DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
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 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)(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 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Baber et al. (US Pub: 2024/0053825 A1).
As to claim 1, Baber discloses a system (i.e. the system of Baber figure 14 and 15 embodiment) (see Fig. 14-15, [0080-0086]), comprising:
a brain-computer interface comprising a set of electrodes configured to record a set of electrical measurements from a brain of a subject (i.e. the processing of the user’s EEG information as seen in figure 14 which shows brain data 1401 being processed in the Latent Space) (see Fig. 14); and
a processing system communicatively coupled to the set of electrodes, wherein the processing system is configured (i.e. the EEG data collection system is demonstrated in figure 12B embodiment which shows the computer 1204 as the processing system linked the EEG) (see Fig. 12B, 14, [0080-0084]) to:
determin a neural state vector based on the set of electrical measurements (i.e. the neural state vector is the EEG signals that is processed to detect user input) (see Fig. 12, [0082]);
determine a latent space position based on the neural state vector (i.e. as seen in figure 14 the latent space position is demonstrated as the combined EEG data and virtual eye tracking data) (see Fig. 14, [0083-0084]), wherein the latent space position comprises a position within a latent space of a generative model (i.e. the generative model is the model as demonstrated in figure 15, [0088] “For example, implementations herein may utilize a generator or generator network 1502 to recreate the image latent representation from EEG latent representation. In the example generator model shown and described, here, the generator may be created/implemented by concatenating the two parts of the VAE and linking them with fully connected layers”), wherein the generative model comprises a set of input layers (i.e. the Convolution + Max. Pooling layer seen in the generation network 1502) (see Fig. 15, [0086-0088]), an intermediate layer (i.e. the fully connected layers) (see Fig. 15, [0086-0088]), and a set of output layers (i.e. the output layers are the layers applied to the discriminator network) (see Fig. 15, [0086-0088]), wherein the latent space corresponds to the intermediate layer (i.e. Generator Network 1502 processes the data through the generation of the different layers) (see Fig. 15, [0086]);
using the set of output layers of the generative model, determine an output based on the latent space position (i.e. the output layer is the layer that is applied to the Discrimination network 1502 which process the brain data intermediate layer to Generated Saliency Map) (see Fig. 15, [0086-0088]); and
control an external system based on the output (i.e. as the display figure 16 shows the user output of the focus and selection of the screen icon 1601 is the actual control of the external computing system) (see Fig. 15-16, [0086-0092]).
As to claim 11, Baber teaches a method (i.e. the data processing method of Baber figure 14 and 15 embodiment) (see Fig. 14-15, [0080-0086]), comprising:
Using a brain-computer interface, recording a set of measurements from a brain of a subject (i.e. the EEG data collection system is demonstrated in figure 12B embodiment which shows the computer 1204 as the processing system linked the EEG) (see Fig. 12B, 14, [0080-0084]):
determining a neural state vector based on the set of measurements (i.e. the neural state vector is the EEG signals that is processed to detect user input) (see Fig. 12, [0082]);
determining a latent space position based on the neural state vector (i.e. as seen in figure 14 the latent space position is demonstrated as the combined EEG data and virtual eye tracking data) (see Fig. 14, [0083-0084]), wherein the latent space position comprises a position within a latent space of a generative model (i.e. the generative model is the model as demonstrated in figure 15, [0088] “For example, implementations herein may utilize a generator or generator network 1502 to recreate the image latent representation from EEG latent representation. In the example generator model shown and described, here, the generator may be created/implemented by concatenating the two parts of the VAE and linking them with fully connected layers”) wherein the generative comprised a set of input layers (i.e. the Convolution + Max. Pooling layer seen in the generation network 1502) (see Fig. 15, [0086-0088]), an intermediate layer (i.e. the fully connected layers) (see Fig. 15, [0086-0088]), and a set of output layers (i.e. the output layers are the layers applied to the discriminator network) (see Fig. 15, [0086-0088]), wherein the latent space corresponds to the intermediate layer (i.e. Generator Network 1502 processes the data through the generation of the different layers) (see Fig. 15, [0086]);
using the set of output layers, of the generative model, determining an output based on the latent space position (i.e. the output layer is the layer that is applied to the Discrimination network 1502 which process the brain data intermediate layer to Generated Saliency Map) (see Fig. 15, [0086-0088]); and
providing the output to the subject (i.e. as the display figure 16 shows the user output of the focus and selection of the screen icon 1601 is the actual control of the external computing system) (see Fig. 15-16, [0086-0092]).
(i.e. as the display is switched back to the new operation of the UHD the image is refreshed to a 60 Hz first frequency) (see Fig. 1-3, Col. 2-3).
As to claim 2, Baber teaches the system of claim 1, wherein determining the latent space position based on the neural state vector comprises: determining a latent vector by transforming the neural stat vector (i.e. the latent vector is the vector that is determined as seen in figure 15 that track the user’s intension in the latent space of the intent area of input) (see Fig. 15, [0086-0088]).
As to claim 3, Baber teaches the system of Claim 2, wherein the latent space position defines coordinates of a point on a manifold in the latent space, wherein the latent vector defines a magnitude and a direction of movement on the manifold (i.e. the EEG data as being processed in figure 15 is based on the latent space position defines coordinates of a point on a manifold in the latent space of the user’s brain EEG image where the latent vector defines a magnitude and direction of movement on the manifold) (see Fig. 15, [0086-0088]).
As to claim 4, Baber teaches the system of Claim 2, wherein the manifold defines a subset of points in the latent space corresponding to valid outputs (i.e. as seen in figure 15, the system of Baber shows that the validation system of figure 15 shows that the visual eye tracking validated by the brain signal of the EEG system) (see Fig. 15, [0085-0088]).
