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 .
Status of Claims
Claims 1-6, 8-9, and 11-22 are pending in response to the claim amendments and remarks filed on 02/23/2026.
Response to Applicant’s Remarks
With respect to the 102 rejections of claims 1-6, 8-9, 11-15, and 20:
Applicant’s remarks filed 02/23/2026 with respect to claims 1-6, 8-9, 11-15, and 20 have been fully considered but are not persuasive. Applicant argues that Edelman fails to teach real-time source imaging, instead relying on post-processing of EEG…would not be considered real-time. Applicant further argues that Edelman fails to teach continuous movement of an external device. See Remarks at 1.
Examiner disagrees.
Claim 1 does not recite “continuous movement of “the [external] device”. Rather, claim 1 recites “the [external] device is controlled through continuous movement”. Applicant’s discloser teaches the context of continuous movement in two different ways [0006] as follows:
In the context of continuous pursuit of task:
[0035] Noninvasive Continuous Virtual Target Tracking Via Motor Intent
[0036] Throughout all experimental sessions, users were instructed to control the trajectory of a virtual cursor using motor imagination (MI) tasks; left- and right-hand MI for the corresponding left and right movement, and both hands MI and rest for up and down movement, respectively.
In the context of continuous movement of a device:
[0063] FIGS. 6A-6E. Source-Based CP BCI Robotic Arm Control. FIG. 6A: Robotic arm CP BCI setup. Users controlled the 2D continuous movement of a seven degree-of-freedom robotic arm to track a randomly moving target on a computer screen.
Thus, Examiner interprets that Edelman teaches controlling the external device based on the user performing a task (Edelman teaches addressing the issues for SMR BCI by matching the MI tasks that generate distinct usable control signals to the action of the output device, page 5 column 2; a human subject [with BCI experience] continuously performs the specified MI task, fig. 1(a) and (b) and page 5, column 2; SMR BCIs1, control of output device based on motor imagery (MI) tasks…motor control of the hand which could be applied to both rehabilitative and prosthetic control purposes, page 5). Adelman teaches that the Brain-Computer Interfaces (BCI) are paired with the EEG source imaging (ESI) (See Edelman: ESI is able to enhance BCI performance of decoding complex righthand motor imagery tasks, page 4 under Abstract-Conclusion), thus teaches a real-time source imaging. Such that BCI and ESI together maps the brain’s electrical activity live as the subject performs physical actions as results are available to provide neurofeedback reflective of the user’s intent and/or to control the output device (See Edelman: SMR BCIs2, control of output device based on motor imagery (MI) tasks…motor control of the hand which could be applied to both rehabilitative and prosthetic control purposes, page 5; human subjects to perform continuous specified Motor Imaginary {MI} tasks [via BCI}, p.5, column 2; noninvasive BCI to obtain user’s motor intent through signals detected at the scalp…SMR BCIs3 incorporated into real life situations to control devices such as wheelchairs, limb orthoses, or quadcopters, See Abstract and p.11 section IV). For this reason, Examiner will maintain the 102 rejections for claims 1-6, 8-9, 11-15 as set forth below.
With respect to the 103 rejections of claims 16-19 and 21-22:
Applicant’s remarks filed 02/23/2026 with respect to claims 16-19 and 21-22 have been fully considered but are not persuasive. Applicant argues that Edelman fails to teach real-time source imaging, instead relying on post-processing of EEG data to determine cortical source and further fails to teach continuous movement of a device and control of devices. See Remarks at 2.
Examiner disagrees.
