Detailed Office 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).
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Claims 2 – 21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1- 3, 8, 9, 10, 15, 16, 17of U.S. Patent No. 12,314,856 in view of Chrisantha Fernando et al. (Convolution by Evolution Differentiable Pattern Producing Networks, June 8, 2016).
Jaderberg et al. disclose all the limitations except the system and method wherein setting (i) the hyperparameters of the candidate neural network or (ii) hyperparameters of another candidate neural network sampled from the plurality of candidate neural networks as the updated hyperparameters of the candidate neural network, or permuting (i) the hyperparameters of the candidate neural network or (ii) the hyperparameters of the other candidate neural network to obtain the updated hyperparameters of the candidate neural network, wherein the other candidate neural network has respective values of the network parameters; updating the maintained data of the candidate neural network to specify the updated values of the hyperparameters and the updated values of the network parameters. However, Chrisantha Fernado et al., in the same field of endeavor, disclose a system and method comprising setting (i) the hyperparameters of the candidate neural network or (ii) hyperparameters of another candidate neural network sampled from the plurality of candidate neural networks as the updated hyperparameters of the candidate neural network, or permuting (i) the hyperparameters of the candidate neural network or (ii) the hyperparameters of the other candidate neural network to obtain the updated hyperparameters of the candidate neural network, wherein the other candidate neural network has respective values of the network parameters; updating the maintained data of the candidate neural network to specify the updated values of the hyperparameters and the updated values of the network parameters (fig. 2, section 3.3; section 3.3.1). Therefore, it would have been obvious to one of ordinary skill in the art at the time of the filing of the invention to incorporate Chrisantha Fernando et al.’s system and method in Jaderberg et al.’s system to improve the performance of the system and method.
US Application Number 19/193,756
US Patent number 12,314,856
(Claim 2)
A method of training a neural network having a plurality of network parameters to perform a particular neural network task and to determine trained values of the network parameters using an iterative training process having a plurality of hyperparameters, the method comprising: maintaining a plurality of candidate neural networks and, for each of the plurality of candidate neural networks, data specifying: (i) values of the network parameters of the candidate neural network, (ii) values of the hyperparameters of the candidate neural network, and (iii) a quality measure that measures a performance of the candidate neural network on the particular neural network task; for each of the plurality of candidate neural networks: repeatedly updating the values of the network parameters of the candidate neural network in accordance with the maintained values of the hyperparameters of the candidate neural network until a termination criterion is satisfied, wherein the maintained values of the hyperparameters remain unchanged; updating the respective values of the hyperparameters of the candidate neural network comprising: setting (i) the hyperparameters of the candidate neural network or (ii) hyperparameters of another candidate neural network sampled from the plurality of candidate neural networks as the updated hyperparameters of the candidate neural network, or permuting (i) the hyperparameters of the candidate neural network or (ii) the hyperparameters of the other candidate neural network to obtain the updated hyperparameters of the candidate neural network, wherein the other candidate neural network has respective values of the network parameters; updating the maintained data of the candidate neural network to specify the updated values of the hyperparameters and the updated values of the network parameters; and selecting the trained values of the network parameters from the parameter values in the maintained data after the training operations have been repeatedly performed.
(Claim 1)
A method of training a neural network having a plurality of network parameters to perform a particular neural network task and to determine trained values of the network parameters using an iterative training process having a plurality of hyperparameters, the method comprising: maintaining a plurality of candidate neural networks and, for each of the plurality of candidate neural networks, data specifying: (i) values of the network parameters of the candidate neural network, (ii) values of the hyperparameters of the candidate neural network, and (iii) a quality measure that measures a performance of the candidate neural network on the particular neural network task; for each of the plurality of candidate neural networks, repeatedly performing the following training operations, comprising: repeatedly updating the values of the network parameters of the candidate neural network in accordance with the maintained values of the hyperparameters of the candidate neural network until a termination criterion is satisfied, wherein the maintained values of hyperparameters remain unchanged; updating the quality measure of the candidate neural network based on the updated values of the network parameters of the candidate neural network; updating the respective values of the hyperparameters of the candidate neural network based on the updated quality measure of the candidate neural network, the updating comprising: sampling another candidate neural network from the plurality of candidate neural networks, the other candidate neural network having respective values of the network parameters and a respective quality measure for the respective values of the network parameters; and updating the hyperparameters of the candidate neural network based on a result of comparing the updated quality measure of the candidate neural network and the respective quality measure of the other candidate neural network; updating the maintained data of the candidate neural network to specify the updated values of the hyperparameters, the updated values of the network parameters, and the updated value of the quality measure; and selecting the trained values of the network parameters from the parameter values in the maintained data based on the maintained quality measures for the plurality of candidate neural networks after the training operations have repeatedly been performed.
