DETAILED ACTION
This Office Action is responsive to the Amendment filed 15 December 2025. Claims 1 – 4, 6 – 15 and 17 are now pending. The Examiner acknowledges the amendments to claims 8, 11, 13, and 17 as well as the cancellation of claim 16.
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 .
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 11 - 12 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.
Claim 11 recites the limitation "the plurality of neural state signals" in line 5. There is insufficient antecedent basis for this limitation in the claim. Examiner suggests to amend to --the plurality of neural activity signals-- if that is the applicant’s intention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 4, 6 – 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Coleman et al (US 20150199010 A1, hereinafter Coleman).
Regarding claim 1, Coleman teaches a brain-computer interface (BCI) system (“BCI”, [0007] – [0008]; abstract), comprising:
a neural activity sensor (“electroencephalogram (“EEG”)”, [0004], [0009], [0049]) configured to monitor a series of neural activity signals indicative of a neural state (“determine the user's cognitive or emotional state”, [0007]; [0207]; “saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals”, [0065]) of a subject (“user”, [0007], [0207]) ([0016]; “a pattern identified in a user's bio-signal data that represents a user's response observed to particular events (which may be called "feature events"), particularly when that user's response is observed multiple times with respect to the repeated occurrence of those particular events”, [0207] – [0210]);
a peripheral stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]) configured to administer a series of peripheral stimulations ([0052] – [0053], “For instance the system platform could help to make distracting advertisements or messaging disappear when the user is trying to work and are having trouble concentrating. Detection for such distractions or interruptions may be performed through theta band phase locking for visual and auditory stimulus.”, [0393]) to the subject; and
a computing device (“mobile device (or client computing device)”, [0067], [0069]) operatively coupled to the neural activity sensor (“Upon connecting an EEG headband with the mobile device”, [0069]) and to the peripheral stimulation device (Examiner interprets “a specific activity type (e.g. meditation exercise)” application [0070] encompasses visual and audio stimulus.; [0071]), the computing device ([0067], [0069]) comprising at least one processor (“a central processing unit ("CPU") 502”, [0424]), wherein the at least one processor (502) is configured to:
receive the series of neural activity signals ([0007], “particularly when that user's response is observed multiple times with respect to the repeated occurrence of those particular events”, [0207]; “Features can be derived from any measure or variable that is available to the system platform, such as time of day, EEG signal, heart rate, person's mood, age, gender, height, weight education, income, etc. The system platform may maintain a database store of all of the combination of feature events.” [0208]; [0210]) from the neural activity sensor (EEG, [0004], [0009]) ([0016], [0208] – [0212]); and
iteratively generate and administer the series of peripheral stimulations ([0052] – [0053], “For instance the system platform could help to make distracting advertisements or messaging disappear when the user is trying to work and are having trouble concentrating. Detection for such distractions or interruptions may be performed through theta band phase locking for visual and auditory stimulus.”, [0393]) based on the series of neural activity signals ([0065], [0396] – [0399]; “particularly when that user's response is observed multiple times with respect to the repeated occurrence of those particular events”, [0207]; “Features can be derived from any measure or variable that is available to the system platform, such as time of day, EEG signal, heart rate, person's mood, age, gender, height, weight education, income, etc. The system platform may maintain a database store of all of the combination of feature events.” [0208]; [0210]),
wherein the BCI system (“BCI”, [0007] – [0008]; abstract) is configured to modify the neural state of the subject from a baseline state (“Based on these thresholds, an estimate of the person's emotional state is determined.”, [0244]) to a target state without volitional input from the subject (“The Machine Learning module of the system platform adjusts the thresholds based on the user's information. The user's heart rate and EMG features are used to determine if the user is under low, medium or high stress.”, [0246]; [0139]).
The modified Coleman does not teach each iteration of peripheral stimulations is generated according to an artificial intelligence model configured to modify each subsequent series of peripheral stimulations based on an analysis of modifications of each previous neural state of the subject induced by an administration of each previous series of peripheral stimulations.