Independent claims 16 and 22 do not recite a controlling of any device, except for the preamble4 (i.e., training a user to control an external device). Claims 16 and 22 do recite the limitation “directing the user to engage in a continuous pursuit task… obtaining a plurality of signals originating in a brain of the user while the user engages in the continuous pursuit task” but is not to be interpreted as the equivalent of “controlling the external device using the control signal, wherein the device is controlled through continuous movement” as being recited in claim 1. Nonetheless, Adelman teaches controlling of an external device (SMR BCIs5, control of output device based on motor imagery (MI) tasks…motor control of the hand which could be applied to both rehabilitative and prosthetic control purposes, page 5). Adelman teaches that the Brain-Computer Interfaces (BCI) are paired with the EEG source imaging (ESI) (See Edelman: ESI is able to enhance BCI performance of decoding complex righthand motor imagery tasks, page 4 under Abstract-Conclusion), thus teaches a real-time source imaging. Such that BCI and ESI together maps the brain’s electrical activity live as the subject performs physical actions as results are available to provide neurofeedback reflective of the user’s intent and/or to control the output device (See Edelman: SMR BCIs6, control of output device based on motor imagery (MI) tasks…motor control of the hand which could be applied to both rehabilitative and prosthetic control purposes, page 5; human subjects to perform continuous specified Motor Imaginary {MI} tasks [via BCI}, p.5, column 2; noninvasive BCI to obtain user’s motor intent through signals detected at the scalp…SMR BCIs7 incorporated into real life situations to control devices such as wheelchairs, limb orthoses, or quadcopters, See Abstract and p.11 section IV). For this reason, Examiner will maintain the 103 rejections for claims 16-19 and 21-22 as set forth below. Note: See Interview Summary for details on proposed claim amendments of claims 16-19 and 21-22.
Claim Objections
Claim 1 as amended on 02/23/2026 is objected to because of the following informality: The following underlining element “wherein the device is controlled through continuous movement” fails to have sufficient antecedent basis.
Appropriate corrections are required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4-6, 8-9, 11-14, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Edelman et al. (“EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks”, Jan 2016, IEEE, p.4-14).
With respect to claim 1, Edelman teaches a method of controlling an external device through a brain-computer interface (sensorimotor-based brain-computer interfaces (BCIs) to control of real and virtual devices, abstract; BCI to control prosthetic arm using MI signals, p.5) comprising:
non-invasively obtaining a plurality of signals originating in a brain of a user while the user performs a task, wherein the plurality of signals are related to the user's mental intent (noninvasive BCI to obtain user’s motor intent through signals detected at the scalp…SMR BCIs incorporated into real life situations to control devices such as wheelchairs, limb orthoses, or quadcopters, See Abstract and p.11 section IV);
identifying the plurality of signals originating from regions of the brain associated with imagery tasks by estimating neural sources generating the plurality of signals through real-time source imaging (EEG source imaging (ESI) as depicts in fig.2, with ESI to discriminate Motor Imaginary {MI} tasks by reconstructing electrical activity on the cortical surface to isolate regions containing discriminable information regarding specific MI tasks, p.5, section I, col.2 and p.6, section D Region of Interest Selection);
analyzing the plurality of signals by processing the plurality of signals in the temporal, spatial, and spectral domains (time-frequency analysis, feature extraction, and classification, fig.2 and p.5-10, includes features traced back to location in corresponding spatial, temporal, and spectral domains to observe signals responsible for separating each task, p.10 section B and col.2);
extracting a control signal from the analyzed plurality of signals (extracting control signals that are more intuitive for a user to perform [tasks], p.5 col.1);
controlling the external device using the control signal (using motor cortex (MI) signals to control prosthetic arm control, p.5 col.1), wherein the device is controlled through continuous movement (addressing the issues for SMR BCI by matching the MI tasks that generate distinct usable control signals to the action of the output device, page 5 column 2; a human subject [with BCI experience] continuously performs the specified MI task, fig. 1(a) and (b) and page 5, column 2; SMR BCIs8, control of output device based on motor imagery (MI) tasks…motor control of the hand which could be applied to both rehabilitative and prosthetic control purposes, page 5).
With respect to claim 2, Edelman teaches wherein the plurality of signals is obtained via electroencephalography (EEG source imaging, fig.2 and p.1-13).
With respect to claim 4, Edelman teaches wherein the plurality of signals is selected from the group consisting of electrical, magnetic, or hemodynamic signals (EEG source imaging, fig.2 and p.1-13).
With respect to claim 5, Edelman teaches wherein the external device is selected from a group consisting of a computer, robotic device, a neuroprosthetic limb, a wheelchair, a drone, a smartphone, or an assistive device (wheelchairs, limb orthoses, or quadcopters, p.11 section IV).
With respect to claim 6, Edelman teaches wherein identifying the plurality of signals originating from regions of the brain associated with imagery tasks further comprises: estimating neural sources generating the plurality of signals through real-time source imaging using a forward head model to mitigate the effects of volume conduction (solving the EEG forward model to describe the electrical conductance between the scalp sampling sites and the modeled dipolar sources of the brain, p.6, section C of col.1).
