DETAILED ACTION
Claims 1-20 filed December 11th 2025 are pending in the current 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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-9 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim1, 3-5 of U.S. Patent No. 11,550,391. Although the claims at issue are not identical, they are not patentably distinct from each other because the current set of claims is broader than the mapped claims.
Current application
US 11,550,391
1. A method of controlling a device, comprising: measuring or monitoring neural-related signals of a subject using a neural interface; feeding the neural-related signals measured to a machine-learning classifier; classifying, using the machine-learning classifier, the neural-related signals into one or more events, wherein the one or more events comprise at least one of a desynchronization event, a rebound event, or a rest event; and selecting an input command to be transmitted to the device based on the events classified by the machine-learning classifier.
1. A method of controlling a device, comprising: detecting a reduction in an intensity of a neural-related signal of a subject below a baseline level measured, wherein the neural-related signal of the subject is a neural oscillation of the subject, and wherein detecting the reduction in the intensity of the neural-related signal comprises detecting a decrease in a power of the neural oscillation below a baseline oscillation power level, wherein the power is a power spectral density, wherein the reduction in the intensity of the neural-related signal is caused by the subject conjuring and holding a task-relevant thought; detecting an increase in the intensity of the neural-related signal beyond the baseline level following the reduction, wherein the increase in the intensity of the neural-related signal is caused by the subject mentally releasing the task-relevant thought; and transmitting an input command to the device upon or following the detection of the increase in the intensity of the neural-related signal, wherein the input command is a command to the device to accomplish at least part of a task associated with the task-relevant thought.
3. The method of claim 1, wherein the neural-related signal is measured or monitored using an endovascular device implanted within the subject, and wherein the steps of detecting the reduction or increase in the intensity of the neural-related signal and transmitting the input command are performed using one or more processors.
4. The method of claim 3, further comprising: filtering, using one or more processors of an apparatus implanted within the subject, raw neural-related signals obtained from the endovascular device using one or more software filters; and feeding filtered signals into a classification layer to automatically detect the reduction and increase in the intensity of the neural-related signal using a machine learning classifier.
2. The method of claim 1, wherein selecting the input command to be transmitted to the device based on the events classified by the machine-learning classifier further comprises selecting the input command based on at least one of a sequence of events and number of events.
1. A method of controlling a device, comprising: detecting a reduction in an intensity of a neural-related signal of a subject below a baseline level measured, wherein the neural-related signal of the subject is a neural oscillation of the subject, and wherein detecting the reduction in the intensity of the neural-related signal comprises detecting a decrease in a power of the neural oscillation below a baseline oscillation power level, wherein the power is a power spectral density, wherein the reduction in the intensity of the neural-related signal is caused by the subject conjuring and holding a task-relevant thought; detecting an increase in the intensity of the neural-related signal beyond the baseline level following the reduction, wherein the increase in the intensity of the neural-related signal is caused by the subject mentally releasing the task-relevant thought; and transmitting an input command to the device upon or following the detection of the increase in the intensity of the neural-related signal, wherein the input command is a command to the device to accomplish at least part of a task associated with the task-relevant thought.
3. The method of claim 2, wherein the sequence of events is one or more desynchronization events followed by a rebound event.
1. A method of controlling a device, comprising: detecting a reduction in an intensity of a neural-related signal of a subject below a baseline level measured, wherein the neural-related signal of the subject is a neural oscillation of the subject, and wherein detecting the reduction in the intensity of the neural-related signal comprises detecting a decrease in a power of the neural oscillation below a baseline oscillation power level, wherein the power is a power spectral density, wherein the reduction in the intensity of the neural-related signal is caused by the subject conjuring and holding a task-relevant thought; detecting an increase in the intensity of the neural-related signal beyond the baseline level following the reduction, wherein the increase in the intensity of the neural-related signal is caused by the subject mentally releasing the task-relevant thought; and transmitting an input command to the device upon or following the detection of the increase in the intensity of the neural-related signal, wherein the input command is a command to the device to accomplish at least part of a task associated with the task-relevant thought.
4. The method of claim 2, wherein the sequence of events is a rebound event followed by one or more desynchronization events.