(Claim 3)
The method of claim 2, wherein selecting the trained values of the network parameters comprises selecting the maintained parameter values of the candidate neural network having the highest quality measure among the plurality of candidate neural networks after the training operations have been repeatedly performed.
(Claim 2)
The method of claim 1, wherein selecting the trained values of the network parameters from the parameter values in the maintained data based on the maintained quality measures of the plurality of candidate neural networks comprises: selecting the maintained parameter values of the candidate neural network having a best maintained quality measure of any of the plurality of candidate neural networks after the training operations have repeatedly been performed.
(Claim 4)
The method of claim 2, further comprising: prior to updating the respective values of the hyperparameters of the candidate neural network, updating the quality measure of the candidate neural network based on the updated values of the network parameters of the candidate neural network.
(Claim 3)
The method of claim 1, wherein updating the hyperparameters of the candidate neural network based on the result of comparing the updated quality measure of the candidate neural network and the respective quality measure of the other candidate neural network comprises: determining whether the respective quality measure of the other candidate neural network is greater than the updated quality measure of the candidate neural network; and in response to determining that the respective quality measure of the other candidate neural network is greater than the updated quality measure of the candidate neural network, setting new values of the hyperparameters of the candidate neural network to the maintained values of the hyperparameters of the other candidate neural network.
(Claim 5)
The method of claim 4, further comprising: in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure greater than the updated quality measure of the candidate neural network, permuting the hyperparameters of the other candidate neural network according to a pre-determined factor or distribution to obtain the updated hyperparameters of the candidate neural network; or in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure less than the updated quality measure of the candidate neural network, permuting the hyperparameters of the candidate neural network according to the pre-determined factor or distribution to obtain the updated hyperparameters of the candidate neural network.
(Claim 6)
The method of claim 4, further comprising: in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure greater than the updated quality measure of the candidate neural network, setting the hyperparameters of the other candidate neural network as the updated hyperparameters of the candidate neural network; or in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure less than the updated quality measure of the candidate neural network, maintaining the hyperparameters of the candidate neural network.
(Claim 7)
The method of claim 4, further comprising: in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure greater than a threshold quality measure, setting the hyperparameters of the other candidate neural network as the updated hyperparameters of the candidate neural network.
(Claim 8)
The method of claim 2, further comprising: providing the trained values of the network parameters for use in processing new inputs to the neural network.
(Claim 7)
The method of claim 1, further comprising: providing the trained values of the network parameters for use in processing new inputs to the neural network.
(Claim 9)
A system comprising: one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations for training a neural network having a plurality of network parameters to perform a particular neural network task and to determine trained values of the network parameters using an iterative training process having a plurality of hyperparameters, the operations comprising: maintaining a plurality of candidate neural networks and, for each of the plurality of candidate neural networks, data specifying: (i) values of the network parameters of the candidate neural network, (ii) values of the hyperparameters of the candidate neural network, and (iii) a quality measure that measures a performance of the candidate neural network on the particular neural network task; for each of the plurality of candidate neural networks: repeatedly updating the values of the network parameters of the candidate neural network in accordance with the maintained values of the hyperparameters of the candidate neural network until a termination criterion is satisfied, wherein the maintained values of the hyperparameters remain unchanged; updating the respective values of the hyperparameters of the candidate neural network comprising: setting (i) the hyperparameters of the candidate neural network or (ii) hyperparameters of another candidate neural network sampled from the plurality of candidate neural networks as the updated hyperparameters of the candidate neural network, or permuting (i) the hyperparameters of the candidate neural network or (ii) the hyperparameters of the other candidate neural network to obtain the updated hyperparameters of the candidate neural network, wherein the other candidate neural network has respective values of the network parameters; updating the maintained data of the candidate neural network to specify the updated values of the hyperparameters and the updated values of the network parameters; and selecting the trained values of the network parameters from the parameter values in the maintained data after the training operations have been repeatedly performed.