With respect to claim 8, Edelman teaches wherein the real-time source imaging comprises real-time electrical source imaging (EEG source imagining detection, fig.2, to interpret user’s motor intent…incorporated into many real-life situations to control devices, p.11 section IV; as such EEG source imagining detection localize and visualize neural activity within the brain as it happens in “real-time”).
With respect to claim 9, Edelman teaches further comprising: isolating and evaluating sensor and source signals during online processing (EEG source imaging (ESI) as depicts in fig.2, with ESI to discriminate MI tasks by reconstructing electrical activity on the cortical surface to isolate regions containing discriminable information regarding specific MI tasks, p.5, section I, col.2 and p.6, section D Region of Interest Selection).
With respect to claim 11, Edelman teaches wherein analyzing the plurality of signals comprises: decoding the user's mental intent or state based on the spatio-temporal-spectral signatures contained within the plurality of signals (time-frequency analysis, feature extraction, and classification, fig.2 and p.5-10, includes features traced back to location in corresponding spatial, temporal, and spectral domains to observe signals responsible for separating each task, p.10 section B and col.2).
With respect to claim 12, Edelman teaches wherein the plurality of signals is processed to identify brain signals representing a user's motor intention (noninvasive BCI to obtain user’s motor intent through signals detected at the scalp…SMR BCIs incorporated into real life situations to control devices such as wheelchairs, limb orthoses, or quadcopters, p.11 section IV).
With respect to claim 13, Edelman teaches wherein analyzing the plurality of signals by processing the plurality of signals in the temporal, spatial, and spectral domains comprise: extracting spatio-temporal-spectral features from the plurality of signals (time-frequency analysis, feature extraction, and classification, fig.2 and p.5-10, includes features traced back to location in corresponding spatial, temporal, and spectral domains to observe signals responsible for separating each task, p.10 section B and col.2); and identifying the control signal using linear or non-linear classifiers (Gaussian kernel with Mahalanobis distance metric, p.8 col.1 as further seen in fig.4, is interpreted as a non-linear function to calculate similarity between data points including a MD-based classifier, p.9-section F).
With respect to claim 149, Edelman teaches wherein the linear classifier can include at least one of simple linear combination of powers, linear discriminative analysis, and support vector machine with linear kernels.
With respect to claim 20, Edelman teaches further comprising: estimating a motor state or mental state using continuous pursuit signals, wherein estimating is online and adaptive (noninvasive BCI to obtain user’s motor intent through signals detected at the scalp…SMR BCIs incorporated into real life situations to control devices such as wheelchairs, limb orthoses, or quadcopters, p.11 section IV).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Edelman et al. (“EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks”, Jan 2016, IEEE, p.4-14) in view of Geva et al. (WO-2018116248-A1).
With respect to claims 3 and 15, Edelman does not appear to teach wherein the plurality of signals is obtained via magnetoencephalography and wherein the non-linear classifier can include at least one of neural networks, deep learning networks, and support vector machine with nonlinear kernels.
However, it is known by Geva to teach of a brain computer interface (BCI) having a system and method for iterative classification using neurophysiological signals (Geva: p.1 lines 10-12) comprising at least: wherein the plurality of signals is obtained via magnetoencephalography (Geva: MEG signals, p.12 line 13) and wherein the non-linear classifier can include at least one of neural networks, deep learning networks, and support vector machine with nonlinear kernels (Geva: convolutional neural network classifier providing a non-linear combination of outputs, p.15 lines 8-24).
Because Geva is also directed to a Brain Computer Interface (BCI) (Geva: BCI, p.7 lines 30-32; Edelman: sensorimotor-based BCI, abstract and p.5), it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of obtaining the plurality of signals via magnetoencephalography and wherein the non-linear classifier is of a neural networks as taught by Geva with the sensorimotor-based brain-computer interfaces (BCIs) system as taught by Edelman for the purpose of iterative classification of neurophysiological signals (Geva: p.7 lines 30-32).
Claims 16-19 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Edelman et al. (“EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks”, Jan 2016, IEEE, p.4-14) in view of Crawford et al. (KR-20150106954-A).