1. A method of controlling a device, comprising: detecting a reduction in an intensity of a neural-related signal of a subject below a baseline level measured, wherein the neural-related signal of the subject is a neural oscillation of the subject, and wherein detecting the reduction in the intensity of the neural-related signal comprises detecting a decrease in a power of the neural oscillation below a baseline oscillation power level, wherein the power is a power spectral density, wherein the reduction in the intensity of the neural-related signal is caused by the subject conjuring and holding a task-relevant thought; detecting an increase in the intensity of the neural-related signal beyond the baseline level following the reduction, wherein the increase in the intensity of the neural-related signal is caused by the subject mentally releasing the task-relevant thought; and transmitting an input command to the device upon or following the detection of the increase in the intensity of the neural-related signal, wherein the input command is a command to the device to accomplish at least part of a task associated with the task-relevant thought.
5. The method of claim 1, wherein the desynchronization event is a decrease in an intensity of a neural-related signal below a baseline level.
1. A method of controlling a device, comprising: detecting a reduction in an intensity of a neural-related signal of a subject below a baseline level measured, wherein the neural-related signal of the subject is a neural oscillation of the subject, and wherein detecting the reduction in the intensity of the neural-related signal comprises detecting a decrease in a power of the neural oscillation below a baseline oscillation power level, wherein the power is a power spectral density, wherein the reduction in the intensity of the neural-related signal is caused by the subject conjuring and holding a task-relevant thought; detecting an increase in the intensity of the neural-related signal beyond the baseline level following the reduction, wherein the increase in the intensity of the neural-related signal is caused by the subject mentally releasing the task-relevant thought; and transmitting an input command to the device upon or following the detection of the increase in the intensity of the neural-related signal, wherein the input command is a command to the device to accomplish at least part of a task associated with the task-relevant thought.
6. The method of claim 5, wherein the decrease in the intensity of the neural-related signal is a decrease in a power of a neural oscillation of the subject.
1. A method of controlling a device, comprising: detecting a reduction in an intensity of a neural-related signal of a subject below a baseline level measured, wherein the neural-related signal of the subject is a neural oscillation of the subject, and wherein detecting the reduction in the intensity of the neural-related signal comprises detecting a decrease in a power of the neural oscillation below a baseline oscillation power level, wherein the power is a power spectral density, wherein the reduction in the intensity of the neural-related signal is caused by the subject conjuring and holding a task-relevant thought; detecting an increase in the intensity of the neural-related signal beyond the baseline level following the reduction, wherein the increase in the intensity of the neural-related signal is caused by the subject mentally releasing the task-relevant thought; and transmitting an input command to the device upon or following the detection of the increase in the intensity of the neural-related signal, wherein the input command is a command to the device to accomplish at least part of a task associated with the task-relevant thought.
7. The method of claim 6, wherein the power of the neural oscillation is a power spectral density.
1. A method of controlling a device, comprising: detecting a reduction in an intensity of a neural-related signal of a subject below a baseline level measured, wherein the neural-related signal of the subject is a neural oscillation of the subject, and wherein detecting the reduction in the intensity of the neural-related signal comprises detecting a decrease in a power of the neural oscillation below a baseline oscillation power level, wherein the power is a power spectral density, wherein the reduction in the intensity of the neural-related signal is caused by the subject conjuring and holding a task-relevant thought; detecting an increase in the intensity of the neural-related signal beyond the baseline level following the reduction, wherein the increase in the intensity of the neural-related signal is caused by the subject mentally releasing the task-relevant thought; and transmitting an input command to the device upon or following the detection of the increase in the intensity of the neural-related signal, wherein the input command is a command to the device to accomplish at least part of a task associated with the task-relevant thought.
8. The method of claim 1, wherein the rebound event is an increase in an intensity of a neural-related signal above a baseline level following a desynchronization event.
5. The method of claim 1, wherein the reduction in the intensity of the neural-related signal below the baseline level measured is a desynchronization of the neural-related signal and wherein the increase in the intensity of the neural-related signal beyond the baseline level measured is a rebound of the neural-related signal.
9. The method of claim 1, further comprising filtering the neural-related signals measured prior to feeding the neural-related signals to the machine-learning classifier.
4. The method of claim 3, further comprising: filtering, using one or more processors of an apparatus implanted within the subject, raw neural-related signals obtained from the endovascular device using one or more software filters; and feeding filtered signals into a classification layer to automatically detect the reduction and increase in the intensity of the neural-related signal using a machine learning classifier.
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.
