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 .
Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim(s) 1, 9 and 15, each recite(s) “sparse number of non-zero weights”.
The term “sparse” in claims 1, 9 and 15 is a relative term, which renders the claim indefinite. The term “1, 9 and 15” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim(s) 4, 11 and 17, each recite(s) “calculating correlation parameters between the trained first plurality of neural sub-networks”. It is unclear, between “the trained first plurality of neural sub-networks” and what, the correlation parameters are calculated, rendering the claim(s) indefinite.
For examination purposes, based on applicant’s specification, the examiner has interpreted “calculating correlation parameters between the trained first plurality of neural sub-networks”
to be “calculating correlation parameters between the trained first plurality of neural sub-networks and training data”.
Claim(s) 4, 11, 17, 7, each recite(s) “the first plurality of networks”. There is lack of antecedent basis for this limitation in these claim(s), rendering the claim(s) indefinite.
For examination purposes, based on applicant’s specification, the examiner has interpreted “the first plurality of networks” to be “the first plurality of sub-networks”
Claim(s) 6, 13 and 19, each recite(s) “mediocre performance”. The term “mediocre” in claims 6, 13 and 19 is a relative term which renders the claims indefinite. The term “mediocre” is not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim(s) 7, 8, 14, 20, each recite(s) level(s) of sub-networks. It is unclear what constitutes a “level” in the context of a sub-network, rendering the claim(s) indefinite.
Claim(s) 11 recite(s) “the re-training”. There is lack of antecedent basis for this limitation in these claim(s), rendering the claim(s) indefinite.
For examination purposes the examiner has interpreted “The processing device of claim 9 the re-training further includes re-training the neural network for the new task”
to be “The processing device of claim 9 further including re-training the neural network for the new task”.
Claim(s) 14 recite(s) “the plurality of neural sub-networks”. It is unclear whether “the plurality of neural sub-networks” refers to “the first plurality of neural sub-networks” or “the second plurality of neural sub-networks” or both, rendering the claim(s) indefinite.
Claim(s) 15 recite(s) “the third plurality of networks”. There is lack of antecedent basis for this limitation in these claim(s), rendering the claim(s) indefinite.
Claim(s) 15 recite(s) “creating the neural network by superpositioning non-zero weights in multi-dimensional nodes of the neural network ones of the third plurality of networks”. It is unclear whether “ones” refers to instances of nodes or instances of the third plurality, rendering the claim(s) indefinite.
For examination purposes the examiner has interpreted “creating the neural network by superpositioning non-zero weights in multi-dimensional nodes of the neural network ones of the third plurality of networks”
to be “creating the neural network by superpositioning non-zero weights in the third number of multi-dimensional nodes of the second plurality of sub-networks”.
Claim(s) 20 recite(s) “the first and second plurality of networks”. There is lack of antecedent basis for this limitation in these claim(s), rendering the claim(s) indefinite.
For examination purposes the examiner has interpreted “the first and second plurality of networks” to be “the first and second plurality of sub-networks”.
Claim(s) 2-8, 10-14 and 16-20 do not contain claim limitations that cure the indefiniteness of claim(s) 1, 9 and 15 respectively, and therefore are also indefinite under 35 U.S.C. 112(b).
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Garcia (US20190258931A1), in view of Cheung et al “Superposition of many models into one”, ARXIV.ORG dated 2/14/2019, XP081369698.
Garcia and Cheung were cited in the IDS filed on 10/12/2025.
Regarding claim 1, Garcia teaches a computer implemented method of training a neural network comprising a number nodes, comprising (Garcia [Abstract, 14, 83, 104] computer implemented method to train neural network(s) with neurons (nodes)):
instantiating a first plurality of pre-trained neural sub-networks each having a first number of multi-dimensional nodes, at least some of the multi-dimensional nodes having non-zero weights (Garcia [32-36, 60, 61] pre-trained sub-networks of base network have multi-dimensional nodes (neurons) with weights that may or may not be zero);
up-scaling ones of the first plurality of pre-trained neural sub-networks to have a second, larger number of multi-dimensional nodes such that ones of the first plurality of pre-trained neural sub-networks have a sparse number of non-zero weights associated with the second, larger number of multi-dimensional nodes (Garcia [7, 17, 18, 84, 85, 90] sparse sub-networks may be modified to increase dimensionality (upscale));
creating the neural network ... based on ... non-zero weights of the plurality of pre-trained neural sub-networks by representing the non-zero weights in multi-dimensional nodes of the neural network (Garcia [49, 61, 68, 71] modified neural network is generated using the modified layer(s));
receiving data for a first task for computation by the neural network; and executing the first task to generate a solution to the first task from the neural network (Garcia [4, 74] input data for task may be received, and modified network provides output data for the task).
