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).
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Claims 1-13 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of copending Application No. 19/433,384 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other as shown on table below mapping claims side by side.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
For example:
19/433,334
19/433,384
1. A Method for training a brain-computer interface, the brain-computer interface being connected to sensors arranged around the brain of a user, each sensor being configured to detect an electrophysiological signal representative of neural activity of the user, the interface being configured to control an actuator based on detected electrophysiological signals, the training method comprising: - a) selecting a mental task to be performed by the user, chosen from I groups of states, each group of states comprising a list of Ki predetermined tasks, each task in a group of states being executable at the same time as each task in another group of states; - b) executing, by the user the task selected in step a) and, during said execution, acquiring electrophysiological signals from the various sensors, the execution of each task corresponding to a state of the user, and using a processing unit to form an observation tensor based on characteristics of the electrophysiological signals; - c) reiterating steps a) and b) over multiple epochs, each epoch being a time window, associated with a state, the epochs forming a sequence; - d) forming I training tensors from the electrophysiological signals detected in each epoch, each training tensor being associated with one of said group of states; - e) forming I control tensors from the selected tasks, each control tensor being associated with one of said group of states, the value of the control tensor, in an epoch, taking an inactive value when the task selected in said epoch does not belong to the group of states with which the control tensor is associated; - f) for each group of states, forming a predictive model, by way of regression between the training tensor and the control tensor respectively associated to each group of states, the predictive model estimating a probability of the user being in each state of each group of states; - g) for each group of states, from each predictive model resulting from f), defining a hidden Markov model, each hidden Markov model being configured to estimate a probability of the state of the user in each epoch; wherein steps c) to g) are implemented by the processing unit; wherein the method further comprises: - defining a weighting criterion for each epoch; - in each group of states, assigning a weight to each epoch, the weight being defined depending on the weighting criterion for said epoch, of the sequence, based on which two different epochs of the sequence, for which the weighting criterion is different, are assigned two different weights; wherein, in each group of states, the predictive model is formed depending on the weight respectively assigned to each epoch.
1. A Method for training a brain-computer interface, the brain-computer interface being connected to sensors arranged around the brain of a user, each sensor being configured to detect an electrophysiological signal dependent on a neural activity of the user, the interface being configured to control an actuator based on the detected electrophysiological signals, the method comprising:- a) selecting a mental task to be performed, chosen from a predetermined list of tasks;- b) the user executing the mental task selected in step a), the training method further comprising, during execution of the task: acquiring electronic signals generated by the various sensors; extracting features from the electronic signals; forming an observation tensor from the features extracted from the signals;- c) repeating steps a) and b) during a predetermined number of time epochs, forming a sequence;- d) forming a training tensor from the observation tensors formed in each time epoch, and a control tensor from the tasks selected in each epoch, each term of the training tensor and of the control tensor being associated with one epoch of the sequence;- e) forming a predictive model, by regression between the training tensor and the control tensor, the predictive model being configured to predict a task, chosen from the list of tasks, imagined by the user depending on the observation tensor formed in each epoch; wherein steps b), d) and e) are implemented by a processing unit; wherein the method further comprises:- defining a weighting criterion for each epoch;- assigning a weight to each epoch, the weight being defined depending on the weighting criterion of said epoch, so that to two different epochs, of the sequence, of which the weighting criterion is different, two different weights are assigned; wherein the predictive model is formed depending on the weight assigned to each epoch.
2. The Method according to claim 1, wherein the weight assigned to an epoch depends on the task selected during said epoch and on the group of states
2. The method according to Claim 1, wherein the weight assigned to an epoch depends on the task selected during said epoch.
3. The Method according to claim 1, wherein - step c) is repeated so as to form multiple successive sequences, each sequence being assigned a chronological rank; - steps d) to g) are implemented for each sequence; - in step f), in each group of states, each predictive model is established from two consecutive sequences, by assigning a forgetting factor to the data resulting from the lower-ranking sequence.
8. The method according to Claim 7, wherein - step c) is repeated so as to form a plurality of successive sequences, each sequence being assigned a rank; - step d) is implemented for each sequence;
- in step e), the predictive model is formed from two successive sequences, comprising a sequence of low rank and a sequence of high rank, on the basis of a sum of the cross-covariance tensor established for the sequence of high rank and of the cross-covariance tensor established for the sequence of low rank multiplied by a forgetting factor (λ).
4. The Method according to claim 3, wherein, for each group of states, the weighting criterion is a frequency of occurrence of each task, the weight of each epoch being higher the lower the number of occurrences of the task associated with the epoch, following the successive performed sequences.
3. The method according to Claim 2, wherein:- steps a) to e) are implemented during a plurality of successive sequences; - the weighting criterion is a frequency of occurrence of each task, the weight of each epoch increasing as the number of occurrences of the task, in the successive performed sequences, decreases.
5. The Method according to claim 4, comprising, in each group of states, after each new sequence, updating a weighted total number of occurrences for each task, wherein updating comprises, for each task : - determining a number of occurrences in the new sequence; - weighting the number of occurrences, during the new sequence, with the weight respectively assigned to the task in the new sequence; - summing the weighted number of occurrences of the task, for the new sequence, to the weighted total number for each task resulting from the lower-ranking sequence, the latter being multiplied by the forgetting factor.