Claim(s) 1-3, 5-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Petley et al. (US2019/0166434) in view of Saab et al. (US2019/0209038)
Consider claim 1, where Petley teaches a method of controlling a device, comprising: measuring or monitoring neural-related signals of a subject using a neural interface; (See Petley ¶3 where The method comprises detecting, during a baseline period of no wearer movement, EEG signals from or proximate an ear of the wearer using the ear-worn electronic device.) feeding the neural-related signals measured to a classifier; (See Petley Fig. 5 and ¶50-53 where FIG. 5 shows a generalized data analysis pipeline configured to classify neural signals corresponding to a control movement planned, imagined, or executed by a wearer of an ear-worn electronic device.) classifying, using the classifier, the neural-related signals into one or more events, wherein the one or more events comprise at least one of a desynchronization event, a rebound event, or a rest event; (See Petley ¶31, 66-67 where the sequence of neural events that unfold with planned, imagined, or executed movements can be broadly described as follows. When movements (planned, imagined, or executed) are self-initiated, approximately two seconds prior to movement, there is a reduction in upper alpha/lower beta power in Rolandic regions contralateral (i.e., on the opposite side of the body) to the executed movement, which becomes bilateral immediately before movement execution. This transient reduction in band power is known as an event-related desynchronization (ERD). Against this background of alpha ERD, shortly before movement onset and during execution, an increase in gamma power occurs. Such a transient power increase is known as an event-related synchronization (ERS). The Common Spatial Pattern (CSP) algorithm finds filters that are optimized for the two classes of data in the training set. After applying the filter, the variance of one class will be maximized and the other will be minimized. CSP is commonly carried out using a wideband filtered EEG signal, often in the 8-30 Hz range to cover alpha and beta ERD/ERS, but can be carried out in a frequency-specific fashion, such as in the known ERDmax method. This method specifies the frequency bands and times at which ERD/ERS are expected to derive CSP filters that maximize these power fluctuations.) and selecting an input command to be transmitted to the device based on the events classified by the classifier. (See Petley ¶87 where the EEG signals associated with each of the selected control movements are obtained in response to instructions and feedback delivered to the wearer by audio input and output electronics 1106, 1112 of the ear-worn electronic device 1102.)
Petley teaches a classifier, (See Petley ¶35 where computing discriminability metrics can also involve classification by one or more classifiers, for example using a linear discriminant algorithm.) however Petley does not explicitly teach a machine learning classifier. However, in an analogous field of endeavor Saab teaches a machine learning classifier. (See Saab ¶91 where BLDA classifiers, or other machine learning (i.e., neural networks, deep learning, etc.) classifiers, may be used to train both the P300 and ASSR classifiers. These labelled vectors may be used to train two separate Step-wise Linear Discriminant Analysis (SWLDA) classifiers, one for the P300 and one for the ASSR) Therefore, it would have been obvious to one of ordinary skill in the art that the classifier using a linear discriminant algorithm of Petley would be considered a machine learning classifier as taught by Saab. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known methods to yield the intended results.
Consider claim 2, where Petley in view of Saab teaches the method of claim 1, wherein selecting the input command to be transmitted to the device based on the events classified by the machine-learning classifier further comprises selecting the input command based on at least one of a sequence of events and number of events. (See Petley ¶29-31 where the sequence of neural events that unfold with planned, imagined, or executed movements can be broadly described as follows. When movements (planned, imagined, or executed) are self-initiated, approximately two seconds prior to movement, there is a reduction in upper alpha/lower beta power in Rolandic regions contralateral (i.e., on the opposite side of the body) to the executed movement, which becomes bilateral immediately before movement execution. This transient reduction in band power is known as an event-related desynchronization (ERD). Against this background of alpha ERD, shortly before movement onset and during execution, an increase in gamma power occurs. the motor BCI can be configured to use a combination of imagined, planned, and executed movements as control signals.)
Consider claim 3, where Petley in view of Saab teaches the method of claim 2, wherein the sequence of events is one or more desynchronization events followed by a rebound event. (See Petley ¶31 where This transient reduction in band power is known as an event-related desynchronization (ERD). Against this background of alpha ERD, shortly before movement onset and during execution, an increase in gamma power occurs. Such a transient power increase is known as an event-related synchronization (ERS).)