With respect to claim 16, Edelman teaches method of training a user to control an external device through a brain-computer interface (sensorimotor-based brain-computer interfaces (BCIs) to control of real and virtual devices, abstract; BCI to control prosthetic arm using MI signals, p.5) comprising:
directing the user to engage in a continuous pursuit task wherein a user performs motor imagination [[to chase a randomly moving target]] (human subjects to perform continuous specified Motor Imaginary {MI} tasks, p.5 section II; noninvasive BCI to obtain user’s motor intent through signals detected at the scalp…SMR BCIs incorporated into real life situations to control devices such as wheelchairs, limb orthoses, or quadcopters, See Abstract and p.11 section IV);
non-invasively obtaining a plurality of signals originating in the brain while the user engages in the continuous pursuit task, wherein the plurality of signals are related to the user’s mental intent (noninvasive BCI to obtain user’s motor intent through signals detected at the scalp…SMR BCIs incorporated into real life situations to control devices such as wheelchairs, limb orthoses, or quadcopters, See Abstract and p.11 section IV);
identifying the plurality of signals originating from regions of the brain associated with imagery tasks by estimating neural sources generating the plurality of signals through real-time source imaging (EEG source imaging (ESI) as depicts in fig.2, with ESI to discriminate Motor Imaginary {MI} tasks by reconstructing electrical activity on the cortical surface to isolate regions containing discriminable information regarding specific MI tasks, p.5, section I, col.2 and p.6, section D Region of Interest Selection); and
analyzing the plurality of signals by processing the plurality of signals by processing the plurality of signals in the temporal, spatial, and spectral domains (time-frequency analysis, feature extraction, and classification, fig.2 and p.5-10, includes features traced back to location in corresponding spatial, temporal, and spectral domains to observe signals responsible for separating each task, p.10 section B and col.2).
With respect to claim 16, Edelman does not appear to teach wherein the user performs motor imagination to chase a randomly moving target. However, it is known by Crawford to teach of a brain computer interface system and method to control a processing device (Crawford: p.5) comprising at least: wherein the user performs motor imagination to chase a randomly moving target (Crawford: users direct movement of augmented reality (AR) objects/characters through their thoughts…control AR characters that are moving or racing, p.9).
Because Crawford is also directed to a Brain Computer Interface (BCI) (Crawford: p.5; Edelman: sensorimotor-based BCI, abstract and p.5), it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of the user performs motor imagination to chase a randomly moving target as taught by Crawford with the sensorimotor-based brain-computer interfaces (BCIs) system as taught by Edelman for the purpose of enables direct communication between the brain and a computer or electronic device (Crawford: p.2).
With respect to claim 17/16, Edelman and Crawford combined teaches further wherein the moving target comprises at least one of a virtual object appearing on a screen and a real object appearing in physical space (Crawford: enable telepathy communication and telepathy AR, a stimulus is retrieved and displayed when correlated brain activity scales are detected, p.6).
With respect to claim 18/16, Edelman and Crawford combined teaches further comprising: identifying relevant spatio-temporal-spectral features from the plurality of signals (Edelman: time-frequency analysis, feature extraction, and classification, fig.2 and p.5-10, includes features traced back to location in corresponding spatial, temporal, and spectral domains to observe signals responsible for separating each task, p.10 section B and col.2).
With respect to claim 19/16, Edelman and Crawford combined teaches further comprising: producing a continuous estimate of motor or mental intention (Edelman: human subjects to perform continuous specified Motor Imaginary {MI} tasks, p.5 section II; noninvasive BCI to obtain user’s motor intent through signals detected at the scalp…SMR BCIs incorporated into real life situations to control devices such as wheelchairs, limb orthoses, or quadcopters, See Abstract and p.11 section IV).
With respect to claim 21/16, Edelman and Crawford combined teaches further comprising: estimating a motor state or mental state using continuous pursuit signals, wherein estimating is online and adaptive (Edelman: noninvasive BCI to obtain user’s motor intent through signals detected at the scalp…SMR BCIs incorporated into real life situations to control devices such as wheelchairs, limb orthoses, or quadcopters, p.11 section IV).