However, Coleman additionally teaches “[w]orkflows that include machine learning and other statistical methods that determine if specific rules are effective and need to be updated as well as the updating process” ([0196]), “variations to rules are tested with a user to see if the behaviour is desired” ([0196]) ([0197]; [0199]) for optimization ([0393]), each iteration of peripheral stimulations ([0052] – [0053], “For instance the system platform could help to make distracting advertisements or messaging disappear when the user is trying to work and are having trouble concentrating. Detection for such distractions or interruptions may be performed through theta band phase locking for visual and auditory stimulus.”, [0393]) is generated according to an artificial intelligence model ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) configured to modify each subsequent series of peripheral stimulations ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) based on an analysis of modifications of each previous neural state of the subject induced by an administration of each previous series of peripheral stimulations ([0052] – [0053], “For instance the system platform could help to make distracting advertisements or messaging disappear when the user is trying to work and are having trouble concentrating. Detection for such distractions or interruptions may be performed through theta band phase locking for visual and auditory stimulus.”, [0393]) ([0065]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Coleman to update and modify processes using machine learning, as taught by Coleman, for the benefit of achieving a desired behaviour (Coleman: [0196]) and providing a brainwave focus disrupter/encourager (Coleman: [0393]).
Regarding claim 2, Coleman teaches all limitations of claim 1. Coleman teaches the neural activity sensor is selected from at least one electroencephalographic (EEG) electrode (“electroencephalogram ("EEG")”, [0004], [0009], [0049]), at least one single neuron recording electrode, at least one electrocorticography (ECoG) electrode, a functional magnetic resonance imaging (fMRI) scanner, a magnetoencephalographic (MEG) magnetometer, and at least one functional optical coherence tomography (fOCT) sensor.
Regarding claim 3, Coleman teaches all limitations of claim 2. Coleman teaches the peripheral stimulation device is selected from a pressure stimulation device, a vibrational stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]), an auditory stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]), a visual stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]), and any combination thereof ([0052] – [0053]).
Regarding claim 4, Coleman teaches all limitations of claim 3. Coleman teaches the at least one processor (“a central processing unit ("CPU") 502”, [0424]) is further configured to receive the target neural state ([0059]; “important neural correlate of mediation expertise”, [0063]; “determine the general signal characteristics of the target brain states of interest”, [0165]; “user to choose a target brain state or mood”, [0384]) from an operator (“operator”, [0424]) of the system ([0007], [0013], [0424]).
Regarding claim 6, Coleman teaches all limitations of claim 1. Coleman teaches the artificial intelligence model ([0195] – [0197]; [0199]) is configured to reconfigure the series of peripheral stimulations ([0052] - [0053], “For instance the system platform could help to make distracting advertisements or messaging disappear when the user is trying to work and are having trouble concentrating. Detection for such distractions or interruptions may be performed through theta band phase locking for visual and auditory stimulus.”, [0393]) based on changes in the series of neural activity signals ([0065], “a change in their data sources or algorithms. This could create feedback loops”, [0229], “particularly when that user's response is observed multiple times with respect to the repeated occurrence of those particular events”, [0207]; “Features can be derived from any measure or variable that is available to the system platform, such as time of day, EEG signal, heart rate, person's mood, age, gender, height, weight education, income, etc. The system platform may maintain a database store of all of the combination of feature events.” [0208]; [0210]).
Regarding claim 7, Coleman teaches all limitations of claim 6. Coleman teaches the artificial intelligence model is a genetic algorithm (“genetic algorithm”, [0200], [0223], [0276]).