Consider claim 5, where Petley in view of Saab teaches the method of claim 1, wherein the desynchronization event is a decrease in an intensity of a neural-related signal below a baseline level. (See Petley ¶31-34, 86 where the discriminability metrics indicate how discriminable neural signals associated with the candidate control movements and the baseline period are from one another. The method of FIG. 1 further involves computing 110 discriminability metrics for the candidate control movements, both versus each other and versus a non-movement baseline period.)
Consider claim 6, where Petley in view of Saab teaches the method of claim 5, wherein the decrease in the intensity of the neural-related signal is a decrease in a power of a neural oscillation of the subject. (See Petley ¶31 where when movements (planned, imagined, or executed) are self-initiated, approximately two seconds prior to movement, there is a reduction in upper alpha/lower beta power in Rolandic regions contralateral (i.e., on the opposite side of the body) to the executed movement, which becomes bilateral immediately before movement execution. This transient reduction in band power is known as an event-related desynchronization (ERD).)
Consider claim 7, where Petley in view of Saab teaches the method of claim 6, wherein the power of the neural oscillation is a power spectral density. (See Petley ¶74 where a simpler method of time-frequency decomposition involves using the Welch periodgram to extract the power spectral density, which yields similar success to autoregressive and wavelet-based methods.)
Consider claim 8, where Petley in view of Saab teaches the method of claim 1, wherein the rebound event is an increase in an intensity of a neural-related signal above a baseline level following a desynchronization event. (See Petley ¶31-34, 86 where against this background of alpha ERD, shortly before movement onset and during execution, an increase in gamma power occurs. Such a transient power increase is known as an event-related synchronization (ERS).) The discriminability metrics indicate how discriminable neural signals associated with the candidate control movements and the baseline period are from one another. The method of FIG. 1 further involves computing 110 discriminability metrics for the candidate control movements, both versus each other and versus a non-movement baseline period.)
Consider claim 9, where Petley in view of Saab teaches the method of claim 1, further comprising filtering the neural-related signals measured prior to feeding the neural-related signals to the machine-learning classifier. (See Petley ¶59--60 where use of a multiplicity of candidate analysis pipelines allows the system 700 to characterize the neural signatures associated with the wearer's selected control movements, involving extraction of features in the temporal, spectral, and spatial domains. Use of a multiplicity of candidate analysis pipelines also allows the system 700 to determine the optimal feature translation algorithm, which may be an optimal method for discrete classification or an optimal continuous mapping of neural features to device control parameters (e.g., using a form of regression). Examples of the candidate spatial features include source estimation, spatial filters (e.g., Laplacian derivations, Common Spatial Patterns), independent component analysis (ICA), pooling, re-referencing, or subtraction, as well as computing indices describing the relationships between sensors such as correlation, coherence, phase differences, and measurements of laterality.)
Consider claim 10, where Petley in view of Saab teaches the method of claim 1, wherein the machine-learning classifier is a pre-trained classifier. (See Petley ¶79 where regardless of type, to achieve optimal performance, these algorithms are trained using each individual's brain data—this is because each person's brain activations are unique. Thus, the classifier is pre-trained before use)
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Petley in view of Saab as applied to claim 2 above, and further in view of Huang et al. (“Electroencephalography (EEG)-Based Brain–Computer Interface (BCI): A 2-D Virtual Wheelchair Control Based on Event-Related Desynchronization/Synchronization and State Control” IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 20, NO. 3, MAY 2012)
Consider claim 4, where Petley in view of Saab teaches the method of claim 2, wherein there is an ERD event (power decrease) followed by an ERS event (power increase) However, they do not explicitly teach wherein the sequence of events is a rebound event followed by one or more desynchronization events. However, in an analogous field of endeavor Huang teaches wherein the sequence of events is a rebound event followed by one or more desynchronization events. (See Huang page 381 and Fig. 2 where the Strategy of wheelchair control. (a)–(d) Follow the time sequence. (a) In stop state, the wheelchair is keeping still. (b) In the first cue period T1, subjects start any of the motor tasks; from top to bottom are the four situations: right wrist extension-left hemisphere ERD pattern, right wrist extension-left hemisphere ERD pattern, left wrist extension-right hemisphere ERD pattern or no motor task-Idle/baseline activity. (c) In the second cue period T2, subjects continue with the task: continue right wrist extension-left hemisphere ERD pattern, stop right wrist extension and relax-left hemisphere ERS, stop left wrist extension and relax-right hemisphere ERS, or no motor task-baseline activity. (d) Movement intention is decoded and wheelchair is driven to move forward (or stop when moving), turn right, turn left or keep current moving status. Inter-trial interval (the end of T2 to the beginning of next T1) is 2 s. Thus, after a detection of an ERS in T2, the next ERD may be detected in T1.) Therefore, it would have been obvious for one of ordinary skill in the art to modify the ERD/ERS event detection of Petley to continually read these events as taught by Huang. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of performing a series of commands using the existing ERD->ERS pattern.