4. The method according to Claim 3 comprising, after each new sequence, updating a weighted total number of occurrences for each task, wherein updating comprises, for each task:- determining a number of occurrences of the task in the new sequence;- weighting the number of occurrences of the task, in the new sequence, by the weight assigned to said task in the new sequence;- summing the weighted number of occurrences of the task, in the new sequence, to the weighted total number for said task resulting from the previous sequence, the latter being multiplied by a forgetting factor.
6. The Method according to claim 1, wherein the weighting criterion is a training-performance criterion, the method further comprising, for each group of states: - determining a training-performance indicator for each task following each epoch; - determining the weight of each task based on the training-performance indicator for the task.
5. The method according to Claim 1, wherein the weighting criterion is a training- performance criterion, the method comprising:- determining a training-performance indicator for each task following each epoch;- determining the weight of each task based on the training-performance criterion of the task.
7. The Method according to claim 1, wherein the weighting criterion is a quality of the electrophysiological signals detected in each sequence, the method comprising: - determining a criterion concerning the quality of the signals collected in each sequence;
in each group of states, determining the weight of each task based on the
criterion concerning the quality of the signals.
6. The method according to Claim 1, wherein the weighting criterion is a signal-quality criterion quantifying the quality of the signals collected in each sequence, the method comprising:- determining a signal-quality criterion for the signals collected in each sequence;- determining the weight of each task depending on the signal-quality criterion of the respective signals collected during the execution of each task.
8. The Method according to claim 1, wherein, for each group of states, the predictive model is implemented by multivariate regression, comprising calculating a cross-covariance tensor expressing the cross-covariance between the training tensor and the control tensor, the cross-covariance tensor of each sequence being established from a product: - of the learning tensor respectively associated to each group of states; - of the control tensor respectively associated to each group of states; - and of the weights assigned to each epoch.
7. The method according to Claim 1, wherein, in step e), the predictive model is formed by N-way regression, comprising calculation of a cross-covariance tensor expressing the cross-covariance between the training tensor and the control tensor, the cross-covariance tensor of each sequence being established from a product:- of the observation tensor;- of the control tensor;- and of the respective weights assigned to each epoch.
9. The Method according to Claim 8, wherein: - step c) is repeated so as to form multiple successive sequences, each sequence being assigned a chronological rank; - steps d) and e) are implemented for each sequence; - in step f), the predictive model is established, for each group of states, from two consecutive sequences comprising a high-rank sequence and a low-rank sequence, with a sum of : ° the cross-covariance tensor established for the high-rank sequence ; and ° the cross-covariance tensor established for the lor-rank sequence multiplied by a forgetting factor.
8. The method according to Claim 7, wherein - step c) is repeated so as to form a plurality of successive sequences, each sequence being assigned a rank; - step d) is implemented for each sequence;
- in step e), the predictive model is formed from two successive sequences, comprising a sequence of low rank and a sequence of high rank, on the basis of a sum of the cross-covariance tensor established for the sequence of high rank and of the cross-covariance tensor established for the sequence of low rank multiplied by a forgetting factor (λ).
10. The Method according to Claim 8, wherein, in each group of states: - the training tensor and the control tensor are formed from a matrix, one dimension of which is the number of epochs per sequence; - the weights form a diagonal matrix, each dimension of which is the number of epochs per sequence, each term of the diagonal matrix corresponding to the weight assigned to the task respectively performed in said sequence.
9. The method according Claim 8, wherein:- the training tensor and the control tensor each form a matrix, wherein one dimension of each matrix is the number of epochs in the sequence;- the weights form a diagonal matrix, each dimension of which is the number of epochs per sequence, each term of the diagonal matrix corresponding to the weight assigned to each epoch executed in said sequence.
11. The Method according to Claim 1, wherein, in each group of states, for at least one specific task, the weight is determined such that the number of occurrences of said specific task, weighted by the weight assigned to the specific task, is greater than the number of occurrences of at least one other task, weighted by the weight assigned to said other task.
10. The method according to Claim 1, wherein, for at least one specific task, the weight is determined such that the number of occurrences of said specific task, weighted by the weight assigned to the specific task, is greater than the number of occurrences of at least one other task, weighted by the weight assigned to said other task.
12. A Brain-computer interface, comprising sensors configured to be arranged around the brain of a user, and configured to detect electrophysiological signals representative of neural activity of the user, the interface being configured to control an actuator, by implementing a predictive control model, the predictive model being configured to generate an actuator control signal from detected electrophysiological signals, the brain-computer interface comprising a processing unit configured to acquire the electrophysiological signals in each step b), and to implement steps d) to g) of the Method according to claim 1.
12. A brain-computer interface, comprising sensors arranged around the brain of a user, and configured to detect electrophysiological signals representative of neural activity of the user, the interface being configured to control an actuator, by implementing a predictive model, the predictive model being configured to generate an actuator control signal from detected electrophysiological signals; the interface comprising a processing unit (3), configured to acquire the electronic signals in each step b), and to implement steps d) and e) of a method according to
Claim 1.
13. The Brain-computer interface according to Claim 12, wherein the actuator is a device external to the user or a device able to be implanted in the user's body.
13. The brain-computer interface according to Claim 12, wherein the actuator is a device external to the user or a device implanted in the user's body.
Claims 1-13 are allowable over the prior art of record. The rejection(s) under the provisional nonstatutory double patenting rejection, set forth in this Office action, would need to be overcome.
Conclusion
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INSA . SADIO
Primary Examiner
Art Unit 2628
/INSA SADIO/Primary Examiner, Art Unit 2628
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