Regarding claim 8, Coleman teaches a computer-implemented method ([0007]) for modifying a neural state of a subject in need ([0007], [0012]), the method comprising:
providing a brain-computer interface (BCI) system (“BCI”, [0007] – [0008]; abstract) comprising:
a neural activity sensor (“electroencephalogram (“EEG”)”, [0004], [0009], [0049]) configured to detect a plurality of neural activity signals indicative of a neural state (“determine the user's cognitive or emotional state”, [0007]; “saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals”, [0065]) of the subject (“user”, [0007]) ([0016]);
a peripheral stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]) configured to administer a plurality of peripheral stimulations ([0052] – [0053]) to the subject; and
a computing device (“mobile device (or client computing device”, [0067]) operatively coupled to the neural activity sensor (“electroencephalogram (“EEG”)”, [0004], [0009], [0049]) ([0062], [0069]) and to the peripheral stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]) comprising at least one processor (“a central processing unit ("CPU") 502”, [0424]); receiving, using the computing device, a target neural state from an operator of the system ([0388]);
detecting, at the neural activity sensor (“electroencephalogram (“EEG”)”, [0004], [0009], [0049]), a plurality of baseline neural activity signals ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065], [0388]) indicative of a baseline neural state of the subject ([0065], [0388]);
transforming, using the computing device, the plurality of baseline neural activity signals ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) into a peripheral stimulation pattern ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) according to an artificial intelligence model ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]);
detecting, at the neural activity sensor (“electroencephalogram (“EEG”)”, [0004], [0009], [0049]), a plurality of modified neural activity signals ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065], [0388]) indicative of a modified neural state of the subject ([0065], [0388]); and
iteratively modifying the peripheral stimulation pattern ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) to match the modified neural state of the subject to the target neural state ([0040], [0065] – [0066]),
wherein the method is configured to modify the neural state of the subject from the baseline neural state (“Based on these thresholds, an estimate of the person's emotional state is determined.”, [0244]) to the target neural state without volitional input from the subject (“The Machine Learning module of the system platform adjusts the thresholds based on the user's information. The user's heart rate and EMG features are used to determine if the user is under low, medium or high stress.”, [0246], [0139]).
The modified invention of Coleman does not explicitly teach in the general embodiment administering, using the peripheral stimulation device, a peripheral stimulation to the subject, the peripheral stimulation defined by the peripheral stimulation.
However, Coleman does teach an specific embodiment ([0375]) administering, using a peripheral stimulation device (“environmental control of music or lights”, [0375]), a peripheral stimulation (“e.g. dimming the lights or turn off the lights while when the user falls asleep”, [0375]) to the subject (“user”, [0375]), the peripheral stimulation defined by a peripheral stimulation pattern ("provide specialized algorithms to facilitate dream states and lucid dreaming”, [0375]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Coleman such that the administration, using a peripheral stimulation device, of a peripheral stimulation to the subject, the peripheral stimulation defined by a peripheral stimulation pattern, as taught by Coleman, in the alternative embodiment of a sleep monitoring and aid device for the benefit of “providing an overall solution that adapts sleep algorithms to particular users” and “facilitating dream states and lucid dreaming” (Coleman: [0375]).
The modified invention of Coleman does not teach each iteration of peripheral stimulations is generated according to the artificial intelligence model configured to modify subsequent series of peripheral stimulations based on an analysis of modifications of the neural state of the subject induced by an administration of a previous series of peripheral stimulations.
However, Coleman additionally teaches “[w]orkflows that include machine learning and other statistical methods that determine if specific rules are effective and need to be updated as well as the updating process” ([0196]), “variations to rules are tested with a user to see if the behaviour is desired” ([0196]) ([0197]; [0199]) for optimization ([0393]), and each iteration of the peripheral stimulation pattern ([0052] – [0053], “For instance the system platform could help to make distracting advertisements or messaging disappear when the user is trying to work and are having trouble concentrating. Detection for such distractions or interruptions may be performed through theta band phase locking for visual and auditory stimulus.”, [0393]) is generated according to an artificial intelligence model ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) configured to modify subsequent series of peripheral stimulations ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) based on an analysis of modifications of the neural state of the subject induced by an administration of a previous series of peripheral stimulations ([0052] – [0053], “For instance the system platform could help to make distracting advertisements or messaging disappear when the user is trying to work and are having trouble concentrating. Detection for such distractions or interruptions may be performed through theta band phase locking for visual and auditory stimulus.”, [0393]) ([0065]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Coleman to update and modify processes using machine learning, as taught by Coleman, for the benefit of achieving a desired behaviour (Coleman: [0196]) and providing a brainwave focus disrupter/encourager (Coleman: [0393]).