With respect to claim 22, Edelman teaches method of training a user to control an external device through a brain-computer interface (sensorimotor-based brain-computer interfaces (BCIs) to control of real and virtual devices, abstract; BCI to control prosthetic arm using MI signals, p.5) comprising:
directing a user of the external device to engage in a continuous pursuit task wherein the user performs motor imagination [[to chase a randomly moving target]] (human subjects to perform continuous specified Motor Imaginary {MI} tasks, p.5 section II; noninvasive BCI to obtain user’s motor intent through signals detected at the scalp…SMR BCIs incorporated into real life situations to control devices such as wheelchairs, limb orthoses, or quadcopters, See Abstract and p.11 section IV);
non-invasively obtaining a plurality of signals originating in a brain of the user while the user engages in the continuous pursuit task, wherein the plurality of signals are related to the user’s mental intent (noninvasive BCI to obtain user’s motor intent through signals detected at the scalp…SMR BCIs incorporated into real life situations to control devices such as wheelchairs, limb orthoses, or quadcopters, See Abstract and p.11 section IV);
using real-time source imaging, identifying the plurality of signals originating from regions of the brain associated with imagery tasks (EEG source imaging (ESI) as depicts in fig.2, with ESI to discriminate Motor Imaginary {MI} tasks by reconstructing electrical activity on the cortical surface to isolate regions containing discriminable information regarding specific MI tasks, p.5, section I, col.2 and p.6, section D Region of Interest Selection);
analyzing the portion of the plurality of signals to identify a mental intent of the user, wherein the mental intent relates to the continuous pursuit task (noninvasive BCI to obtain user’s motor intent through signals detected at the scalp…SMR BCIs incorporated into real life situations to control devices such as wheelchairs, limb orthoses, or quadcopters, See Abstract and p.11 section IV);
providing feedback to the user (provide neurofeedback reflective of the user’s intent, p.5).
With respect to claim 22, Edelman does not appear to teach wherein the user performs motor imagination to chase a randomly moving target. However, it is known by Crawford to teach of a brain computer interface system and method to control a processing device (Crawford: p.5) comprising at least: wherein the user performs motor imagination to chase a randomly moving target (Crawford: users direct movement of augmented reality (AR) objects/characters through their thoughts…control AR characters that are moving or racing, p.9).
Because Crawford is also directed to a Brain Computer Interface (BCI) (Crawford: p.5; Edelman: sensorimotor-based BCI, abstract and p.5), it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of the user performs motor imagination to chase a randomly moving target as taught by Crawford with the sensorimotor-based brain-computer interfaces (BCIs) system as taught by Edelman for the purpose of enables direct communication between the brain and a computer or electronic device (Crawford: p.2).
Conclusion
The additional prior arts made of record and have not yet been relied upon are considered pertinent to applicant's disclosure as follows:
He et al. ("Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms," in Proceedings of the IEEE, vol. 103, no. 6, pp. 907-925, June 2015) teaches of a noninvasive BCI based on SMR to control external devices (Abstract) in real-time physical interaction with the environment (page 909) with real-time source imaging platforms (page 915).
Xu et al. ("Enhanced Low-Latency Detection of Motor Intention From EEG for Closed-Loop Brain-Computer Interface Applications", IEEE, 2013, p.288-296) teaches of an online BCI experiment to detect motor intentions from EEG to trigger controls of external devices (See abstract and pages 288-296), where Movement-related cortical potentials (MRCP) from EEG signals are detected in real-time (Abstract, page 292 and 295).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEN (CINDY) D KHUU whose telephone number is (571)272-8585. The examiner can normally be reached on Monday-Friday 8a-8p.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ken Lo can be reached on 571-272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HIEN D KHUU/Primary Examiner, Art Unit 2116 June 15, 2026
1 SMR-BCIs are further well-known as non-invasive systems that translate a user's brain activity into computer commands to control external devices. See NPL, He et al.
2 SMR-BCIs are further well-known as non-invasive systems that translate a user's brain activity into computer commands to control external devices. See NPL, He et al.
3 SMR-BCIs are known as non-invasive systems that translate a user's brain activity into computer commands to control external devices. See NPL, He et al.
4 The preamble of the claim(s) is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02.
5 SMR-BCIs are further well-known as non-invasive systems that translate a user's brain activity into computer commands to control external devices. See NPL, He et al.
6 SMR-BCIs are further well-known as non-invasive systems that translate a user's brain activity into computer commands to control external devices. See NPL, He et al.
7 SMR-BCIs are known as non-invasive systems that translate a user's brain activity into computer commands to control external devices. See NPL, He et al.
8 SMR-BCIs are further well-known as non-invasive systems that translate a user's brain activity into computer commands to control external devices. See NPL, He et al.
9 Linear classifier is recited in alternative as claimed in claim 13.
Prosecution Insights