As to claim 5, Baber teaches the system of Claim 1, wherein the latent space position is further determined based on an initial latent space position, wherein the processing system is further configured to control the external system based on an initial output, the initial output determined based on the initial latent space position, wherein the initial output is determined using the set of output layers of the generative model (i.e. as the system of Baber shows an EEG system the initial set of signals to the brain of the subject shown in figure 15-17 where the generative model based on the Generative network allow the initial output of the element 406 to be further compared with the Generated Saliency Map) (see Fig. 15-17, [0080-0092]).
As to claim 6, Baber teaches the system of Claim 5, wherein the brain-computer interface is configured to deliver an initial set of signals to the brain of the subject, the initial set of signals determined based on the initial latent space position (i.e. as the system of Baber shows an EEG system the initial set of signals to the brain of the subject shown in figure 15-17 at the start of the EEG imaging process) (see Fig. 15-17, [0092]).
As to claim 7, Baber teaches the system of Claim 6, wherein the brain-computer interface is further configured to deliver an updated set of signals to the brain of the subject, the updated set of signals determined based on the latent space position (i.e. the EEG system is shown to continuously update the Brain Data images as the system run the detection loops seen in figure 17) (see Fig. 17, [0092]).
As to claim 8, Baber teaches the system of Claim 1, wherein the external system comprises an interface, wherein the interface is configured to display the output, wherein the output comprises at least one of: image or text (i.e. the image is clearly seen in figure 16 which is a GUI interface that the user is able to select with eye tracking and brain activities) (see Fig. 16, [0089-0090]).
As to claim 9, Baber teaches the system of Claim 1, wherein the set of input layers of the generative model are not used to determine the output (i.e. the input layers is the Convolution layers which are processing layers and not used to determine the output which is via the intermediate layers) (see Fig. 15, [0086-0088]).
As to claim 10, Baber teaches the system of Claim 1, wherein each vector component of the neural state vector corresponds to an electrode in the set of electrodes (i.e. as seen in figure 6A and 6B the EEG electrode are the source for the vector component of the neural state vector) (see Fig. 6A, 6B, [0069-0070]).
As to claim 12, Baber teaches the method of Claim 11, wherein the latent space position is further determined based on an initial latent space position, the method further comprising providing an initial output corresponding to the initial latent space position (i.e. the latent vector is the vector that is determined as seen in figure 15 that track the user’s intension in the latent space of the intent area of input) (see Fig. 15, [0086-0088]).
As to claim 13, Baber teaches the method of Claim 12, wherein the initial output is determined using the set of output layers of the generative model (i.e. as the system of Baber shows an EEG system the initial set of signals to the brain of the subject shown in figure 15-17 where the generative model based on the Generative network allow the initial output of the element 406 to be further compared with the Generated Saliency Map) (see Fig. 15-17, [0080-0092]).
As to claim 14, Baber teaches the method of Claim 12, wherein the brain-computer interface is configured to deliver an initial set signals to the brain of the subject, the initial set of signals determined based on the initial latent space position (i.e. as seen in figure 15 the EEG system of Baber is configured to deliver an initial set signals to the brain to initial the EEG scan to input the images based on the initial latent space position) (see Fig. 15, [0086-0088]).
As to claim 15, Baber teaches the method of Claim 14, wherein the brain-computer interface is further configured to deliver an updated set of signals to the brain of the subject, the updated set of signals determined based on the latent space position (i.e. the EEG system is shown to continuously update the Brain Data images as the system run the detection loops seen in figure 17) (see Fig. 17, [0092]).
As to claim 16, Baber teaches the method of Claim 14, wherein the initial set of signals comprises electrical signals, wherein the brain-computer interface comprises a set of electrodes configured to deliver the initial set of signals to the brain of the subject (i.e. as the system of Baber shows an EEG system the initial set of signals to the brain of the subject shown in figure 15-17 at the start of the EEG imaging process) (see Fig. 15-17, [0092]).
As to claim 17, Baber teaches the method of Claim 11, wherein determining the latent space position based on the neural state vector comprises: determining a latent vector by transforming the neural state vector, and determining the latent space position based on the latent vector (i.e. the EEG data as being processed in figure 15 is based on the latent space position defines coordinates of a point on latent vector of the user’s brain EEG image where the latent vector defines latent space position) (see Fig. 15, [0086-0088]).
As to claim 18, Baber teaches the method of Claim 11, wherein the generative model comprises a pretrained generative model (i.e. the system of figure 15 shows a generative network 1502 which is shown to be pretrained with preset data process algorithm) (see Fig. 15, [0086-0088]).
As to claim 19, Baber teaches the method of Claim 11, wherein the set of input layers of the generative model are not used to determine the output (i.e. the input layers is the Convolution layers which are processing layers and not used to determine the output which is via the intermediate layers) (see Fig. 15, [0086-0088]).
As to claim 20, Baber teaches the method of Claim 11, wherein the output comprises at least one of an image, text, or a sound (i.e. the image is clearly seen in figure 16 which is a GUI interface that the user is able to select with eye tracking and brain activities) (see Fig. 16, [0089-0090]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art Leuthardt (US Pub: 2021/0290890 A1) is cited to teach another type of brain-computer interface input method which has neural signal input based on generative model as seen in figures 1-7 embodiments.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CALVIN C. MA whose telephone number is (571)270-1713. The examiner can normally be reached 8:00AM-5:00PM.
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/CALVIN C MA/Primary Examiner, Art Unit 2693 July 11, 2026
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