Regarding claim 9, Coleman teaches all limitations of claim 8. Coleman teaches the neural activity sensor (“electroencephalogram ("EEG")”, [0004], [0009], [0049]) is selected from at least one electroencephalographic (EEG) electrode (“electroencephalogram ("EEG")”, [0004], [0009], [0049]), at least one single neuron recording electrode, at least one electrocorticography (ECoG) electrode, a functional magnetic resonance imaging (fMRI) scanner, a magnetoencephalographic (MEG) magnetometer, and at least one functional optical coherence tomography (fOCT) sensor.
Regarding claim 10, Coleman teaches all limitations of claim 9. Coleman teaches the peripheral stimulation device is selected from a pressure stimulation device, a vibrational stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]), an auditory stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]), a visual stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]), and any combination thereof ([0052] – [0053]).
Regarding claim 11, Coleman teaches all limitations of claim 10. Coleman teaches
transforming, using the computing device, the plurality of baseline neural activity signals ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) into a peripheral stimulation pattern ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) according to the artificial intelligence model ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]).
Coleman does not teach the general embodiment further comprises reconfiguring, using the artificial intelligence model, the plurality of baseline peripheral stimulations based on changes in the plurality of neural state signals.
However, Coleman does teach specific embodiment ([0393]) which comprises reconfiguring, using the artificial intelligence model (“allows for intelligent intervention that can help a user filter out distracting information from the user's environment”, [0393]), the plurality of baseline peripheral stimulations (“visual and auditory stimulus”, [0393]) based on changes in the plurality of neural state signals (“a change in their data sources or algorithms. This could create feedback loops”, [0229]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Coleman such that the transforming step further comprises reconfiguring, using the artificial intelligence model, the plurality of peripheral stimulations based on changes in the plurality of neural state signals, as taught by Coleman, for the benefit of using the supervised machine learning to extract classify desired features ([0226] – [0229]) and in this case, achieving a desired behaviour through a brainwave focus disrupter/encourager (Coleman: [0393]).
Regarding claim 12, Coleman teaches all limitations of claim 11. Coleman teaches the artificial intelligence model is a genetic algorithm (“genetic algorithm”, [0200], [0223], [0276]).
Regarding claim 13, Coleman teaches at least one non-transitory computer-readable storage media (“storage media, computer storage media, or data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, tape, and other forms of computer readable media”, [0422]) having computer- executable instructions (“computer readable/executable instruction”, [0422]) embodied thereon, wherein when executed by at least one processor (“a central processing unit ("CPU") 502”, [0424]), the computer-executable instructions cause the at least one processor (502) to:
receive a target neural state ([0059]; “important neural correlate of mediation expertise”, [0063]; “determine the general signal characteristics of the target brain states of interest”, [0165]; “user to choose a target brain state or mood”, [0384]) from an operator (“operator”, [0424]) of the system ([0007], [0013], [0424]);
receive a plurality of baseline neural activity signals ([0139]) indicative of a baseline neural state of the subject from a neural activity sensor (“electroencephalogram (“EEG”)”, [0004], [0009], [0049]);
transform the plurality of baseline neural activity signals ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) into a peripheral stimulation pattern ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) according to an artificial intelligence model ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]);
operate a peripheral stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]) to administer a peripheral stimulation ([0052] – [0053]) to the subject, the peripheral stimulation defined by the peripheral stimulation pattern ([0393]);
receive a plurality of modified neural activity signals ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065], [0388]) indicative of a modified neural state of the subject ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065], [0388]) from the neural activity sensor (“electroencephalogram (“EEG”)”, [0004], [0009], [0049]); and
iteratively modify the peripheral stimulation pattern ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) to match the modified neural state of the subject to the target neural state ([0065], [0388])
wherein the computer-executable instructions (“computer readable/executable instruction”, [0422]) are configured to modify the baseline neural state (“Based on these thresholds, an estimate of the person's emotional state is determined.”, [0244]) to the target neural state without volitional input from the subject (“The Machine Learning module of the system platform adjusts the thresholds based on the user's information. The user's heart rate and EMG features are used to determine if the user is under low, medium or high stress.”, [0246]; [0139]).