Claim(s) 11-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Petley et al. (US2019/0166434) in view of Segal (US2015/0091791)
Consider claim 11, where Petley teaches a method of controlling a device, comprising: detecting one or more desynchronization events based on neural-related signals of a subject measured using a neural interface; (See Petley ¶29-31 where the sequence of neural events that unfold with planned, imagined, or executed movements can be broadly described as follows. When movements (planned, imagined, or executed) are self-initiated, approximately two seconds prior to movement, there is a reduction in upper alpha/lower beta power in Rolandic regions contralateral (i.e., on the opposite side of the body) to the executed movement, which becomes bilateral immediately before movement execution. This transient reduction in band power is known as an event-related desynchronization (ERD). Against this background of alpha ERD, shortly before movement onset and during execution, an increase in gamma power occurs. the motor BCI can be configured to use a combination of imagined, planned, and executed movements as control signals.) detecting a rebound event based on the neural-related signals measured by the neural interface; (See Petley ¶31 where This transient reduction in band power is known as an event-related desynchronization (ERD). Against this background of alpha ERD, shortly before movement onset and during execution, an increase in gamma power occurs. Such a transient power increase is known as an event-related synchronization (ERS).) and transmitting the input command to the device upon or following the detection of the rebound event. (See Petley ¶31, 38 where after completion of the candidate control movement, the EEG signals acquired by the ear-worn electronic device 202 are communicated to the processor-based system 204 and stored in a memory of the processor-based system 204.)
Petley teaches a desynchronization event and discriminability metrics, (See Petley ¶86 where at least one of the processors 1104, 1152, and 1162 is also configured to select a subset of the candidate control movements using the discriminability metrics, wherein each of the selected control movements defines a neural command for controlling the ear-worn electronic device 1102 by the wearer.) however Petley does not explicitly teach determining a duration of at least one of the desynchronization events; selecting an input command to be transmitted to the device based on the duration of the desynchronization event. However, in an analogous field of endeavor Segal teaches determining a duration of at least one of the desynchronization events; (See Segal ¶40 where Movement or preparation for movement is typically accompanied by a decrease in mu and beta rhythms, particularly contralateral to the movement. This decrease has been labeled "event-related desynchronization" or ERD. The opposite, rhythm increase, or "event-related synchronization" (ERS) occurs after movement and with relaxation.) selecting an input command to be transmitted to the device based on the duration of the desynchronization event. (See Segal ¶68 where in certain embodiments of the present invention, directional or other recognized intentions may have a depth. As used in this context, depth may mean the length or duration of a thought. For example, depth may include the user thinking "up" persistently for more than one second, or multiple seconds. In this context, and according to certain embodiments of the present invention, the directional intention may correlate to a first condition or action if imagined for one second, and may correlate to a second condition or action if imaged for more than one second (e.g., two or three seconds)). Therefore, it would have been obvious for one of ordinary skill in the art that the discriminability metrics of Petley may use the duration of a movement as a metric as taught by Segal. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known parameters for the purposes of discriminating between commands.
Consider claim 12, where Petley in view of Segal teaches the method of claim 11, wherein the desynchronization event is a decrease in an intensity of the neural-related signals below a baseline level. (See Petley ¶31-34, 86 where the discriminability metrics indicate how discriminable neural signals associated with the candidate control movements and the baseline period are from one another. The method of FIG. 1 further involves computing 110 discriminability metrics for the candidate control movements, both versus each other and versus a non-movement baseline period.)
Consider claim 13, where Petley in view of Segal teaches the method of claim 12, wherein the decrease in the intensity of the neural-related signals is a decrease in a power of a neural oscillation of the subject. (See Petley ¶31 where when movements (planned, imagined, or executed) are self-initiated, approximately two seconds prior to movement, there is a reduction in upper alpha/lower beta power in Rolandic regions contralateral (i.e., on the opposite side of the body) to the executed movement, which becomes bilateral immediately before movement execution. This transient reduction in band power is known as an event-related desynchronization (ERD).)