(Claim 8)
A system comprising: one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations for training a neural network having a plurality of network parameters to perform a particular neural network task and to determine trained values of the network parameters using an iterative training process having a plurality of hyperparameters, the operations comprising: maintaining a plurality of candidate neural networks and, for each of the plurality of candidate neural networks, data specifying: (i) values of the network parameters of the candidate neural network, (ii) values of the hyperparameters of the candidate neural network, and (iii) a quality measure that measures a performance of the candidate neural network on the particular neural network task; for each of the plurality of candidate neural networks, repeatedly performing the following training operations, comprising: repeatedly updating the values of the network parameters of the candidate neural network in accordance with the maintained values of the hyperparameters of the candidate neural network until a termination criterion is satisfied, wherein the maintained values of hyperparameters remain unchanged; updating the quality measure of the candidate neural network based on the updated values of the network parameters of the candidate neural network; updating the respective values of the hyperparameters of the candidate neural network based on the updated quality measure of the candidate neural network, the updating comprising: sampling another candidate neural network from the plurality of candidate neural networks, the other candidate neural network having respective values of the network parameters and a respective quality measure for the respective values of the network parameters; and updating the hyperparameters of the candidate neural network based on a result of comparing the updated quality measure of the candidate neural network and the respective quality measure of the other candidate neural network; updating the maintained data of the candidate neural network to specify the updated values of the hyperparameters, the updated values of the network parameters, and the updated value of the quality measure; and selecting the trained values of the network parameters from the parameter values in the maintained data based on the maintained quality measures for the plurality of candidate neural networks after the training operations have repeatedly been performed.
(Claim 10)
The system of claim 9, wherein selecting the trained values of the network parameters comprises selecting the maintained parameter values of the candidate neural network having the highest quality measure among the plurality of candidate neural networks after the training operations have been repeatedly performed.
(Claim 9)
The system of claim 8, wherein selecting the trained values of the network parameters from the parameter values in the maintained data based on the maintained quality measures of the plurality of candidate neural networks comprises: selecting the maintained parameter values of the candidate neural network having a best maintained quality measure of any of the plurality of candidate neural networks after the training operations have repeatedly been performed.
(Claim 11)
The system of claim 9, wherein the operations further comprise: prior to updating the respective values of the hyperparameters of the candidate neural network, updating the quality measure of the candidate neural network based on the updated values of the network parameters of the candidate neural network.
(Claim 10)
The system of claim 8, wherein updating the hyperparameters of the candidate neural network based on the result of comparing the updated quality measure of the candidate neural network and the respective quality measure of the other candidate neural network comprises: determining whether the respective quality measure of the other candidate neural network is greater than the updated quality measure of the candidate neural network; and in response to determining that the respective quality measure of the other candidate neural network is greater than the updated quality measure of the candidate neural network, setting new values of the hyperparameters of the candidate neural network to the maintained values of the hyperparameters of the other candidate neural network.
(Claim 12)
The system of claim 11, wherein the operations further comprise: in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure greater than the updated quality measure of the candidate neural network, permuting the hyperparameters of the other candidate neural network according to a pre-determined factor or distribution to obtain the updated hyperparameters of the candidate neural network; or in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure less than the updated quality measure of the candidate neural network, permuting the hyperparameters of the candidate neural network according to the pre-determined factor or distribution to obtain the updated hyperparameters of the candidate neural network.
(Claim 13)
The system of claim 11, wherein the operations further comprise: in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure greater than the updated quality measure of the candidate neural network, setting the hyperparameters of the other candidate neural network as the updated hyperparameters of the candidate neural network; or in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure less than the updated quality measure of the candidate neural network, maintaining the hyperparameters of the candidate neural network.
(Claim 14)
The system of claim 11, wherein the operations further comprise: in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure greater than a threshold quality measure, setting the hyperparameters of the other candidate neural network as the updated hyperparameters of the candidate neural network.
(Claim 15)
The system of claim 9, wherein the operations further comprise: providing the trained values of the network parameters for use in processing new inputs to the neural network.
(Claim 16)
One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations for training a neural network having a plurality of network parameters to perform a particular neural network task and to determine trained values of the network parameters using an iterative training process having a plurality of hyperparameters, the operations comprising: maintaining a plurality of candidate neural networks and, for each of the plurality of candidate neural networks, data specifying: (i) values of the network parameters of the candidate neural network, (ii) values of the hyperparameters of the candidate neural network, and (iii) a quality measure that measures a performance of the candidate neural network on the particular neural network task; for each of the plurality of candidate neural networks: repeatedly updating the values of the network parameters of the candidate neural network in accordance with the maintained values of the hyperparameters of the candidate neural network until a termination criterion is satisfied, wherein the maintained values of the hyperparameters remain unchanged; updating the respective values of the hyperparameters of the candidate neural network comprising: setting (i) the hyperparameters of the candidate neural network or (ii) hyperparameters of another candidate neural network sampled from the plurality of candidate neural networks as the updated hyperparameters of the candidate neural network, or permuting (i) the hyperparameters of the candidate neural network or (ii) the hyperparameters of the other candidate neural network to obtain the updated hyperparameters of the candidate neural network, wherein the other candidate neural network has respective values of the network parameters; updating the maintained data of the candidate neural network to specify the updated values of the hyperparameters and the updated values of the network parameters; and selecting the trained values of the network parameters from the parameter values in the maintained data after the training operations have been repeatedly performed.