The modified Coleman does not teach each iteration of peripheral stimulations is generated according to the artificial intelligence model configured to modify a subsequent series of peripheral stimulations based on an analysis of modifications of the neural state of the subject induced by an administration of a previous series of peripheral stimulations.
However, Coleman additionally teaches “[w]orkflows that include machine learning and other statistical methods that determine if specific rules are effective and need to be updated as well as the updating process” ([0196]), “variations to rules are tested with a user to see if the behaviour is desired” ([0196]) ([0197]; [0199]) for optimization ([0393]), and each iteration of the peripheral stimulation pattern ([0052] – [0053], “For instance the system platform could help to make distracting advertisements or messaging disappear when the user is trying to work and are having trouble concentrating. Detection for such distractions or interruptions may be performed through theta band phase locking for visual and auditory stimulus.”, [0393]) is generated according to the artificial intelligence model ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) trained to modify a subsequent series of peripheral stimulations ("saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals", [0065]) based on an analysis of modifications of the neural states of the subject induced by an administration of a previous series of peripheral stimulations ([0052] – [0053], “For instance the system platform could help to make distracting advertisements or messaging disappear when the user is trying to work and are having trouble concentrating. Detection for such distractions or interruptions may be performed through theta band phase locking for visual and auditory stimulus.”, [0393]) ([0065]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the storage media of Coleman to update and modify processes using machine learning, as taught by Coleman, for the benefit of achieving a desired behaviour (Coleman: [0196]) and providing a brainwave focus disrupter/encourager (Coleman: [0393]).
Regarding claim 14, Coleman teaches all limitations of claim 13. Coleman teaches the neural activity sensor is selected from at least one electroencephalographic (EEG) electrode (“electroencephalogram ("EEG")”, [0004], [0009], [0049]), at least one single neuron recording electrode, at least one electrocorticography (ECoG) electrode, a functional magnetic resonance imaging (fMRI) scanner, a magnetoencephalographic (MEG) magnetometer, and at least one functional optical coherence tomography (fOCT) sensor.
Regarding claim 15, Coleman teaches all limitations of claim 14. Coleman teaches the peripheral stimulation device is selected from a pressure stimulation device, a vibrational stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]), an auditory stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]), a visual stimulation device (“user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, [0052]), and any combination thereof ([0052] – [0053]).
Regarding claim 17, Coleman teaches all limitations of claim 15. Coleman teaches the artificial intelligence model is a genetic algorithm (“genetic algorithm”, [0200], [0223], [0276]).
Response to Arguments
Applicant’s arguments, see page 7, filed 15 December 2025, with respect to the claim objection has been fully considered and is persuasive. The claim objection for claims 13 of 15 July 2025 has been withdrawn.
Applicant’s arguments, see page 7, filed 15 December 2025, with respect to 35 U.S.C. 112(b) rejection for claims 11 – 12 (claim 11 below) have been fully considered but are not persuasive.
Claim 11 recites the limitation "the plurality of neural state signals" in line 5. There is insufficient antecedent basis for this limitation in the claim. Examiner suggests to amend to --the plurality of neural activity signals-- if that is the applicant’s intention.