Garcia does not specifically teach creating the neural network by superpositioning non-zero weights of the plurality of pre-trained neural sub-networks by representing the non-zero weights in multi-dimensional nodes of the neural network
However Cheung teaches creating the neural network by superpositioning non-zero weights of the plurality of pre-trained neural sub-networks by representing the non-zero weights in multi-dimensional nodes of the neural network (Cheung Sec1 Last 2 Para, Sec2.0 models based on same model for different tasks may be stored simultaneously with a model by superpositioning weights for nodes, no need for separate networks, Cheung Secs 4 - 4.1.1, 5, models may be previously trained, diminished catastrophic forgetting).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Cheung of creating the neural network by superpositioning non-zero weights of the plurality of pre-trained neural sub-networks by representing the non-zero weights in multi-dimensional nodes of the neural network, into the invention suggested by Garcia; since both inventions are directed towards adapting a model to different to different tasks, and incorporating the teaching of Cheung into the invention suggested by Garcia would provide the added advantage of diminished catastrophic forgetting and eliminating the need for separate models for different tasks, and the combination would perform with a reasonable expectation of success (Cheung Sec1-Last2 Para, Sec2.0, Secs 4 - 4.1.1, 5).
Regarding claim 2, Garcia and Cheung teach the invention as claimed in claim 1 above.
Garcia further teaches creating a second plurality of neural sub-networks having the second, larger number of multi-dimensional nodes ... based on ... non-zero weights of the first plurality of neural sub-networks(Garcia [49, 61, 68, 71] modified neural network is generated using the modified layer(s)); and
creating the neural network having multi-dimensional nodes ... based on ... non-zero weights of the second plurality of neural sub-networks into nodes of the neural network (Garcia [4, 74] input data for task may be received, and modified network provides output data for the task).
Garcia does not specifically teach superpositioning non-zero weights of the first plurality of neural sub-networks; and creating the neural network having multi-dimensional nodes by superpositioning non-zero weights of the second plurality of neural sub-networks.
However Cheung teaches creating a second plurality of neural sub-networks ...by superpositioning non-zero weights of the first plurality of neural sub-networks; and creating the neural network having multi-dimensional nodes by superpositioning non-zero weights of the second plurality of neural sub-networks (Cheung Sec1 Last 2 Para, Sec2.0 models based on same model for different tasks may be stored simultaneously with a model by superpositioning weights for nodes, no need for separate networks).
Regarding claim 3, Garcia and Cheung teach the invention as claimed in claim 1 above. Garcia further teaches re-training the neural network for a new task by replacing at least a subset of the first plurality of neural sub-networks for the new task (Garcia [57, 61, 71-73, 4] subset of sub-networks may be replaced for a task, modified network may be retrained).
Regarding claim 4, Garcia and Cheung teach the invention as claimed in claim 3 above.
Garcia further teaches re-training further includes re-training the neural network for the new task by: training each of the first plurality of networks with the training data of the new task; and replacing ones of the first plurality of neural sub-networks with re-trained neural sub-networks (Garcia [57, 61, 71-73, 4] subset of sub-networks may be replaced for a task, modified network may be retrained.
Garcia does not specifically teach calculating correlation parameters between the trained first plurality of neural sub-networks; predicting an empirical distribution of labels in training data of a new task based on the first task.
However Cheung teaches calculating correlation parameters between the trained first plurality of neural sub-networks (Cheung Sec1-Last2Para, Sec4.0-LastPara-Sec 4.1 correlation between trained sub-networks and input data may be determined);
predicting an empirical distribution of labels in training data of a new task based on the first task (Cheung Sec 4.2, input data label distribution for task(s) may be based on existing tasks).
Regarding claim 5, Garcia and Cheung teach the invention as claimed in claim 3 above. Garcia further teaches wherein the replacing comprises replacing ones of the first plurality of neural sub-networks when there are more than a maximum number of pre-trained neural sub-networks (Garcia [57, 61, 71-73, 4] subset of sub-networks may be replaced for a task, modified network may be retrained, more than one (more than a maximum number) may be replaced (Note: claim does not preclude replacing sub-networks if less than a particular number)).
Regarding claim 6, Garcia and Cheung teach the invention as claimed in claim 3 above. Garcia further teaches wherein the replacing comprises replacing neural sub-networks having mediocre performance as determined relative to training data for the new task (Garcia [63, 64] performance of sub-networks for new task may be taken into account in replacing (Note: claim does not preclude replacing sub-networks if performance is not mediocre)).
