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 .
Response to Arguments
Applicant's arguments filed 6/5/2026 have been fully considered but they are not persuasive.
The 101 rejection is withdrawn.
Applicant argues “that Mallya and Wang… do not teach… ‘filters in each layer… [and] compares in each layer the weight of a filter that has learned the task with the weight of a filter that is learning…” Remarks 13. Mallya teaches filters and Mallaya sec. 5.4 teaches that “[b]y using our method, we can control the number of pruned parameters at each layer, allowing one to make use of task-specific requirements…”
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.
Claims 1-3, 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning to Mallya and FEDERATED LEARNING WITH MATCHED AVERAGING to Wang et al
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning to Mallya, FEDERATED LEARNING WITH MATCHED AVERAGING to Wang et al and Early Stopping — But When? by Prechelt.
Mallya teaches claims 1, 5 and 6. A machine learning device comprising:
a weight storage unit that stores weights of a plurality of filters in each layer of a multi-layer convolutional neural network used to detect a feature of a task; (Mallya fig. 1 and sec. 3 p. 3 “The initial weights of a filter are depicted in gray in Figure 1 (a). … we obtain a network with sparse filters and minimal reduction in performance on Task I. The surviving parameters of Task I, those in gray in Figure 1 (b), are hereafter kept fixed.” Mallaya sec. 5.4 “By using our method, we can control the number of pruned parameters at each layer, al lowing one to make use of task-specific requirements, when available.”)
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a continual learning unit that trains the weights of the plurality of filters in response to an input task in continual learning; and (Mallya fig. 1 and sec. 3 p. 3 “The initial weights of a filter are depicted in gray in Figure 1 (a). … we obtain a network with sparse filters and minimal reduction in performance on Task I.” The training/re-training for task I-III is continual learning.)
a filter control unit that, after a predetermined epoch number has been learned (Learning happens for a number of epochs, the number of epochs is not learned.) in continual learning, compares the weight of a filter that has learned the task with in each layer the weight of a filter that is learning the task and extracts (Mallya p. 3 sec. 3 “pruning and re-training is about 1.5× longer than simple fine-tuning, as we generally re-train for half the training epochs…. The weights in a layer are sorted by their absolute magnitude, and the lowest 50% or 75% are selected for removal… By following the iterative training procedure, for a particular Task K, we obtain a filter that is the superposition of weights learned for that particular task and weights learned for all previous Tasks 1, · · · , K − 1.” The epoch number is “half the training epochs”. Mallaya sec. 5.4 “By using our method, we can control the number of pruned parameters at each layer, allowing one to make use of task-specific requirements, when available.”)
Mallya doesn’t teach an overlap filter.
However, Wang teaches how an algorithm extracts overlap filters having a similarity in weight equal to or greater than a predetermined threshold value as shared filters shared by tasks. (Wang p. 3 sec. 2.1 “Due to data heterogeneity, local model j’ may have neurons not present in the global model built from other local models, therefore we want to avoid “poor” matches by saying that if the optimal match has cost larger than some threshold value ε, instead of matching we create a new global neuron from the corresponding local one.” The cost is the similarity in weighs and it is extracted using a “Euclidian” distance. Id. Having a low cost means that there is a high similarity. The low cost neurons will be overlapped, no new neuron is created for the low cost neuronal overlap. That means when cost is below the threshold ε, that the similarity is over a threshold, and no new neuron is created, so the global/overlap neuron is maintained.)
Mallya, Wang and the claims all share filters. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to overlap similar filter neurons “to adapt to heterogeniety in the data…. [and] reduce[] the communications burden…” Wang sec. 1 p. 2.
Mallya teaches claim 2. The machine learning device according to claim 1, wherein the filter control unit leaves one of the overlap filters as the shared filter and initializes the weights of filters other than the shared filter. (Mallya fig. 1 below shows the initialization of non-shared weights, below with red arrows to show initialized non-shared weights.)
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Mallya teaches claim 3. The machine learning device according to claim 2, wherein the continual learning unit trains initialized weights of filters other than the shared filter in response to a further task in continual learning. (Mallya fig. 1 below shows weights that are not shared and then used for a new task in continual learning.)
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Mallya teaches claim 4. The machine learning device according to claim 1, wherein the predetermined epoch number (The instant specification paragraph 33 says that the predetermined epoch number is configured accordingly “(1) Loss is equal to or lower than a certain level (e.g., 0.75). (2) Accuracy is equal to or greater than a certain level (e.g., 0.75). (3) Both conditions (1) and (2) are met.” This is not a determination about an epoch number, this is a classical “stop when loss meets a criteria” or “stop when loss change rate meets a criteria” test. The broadest reasonable interpretation is stop training when loss change rate hits a condition, or another way of saying the same thing is that accuracy change rate hits a condition. (Mallya p. 3 sec. 3 “pruning and re-training is about 1.5× longer than simple fine-tuning, as we generally re-train for half the training epochs…. The weights in a layer are sorted by their absolute magnitude, and the lowest 50% or 75% are selected for removal… By following the iterative training procedure, for a particular Task K, we obtain a filter that is the superposition of weights learned for that particular task and weights learned for all previous Tasks 1, · · · , K − 1.” The epoch number is “half the training epochs”.)
Mallya doesn’t teach error change rate.
However, Prechelt teaches epoch number is determined based on a condition related to a change rate in loss defined as an error between an output value from a learning model and a correct answer given by training data or to a change rate in accuracy defined as an accuracy rate of an output value from a learning model. (Prechelt p. 57 sec. 2.2.1 “stop when the generalization error increased in s successive strips.” This is error over s strips, which is an error change rate, and “increased” is the condition.)
Prechelt, Mallya and the claims are machine learning algorithms with training. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to end training on error change rate increase because “such increases indicate the beginning of final overfitting…” Prechelt p. 57 sec. 2.2.1.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142
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