Applicant’s arguments, see page 7, filed 15 December 2025, with respect to the rest of the 35 U.S.C. 112(b) rejections have been fully considered and are persuasive. The rest of the 35 U.S.C. 112(b) rejections of 15 July 2025 for claims 8, 11 and 16 have been withdrawn.
Applicant's arguments see pages 14 - 15, filed 15 December 2025, have been fully considered but they are not persuasive. Applicant contends “The machine learning model described in Coleman, trained to classify a user's mood or other neural state based on measured EEG and other physiological measurements, is not the same as the artificial intelligence model trained to iteratively generate modifications to a peripheral stimulation pattern classification of previously observed changes in a subject EEGs due to previous modifications to the peripheral stimulation pattern, as is required by independent claims 1, 8, and 13.”. However, Coleman teaches “[w]orkflows that include machine learning and other statistical methods that determine if specific rules are effective and need to be updated as well as the updating process” ([0196]), “variations to rules are tested with a user to see if the behaviour is desired” ([0196]) ([0197]; [0199]) for optimization ([0393]), which reads on “artificial intelligence model trained to iteratively generate modifications to a peripheral stimulation pattern classification of previously observed changes in a subject EEGs due to previous modifications to the peripheral stimulation pattern”. The machine learning of Coleman is trained to iteratively generate modifications (“updated as well as the updating process” ([0196]) to a peripheral stimulation pattern classification of previously observed changes in a subject EEGs (“saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals”, [0065]).
Applicant's arguments see page 15, filed 15 December 2025, have been fully considered but they are not persuasive. Applicant contends “Coleman fails to describe monitoring EEG signals after administering modified peripheral stimulation patterns as is required in independent claims 1, 8, and 13” and “None of these modifications are the same as the iterative modification of peripheral stimulation patterns as is required by claims 1, 8, and 13”. However, Coleman discloses monitoring EEG signals (“saving EEG data along with application state to allow a machine learning algorithm to optimize the methods that transform the user's brainwaves into usable control signals”, [0065], [0388]) after administering modified peripheral stimulation patterns (“The system uses EEG and other biological data to understand the user's emotional state and adjust the challenge of the learning paradigm according to the user's skill level.”, [0388]). Coleman teaches “[w]orkflows that include machine learning and other statistical methods that determine if specific rules are effective and need to be updated as well as the updating process” ([0196]), “variations to rules are tested with a user to see if the behaviour is desired” ([0196]) ([0197]; [0199]) for optimization ([0393]). When the workflow is updated, this reads on “monitoring EEG signals after administering modified peripheral stimulation patterns” and “iterative modification of peripheral stimulation patterns” for optimization ([0393]), which depicts the artificial intelligence is trained to iteratively modify peripheral stimulation patterns based on changes in measured EEG signals.
Applicant's arguments see page 15, filed 15 December 2025, have been fully considered but they are not persuasive. Applicant contends “all of the modifications in the neural states of the subjects as described in Coleman require volitional input from the subjects” and “These modifications in neural states disclosed in Colman require volitional input from the subject and are not the same as modifying the baseline neural state to the target neural state without volitional input from the subject as is required by claims 1, 8, and 13”. However, Coleman discloses “The user's heart rate and EMG features are used to determine if the user is under low, medium or high stress. This is used to label segments of EEG data. The machine learning module then uses these labels to extract features from the EEG signal and learn a model that can predict emotions directly from the EEG signal without processing additional data” ([0246], [0139) and “Workflows that include machine learning and other statistical methods that determine if specific rules are effective and need to be updated as well as the updating process” ([0196]), “variations to rules are tested with a user to see if the behaviour is desired” ([0196]) ([0197]; [0199]) for optimization ([0393]). This, in combination, reads on “modifying the baseline neural state to the target neural state without volitional input from the subject”. Furthermore, the claim states “without volitional input from the subject” and that does not narrow/define what the claim is intending “without volitional input” to be defined as.
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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/JULIE THI TRAN/Examiner, Art Unit 3791 /ALEX M VALVIS/Supervisory Patent Examiner, Art Unit 3791