Regarding claim 7, Garcia and Cheung teach the invention as claimed in claim 1 above. Garcia further teaches connecting each of the first plurality of neural sub-networks such that each of the first plurality of pre-trained neural sub-networks is connected to selective nodes of another of the first plurality of networks, the selective nodes being less than all of the plurality of nodes of the another of the first plurality of networks arranged in a first level of neural sub-networks comprising a sub-set of the first plurality of sub-networks (Garcia [33, 34, 87] sub-network nodes may or may not be connected to each other).
Regarding claim 8, Garcia and Cheung teach the invention as claimed in claim 7 above. Garcia further teaches connecting each of the sub-set of the first plurality of neural sub-networks in the first level to selective ones of nodes of the second plurality of neural sub-networks a second level of neural sub-networks comprising a sub-set of the first level
(Garcia [33, 34, 87] sub-network nodes may be connected to each other, some connections may be pseudo or drop-out layers (levels with all connections with sub-level of pseudo or drop-out layers)).
Regarding claim 9, Garcia teaches a processing device, comprising a non-transitory memory storage comprising instructions; and one or more processors in communication with the memory, wherein the one or more processors create a neural network by executing the instructions to (Garcia [Abstract, 14, 83, 104] computer implemented method to train neural network(s) with neurons (nodes), performed by computer executing (processor) instructions stored in memory):
instantiate at least a first plurality of pre-trained neural sub-networks, each having a first number of multi-dimensional nodes, at least some of the multi-dimensional nodes having non-zero weights (Garcia [32-36, 60, 61] pre-trained sub-networks of base network have multi-dimensional nodes (neurons) with weights that may or may not be zero);
up-scale each of the first plurality of pre-trained neural sub-networks to have a second, larger number of multi-dimensional nodes such that ones of the first plurality of pre-trained neural sub-networks have a sparse number of non-zero weights associated with the second, larger number of multi-dimensional nodes (Garcia [7, 17, 18, 84, 85, 90] sparse sub-networks may be modified to increase dimensionality (upscale)); and
create the neural network ... based on ... non-zero weights of the first plurality of neural sub-networks by representing the non-zero weights in multi-dimensional nodes of the neural network (Garcia [49, 61, 68, 71] modified neural network is generated using the modified layer(s)).
Garcia does not specifically teach create the neural network by superpositioning non-zero weights of the first plurality of neural sub-networks by representing the non-zero weights in multi-dimensional nodes of the neural network.
However Cheung teaches create the neural network by superpositioning non-zero weights of the first plurality of neural sub-networks by representing the non-zero weights in multi-dimensional nodes of the neural network(Cheung Sec1 Last 2 Para, Sec2.0 models based on same model for different tasks may be stored simultaneously with a model by superpositioning weights for nodes, no need for separate networks, Cheung Secs 4 - 4.1.1, 5, models may be previously trained, diminished catastrophic forgetting).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Cheung of create the neural network by superpositioning non-zero weights of the first plurality of neural sub-networks by representing the non-zero weights in multi-dimensional nodes of the neural network, into the invention suggested by Garcia; since both inventions are directed towards adapting a model to different to different tasks, and incorporating the teaching of Cheung into the invention suggested by Garcia would provide the added advantage of diminished catastrophic forgetting and eliminating the need for separate models for different tasks, and the combination would perform with a reasonable expectation of success (Cheung Sec1-Last2 Para, Sec2.0, Secs 4 - 4.1.1, 5).
Regarding claim 10, Garcia and Cheung teach the invention as claimed in claim 9 above. Garcia further teaches re-train the neural network for a new task by replacing at least a subset of the first plurality of neural sub-networks for the new task (Garcia [57, 61, 71-73, 4] subset of sub-networks may be replaced for a task, modified network may be retrained).
Regarding claim 11, Garcia and Cheung teach the invention as claimed in claim 9 above. Garcia further teaches re-training the neural network for the new task by executing instructions to: train each of the first plurality of networks with the training data of the new task; and replace ones of the first plurality of neural sub-networks with re-trained neural sub-networks (Garcia [57, 61, 71-73, 4] subset of sub-networks may be replaced for a task, modified network may be retrained.
Garcia does not specifically teach calculate correlation parameters between the trained first plurality of neural sub-networks; predict an empirical distribution of labels in training data of a new task based on the new task;
However Cheung teaches calculate correlation parameters between the trained first plurality of neural sub-networks (Cheung Sec1-Last2Para, Sec4.0-LastPara-Sec 4.1 correlation between trained sub-networks and input data may be determined);
predict an empirical distribution of labels in training data of a new task based on the new task (Cheung Sec 4.2, input data label distribution for task(s) may be based on existing tasks).