(Claim 15)
One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations for training a neural network having a plurality of network parameters to perform a particular neural network task and to determine trained values of the network parameters using an iterative training process having a plurality of hyperparameters, the operations comprising: maintaining a plurality of candidate neural networks and, for each of the plurality of candidate neural networks, data specifying: (i) values of the network parameters of the candidate neural network, (ii) values of the hyperparameters of the candidate neural network, and (iii) a quality measure that measures a performance of the candidate neural network on the particular neural network task; for each of the plurality of candidate neural networks, repeatedly performing the following training operations, comprising: repeatedly updating the values of the network parameters of the candidate neural network in accordance with the maintained values of the hyperparameters of the candidate neural network until a termination criterion is satisfied, wherein the maintained values of hyperparameters remain unchanged; updating the quality measure of the candidate neural network based on the updated values of the network parameters of the candidate neural network; updating the respective values of the hyperparameters of the candidate neural network based on the updated quality measure of the candidate neural network, the updating comprising: sampling another candidate neural network from the plurality of candidate neural networks, the other candidate neural network having respective values of the network parameters and a respective quality measure for the respective values of the network parameters; and updating the hyperparameters of the candidate neural network based on a result of comparing the updated quality measure of the candidate neural network and the respective quality measure of the other candidate neural network; updating the maintained data of the candidate neural network to specify the updated values of the hyperparameters, the updated values of the network parameters, and the updated value of the quality measure; and selecting the trained values of the network parameters from the parameter values in the maintained data based on the maintained quality measures for the plurality of candidate neural networks after the training operations have repeatedly been performed.
(Claim 17)
The one or more non-transitory computer-readable storage media of claim 16, wherein selecting the trained values of the network parameters comprises: selecting the maintained parameter values of the candidate neural network having the highest quality measure among the plurality of candidate neural networks after the training operations have been repeatedly performed.
(Claim 16)
The one or more non-transitory computer-readable storage media of claim 15, wherein selecting the trained values of the network parameters from the parameter values in the maintained data based on the maintained quality measures of the plurality of candidate neural networks comprises: selecting the maintained parameter values of the candidate neural network having a best maintained quality measure of any of the plurality of candidate neural networks after the training operations have repeatedly been performed.
(Claim 18)
The one or more non-transitory computer-readable storage media of claim 16, wherein the operations further comprise: prior to updating the respective values of the hyperparameters of the candidate neural network, updating the quality measure of the candidate neural network based on the updated values of the network parameters of the candidate neural network.
(Claim 19)
The one or more non-transitory computer-readable storage media of claim 18, wherein the operations further comprise: in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure greater than the updated quality measure of the candidate neural network, permuting the hyperparameters of the other candidate neural network according to a pre-determined factor or distribution to obtain the updated hyperparameters of the candidate neural network; or in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure less than the updated quality measure of the candidate neural network, permuting the hyperparameters of the candidate neural network according to the pre-determined factor or distribution to obtain the updated hyperparameters of the candidate neural network.
(Claim 17)
The one or more non-transitory computer-readable storage media of claim 15, wherein updating the hyperparameters of the candidate neural network based on the result of comparing the updated quality measure of the candidate neural network and the respective quality measure of the other candidate neural network comprises: determining whether the respective quality measure of the other candidate neural network is greater than the updated quality measure of the candidate neural network; and in response to determining that the respective quality measure of the other candidate neural network is greater than the updated quality measure of the candidate neural network, setting new values of the hyperparameters of the candidate neural network to the maintained values of the hyperparameters of the other candidate neural network.
(Claim 20)
The one or more non-transitory computer-readable storage media of claim 18, wherein the operations further comprise: in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure greater than the updated quality measure of the candidate neural network, setting the hyperparameters of the other candidate neural network as the updated hyperparameters of the candidate neural network; or in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure less than the updated quality measure of the candidate neural network, maintaining the hyperparameters of the candidate neural network.
(Claim 21)
The one or more non-transitory computer-readable storage media of claim 18, wherein the operations further comprise: in response to determining that the other candidate neural network sampled from the plurality of candidate neural networks has a quality measure greater than a threshold quality measure, setting the hyperparameters of the other candidate neural network as the updated hyperparameters of the candidate neural network.
Conclusion
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/JEAN B JEANGLAUDE/Primary Examiner, Art Unit 2845
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