However, Gu further discloses implementing the external device together with the machine learning device [0029]. Therefore, 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 Gu with inputting, by the device (implantable device), the received biometric data to a machine learning (ML) engine. Such a modification would provide the predictable results of faster data retrieval without network latency.
Regarding claim 18, Gu discloses wherein the computer-executable instructions are stored within the processor ([0043] control unit 120 comprises a processing unit 121 and a memory unit 122. The processing unit 121 is configured to predict epilepsy seizure events in real time by using the seizure prediction algorithm stored in the memory unit 122).
Claim(s) 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Gu (US 2020/0397363) in view of Wiard et al (US 2017/0146387) hereinafter Wiard.
Regarding claims 2 and 14, Gu discloses training, by the device, the ML engine based on determined portions of the biometric data (Fig. 10: steps S307-S311; [0073-0075]), 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.
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 modify the method as taught by Gu 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 (Ward, Abstract). Examiner notes this would help monitor the user’s progress within their social group.
Claim(s) 4-5, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Gu (US 2020/0397363) in view of Moffit et al (US 2017/0056642) hereinafter Moffit.
Regarding claims 4, 15, and 19, Gu 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 Gu 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]).
Regarding claim 5, Gu discloses training, by a device, a machine learning (ML) engine based on the treatment plan when the determination indicates the treatment plan was effective ([0070] and [0073-0075] Steps S307-S311), wherein the determination of the treatment plan is performed via the device executing the ML engine ([0075] the machine learning device divides the original data into training data and verification data in processing 5310, and performs machine learning based on the training data in processing 5311). Gu fails to disclose the implantable device training machine learning (ML) engine and instead teaches the machine learning device training a machine learning engine [0075]. 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 machine learning-based medical system as taught by Gu, to integrate the machine learning device into the same housing as the implantable device, since such a modification would provide the predictable results of faster data retrieval by making integral what had been made in separate devices, as the use of a one piece construction instead of the structure disclosed in Gu would be merely a matter of obvious engineering choice (see MPEP §2144.04(V)(B)).
Claim(s) 6, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Gu (US 2020/0397363) in view of Moffit et al (US 2017/0056642) and further in view of Ganzer (US 2020/0094040).
Regarding claim 6, the modified Gu 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 as taught by Gu 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 as taught by Gu 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, Gu 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 Gu 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 Gu 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) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Gu (US 2020/0397363) in view of Sullivan et al (US 2016/0135706) hereinafter Sullivan.
Regarding claim 7, Gu discloses the method of claim 1 as discussed above, but fails to disclose wherein the prognosis information indicates onset of the future predicted medical condition of the patient is predicted to occur one or more years from a current date. However, Sullivan discloses wherein the prognosis information indicates onset of the future predicted medical condition of the patient is predicted to occur one or more years from a current date ([0498] scores used to predict events likely to occur within a one and three month timeframe, or six month to one year timeframe).
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 Gu with the prognosis information indicates onset of the future predicted medical condition of the patient is predicted to occur one or more years from a current date as taught by Sullivan. Such a modification would provide the predictable results of guiding a long term treatment (Sullivan, [0498]).
Claim(s) 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Gu (US 2020/0397363) in view of Forsland et al (US 2020/0133393) hereinafter Forsland.
Regarding claim 9, Gu discloses the method of claim 1 as discussed above, but fails to disclose 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. 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 ([0089] When the human user 1114 interacts with the environment, the sensor 1102, located within the BCI 1116, reads the intentions and triggers the operating system).
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 Gu 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, the modified Gu discloses the method of claim 9 as discussed above, but fails to disclose wherein the transmitted electronic stimuli correspond to the AVS program. However, Forsland discloses wherein the transmitted electronic stimuli correspond to the AVS program (Fig. 11; [0089] The signal is then processed, analyzed and mapped to an Audio/Video/Haptic Output 1108 and displayed on the augmented reality glasses 1112). 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 Gu with the transmitted electronic stimuli correspond to the AVS program as taught by Forsland. Such a modification would provide the predictable results of strengthening neural pathways based on feedback (Forsland, [0005]).
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Gu (US 2020/0397363) in view of Ganzer (US 2020/0094040).
Regarding claim 11, Gu discloses wherein the steps are performed by the device executing at least one of a predictive modelling algorithm [0043], but fails to disclose wherein the steps are performed by the device executing at least one of a support vector machine or logistic regression predictive modelling algorithm. However, Ganzer discloses wherein steps are performed by a device executing at least one of a support vector machine ([0031] instantaneous predictions from a support vector machine). 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 Gu with a device executing at least one of a support vector machine as taught by Ganzer. Such a modification would provide the predictable results of instantaneously predicting whether a condition is about to occur (Ganzer, [0031]).
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Gu (US 2020/0397363) in view of Kwalwasser et al (US 2023/0218221) hereinafter Kwalwasser.
Regarding claim 12, Gu discloses the method of claim 1 as discussed above, but fails to disclose wherein the device is associated with a wearable neuromodulation device. However, Kwalwasser discloses a wearable neuromodulation device ([0022] band 122 is worn around the head of the user 105). 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 device as taught by Gu with a wearable neuromodulation device as taught by Kwalwasser. Such a modification would provide the predictable results of a non-invasive device which can be easily removed by the user.
Conclusion
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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/WILLOW GRACE WELCH/Examiner, Art Unit 3792
/ALLEN PORTER/Primary Examiner, Art Unit 3796
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