Regarding claim 12, Garcia and Cheung teach the invention as claimed in claim 10 above. Garcia further teaches replacing ones of the first plurality of neural sub-networks when there are more than a maximum number of pre-trained neural sub-networks(Garcia [57, 61, 71-73, 4] subset of sub-networks may be replaced for a task, modified network may be retrained, more than one (more than a maximum number) may be replaced (Note: claim does not preclude replacing sub-networks if less than a particular number)).
Regarding claim 13, Garcia and Cheung teach the invention as claimed in claim 10 above. Garcia further teaches replacing at least a subset of the first plurality of neural sub-networks for the new task comprises replacing neural sub-networks having mediocre performance as determined relative to training data for the new task (Garcia [63, 64] performance of sub-networks for new task may be taken into account in replacing (Note: claim does not preclude replacing sub-networks if performance is not mediocre)).
Regarding claim 14, Garcia and Cheung teach the invention as claimed in claim 9 above.
Garcia further teaches create a second plurality of neural sub-networks having a second, larger number of multi-dimensional nodes ... based on... non-zero weights of the first plurality of neural sub-networks (Garcia [49, 61, 68, 71] modified neural network is generated using the modified layer(s), (Garcia [4, 74] input data for task may be received, and modified network provides output data for the task); and
connect each of the first plurality of neural sub-networks such that each of the first plurality and the second plurality of neural sub-networks is connected to selective nodes of another of the first plurality of neural sub-networks, the selective nodes being less than all of the nodes of the another of the plurality of neural sub-networks such that multiple ones of the plurality of neural sub-networks are arranged in a level of neural sub-networks, the connected selective ones creating at least two levels of recursive connections of the first plurality of neural sub-networks (Garcia [33, 34, 40, 87] Fig. 2, sub-network nodes may be connected to each other, some connections may be pseudo or drop-out layers (levels with all connections with sub-level of pseudo or drop-out layers, some weights may be reused)).
Garcia does not specifically teach superpositioning non-zero weights of the first plurality of neural sub-networks.
However Cheung teaches create a second plurality of neural sub-networks ... by superpositioning non-zero weights of the first plurality of neural sub-networks (Cheung Sec1 Last 2 Para, Sec2.0 models based on same model for different tasks may be stored simultaneously with a model by superpositioning weights for nodes, no need for separate networks).
Regarding claim 15, Garcia teaches a non-transitory computer-readable medium storing computer instructions to train a neural network, that when executed by one or more processors, cause the one or more processors to perform the steps of: (Garcia [Abstract, 14, 83, 104] computer implemented method to train neural network(s) with neurons (nodes), performed by computer executing (processor) instructions stored in memory):
training a plurality of neural sub-networks each having a first number of multi-dimensional nodes by instantiating a first plurality of pre-trained neural sub-networks, each having a first number of multi-dimensional nodes, at least some of the multi-dimensional nodes having non-zero weights (Garcia [32-36, 60, 61] pre-trained sub-networks of base network have multi-dimensional nodes (neurons) with weights that may or may not be zero);
up-scaling ones of the first plurality of pre-trained neural sub-networks to have a second, larger number of multi-dimensional nodes such that each of the first plurality of pre-trained neural sub-networks have a sparse number of non-zero weights associated with the second, larger number of multi-dimensional nodes (Garcia [7, 17, 18, 84, 85, 90] sparse sub-networks may be modified to increase dimensionality (upscale));
creating a second plurality of neural sub-networks having the second, larger number of multi-dimensional nodes ... based on ... non-zero weights of the first plurality of neural sub-networks in the second plurality of neural sub-networks (Garcia [49, 61, 68, 71] modified neural network is generated using the modified layer(s))
up-scaling ones of the second plurality of neural sub-networks to have a third number of multi-dimensional nodes such that ones of the second plurality of sub-networks have a sparse number of non-zero weights associated with the third number of multi-dimensional nodes (Garcia [[56, 57], Fig. 5, multiple generations each from the previous modified network may be created, Garcia [7, 17, 18, 84, 85, 90] sparse sub-networks may be modified to increase dimensionality (upscale));; and
creating the neural network ... based on ... non-zero weights in multi-dimensional nodes of the neural network ones of the third plurality of networks (Garcia [[56, 57], Fig. 5, multiple generations each from the previous modified network may be created, Garcia [49, 61, 68, 71] modified neural network is generated using the modified layer(s));
receiving data for a first task for computation by the neural network; and computing the task data to generate a solution to the first task from the neural network (Garcia [4, 74] input data for task may be received, and modified network provides output data for the task).