Consider claim 14, where Petley in view of Segal teaches the method of claim 13, wherein the power of the neural oscillation is a power spectral density. (See Petley ¶74 where a simpler method of time-frequency decomposition involves using the Welch periodgram to extract the power spectral density, which yields similar success to autoregressive and wavelet-based methods.)
Consider claim 15, where Petley in view of Segal teaches the method of claim 11, wherein the one or more desynchronization events are caused by the subject conjuring and holding a task-relevant or task-irrelevant thought. (See Petley ¶31 where when movements (planned, imagined, or executed) are self-initiated, approximately two seconds prior to movement, there is a reduction in upper alpha/lower beta power in Rolandic regions contralateral (i.e., on the opposite side of the body) to the executed movement, which becomes bilateral immediately before movement execution. This transient reduction in band power is known as an event-related desynchronization (ERD). Thus, the movement of an arm is a task-relevant thought)
Consider claim 16, where Petley in view of Segal teaches the method of claim 11, wherein the rebound event is caused by the subject mentally releasing a task-relevant thought or a task-irrelevant thought. (See Segal ¶40 where "event-related synchronization" (ERS) occurs after movement and with relaxation.)
Consider claim 17, where Petley in view of Segal teaches the method of claim 11, wherein the device is at least one of a personal computing device, an internet-of-things (IoT) device, and a mobility vehicle. (See Segal ¶8 where command to something as simple as a computer cursor or mouse pointer to something physical, such as a wheelchair, car or other device, or can use the thought as a component into an output of item of information.)
Consider claim 18, where Petley in view of Segal teaches the method of claim 11, further comprising filtering the neural-related signals using one or more software filters. (See Petley ¶59--60 where use of a multiplicity of candidate analysis pipelines allows the system 700 to characterize the neural signatures associated with the wearer's selected control movements, involving extraction of features in the temporal, spectral, and spatial domains. Use of a multiplicity of candidate analysis pipelines also allows the system 700 to determine the optimal feature translation algorithm, which may be an optimal method for discrete classification or an optimal continuous mapping of neural features to device control parameters (e.g., using a form of regression). Examples of the candidate spatial features include source estimation, spatial filters (e.g., Laplacian derivations, Common Spatial Patterns), independent component analysis (ICA), pooling, re-referencing, or subtraction, as well as computing indices describing the relationships between sensors such as correlation, coherence, phase differences, and measurements of laterality.)
Claim(s) 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Petley in view of Saab as applied to claim 18 above, and further in view of Saab
Consider claim 19. The method of claim 18, further comprising feeding the filtered signals to a classifier to detect the one or more desynchronization events and the rebound event. (See Petley Fig. 5 and ¶50-53 where FIG. 5 shows a generalized data analysis pipeline configured to classify neural signals corresponding to a control movement planned, imagined, or executed by a wearer of an ear-worn electronic device.)
Petley teaches a classifier, (See Petley ¶35 where computing discriminability metrics can also involve classification by one or more classifiers, for example using a linear discriminant algorithm.) however Petley does not explicitly teach a machine learning classifier. However, in an analogous field of endeavor Saab teaches a machine learning classifier. (See Saab ¶91 where BLDA classifiers, or other machine learning (i.e., neural networks, deep learning, etc.) classifiers, may be used to train both the P300 and ASSR classifiers. These labelled vectors may be used to train two separate Step-wise Linear Discriminant Analysis (SWLDA) classifiers, one for the P300 and one for the ASSR) Therefore, it would have been obvious to one of ordinary skill in the art that the classifier using a linear discriminant algorithm of Petley would be considered a machine learning classifier as taught by Saab. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known methods to yield the intended results.
Consider claim 20, where Petley in view of Segal in view of Saab teaches the method of claim 19, wherein the machine-learning classifier is a pre-trained classifier. (See Petley ¶79 where regardless of type, to achieve optimal performance, these algorithms are trained using each individual's brain data—this is because each person's brain activations are unique. Thus, the classifier is pre-trained before use)
Conclusion
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WILLIAM LU
Primary Examiner
Art Unit 2624
/WILLIAM LU/Primary Examiner, Art Unit 2624
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