Garcia does not specifically teach creating a second plurality of neural sub-networks .... by superpositioning non-zero weights of the first plurality of neural sub-networks in the second plurality of neural sub-networks; creating the neural network by superpositioning non-zero weights in multi-dimensional nodes of the neural network ones of the third plurality of networks
However Cheung teaches creating a second plurality of neural sub-networks ... by superpositioning non-zero weights of the first plurality of neural sub-networks in the second plurality of neural sub-networks; creating the neural network by superpositioning non-zero weights in multi-dimensional nodes of the neural network ones of the third plurality of networks (Cheung Sec1 Last 2 Para, Sec2.0 models based on same model for different tasks may be stored simultaneously with a model by superpositioning weights for nodes, no need for separate networks, Cheung Secs 4 - 4.1.1, 5, models may be previously trained, diminished catastrophic forgetting, previous superposition iteration may be used subsequently for another task).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Cheung of creating a second plurality of neural sub-networks ... by superpositioning non-zero weights of the first plurality of neural sub-networks in the second plurality of neural sub-networks; creating the neural network by superpositioning non-zero weights in multi-dimensional nodes of the neural network ones of the third plurality of networks, into the invention suggested by Garcia; since both inventions are directed towards adapting a model to different to different tasks, and incorporating the teaching of Cheung into the invention suggested by Garcia would provide the added advantage of diminished catastrophic forgetting and eliminating the need for separate models for different tasks, and the combination would perform with a reasonable expectation of success (Cheung Sec1-Last2 Para, Sec2.0, Secs 4 - 4.1.1, 5).
Regarding claim 16, Garcia and Cheung teach the invention as claimed in claim 15 above. Garcia further teaches re-train the neural network for a new task by replacing at least a subset of the first plurality of neural sub-networks for the new task (Garcia [57, 61, 71-73, 4] subset of sub-networks may be replaced for a task, modified network may be retrained).
Regarding claim 17, Garcia and Cheung teach the invention as claimed in claim 15 above.
Garcia further teaches re-training the neural network for the new task by executing instructions to: train each of the first plurality of networks with the training data of the new task; and replace ones of the first plurality of neural sub-networks with re-trained neural sub-networks (Garcia [57, 61, 71-73, 4] subset of sub-networks may be replaced for a task, modified network may be retrained.
Garcia does not specifically teach calculate correlation parameters between the trained first plurality of neural sub-networks; predict an empirical distribution of labels in training data of a new task based on the first task.
However Cheung teaches calculate correlation parameters between the trained first plurality of neural sub-networks (Cheung Sec1-Last2Para, Sec4.0-LastPara-Sec 4.1 correlation between trained sub-networks and input data may be determined);
predict an empirical distribution of labels in training data of a new task based on the first task (Cheung Sec 4.2, input data label distribution for task(s) may be based on existing tasks).
Regarding claim 18, Garcia and Cheung teach the invention as claimed in claim 16 above. Garcia further teaches replacing ones of the first plurality of neural sub-networks when there are more than a maximum number of pre-trained neural sub-networks (Garcia [57, 61, 71-73, 4] subset of sub-networks may be replaced for a task, modified network may be retrained, more than one (more than a maximum number) may be replaced (Note: claim does not preclude replacing sub-networks if less than a particular number)).
Regarding claim 19, Garcia and Cheung teach the invention as claimed in claim 16 above. Garcia further teaches replacing neural sub-networks having mediocre performance as determined relative to training data for the new task (Garcia [63, 64] performance of sub-networks for new task may be taken into account in replacing (Note: claim does not preclude replacing sub-networks if performance is not mediocre)).
Regarding claim 20, Garcia and Cheung teach the invention as claimed in claim 16 above.
Garcia further teaches connecting each of the first plurality of neural sub-networks such that each of the first plurality and the second plurality of neural sub-networks is connected to selective nodes of another of the first and second plurality of neural sub-networks, the selective nodes being less than all of the nodes of the first and second plurality of networks, such that multiple ones of the first and second plurality of neural sub-networks are arranged in a level of neural sub-networks, the connecting creating at least two levels of recursive connections of the first and second plurality of neural sub-networks(Garcia [33, 34, 87] sub-network nodes may be connected to each other, some connections may be pseudo or drop-out layers (levels with all connections with sub-level of pseudo or drop-out layers)).
Conclusion
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SANCHITA ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146
Prosecution Insights