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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 6/27/2024 and 5/26/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 11 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of the “computer-readable storage medium” encompasses both transitory/signal media and non-transitory media. Therefore, it could be interpreted as a signal and is not limited to the statutory non-transitory media. It is suggested that claim 11 be amended to recite only a “non-transitory” computer-storage readable medium to overcome this rejection.
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, 11, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Zappella (US 20240112011 A1) in view of Chen (US 20240193429 A1).
Regarding claim 1, Zappella discloses “storing part of previous training data, used in previous training, in memory” (See [0090], [0096], FIG. 1, FIG. 6; Zappella discloses storing previously seen training data 132 as batches 610, and training data 132 is stored in storage service 114)
“for an artificial neural network trained using the previous training data… based on… first batch data sampled from the memory and… second batch data including part of new training data” (See [0096]; Zappella discloses using training data from the previously seen batches of training data (first batch data) and the current batch of training data (second batch data))
“training the artificial neural network based on the first batch data, the second batch data…” (See [0096]; Zappella discloses training a model using previously seen batches of training data (first batch data) and a current batch of training data (second batch data))
Zappella fails to explicitly disclose, “computing adaptive learning rates to be applied to the artificial neural network based on a first gradient for first batch data sampled from the memory and a second gradient for second batch data including part of new training data”
“training the artificial neural network based on… the adaptive learning rates”.
Chen teaches “computing adaptive learning rates to be applied to the artificial neural network based on a first gradient for first batch data sampled from the memory and a second gradient for second batch data including part of new training data” (See [0034], [0036]; Chen discloses that an adaptive learning rate is computed on stochastic gradients using Adaptive Moment Estimation (Adam) for each training data batch).
“training the artificial neural network based on the first batch data, the second batch data…” (See [0001], [0012]; Chen discloses using Adaptive Moment Estimation (Adam) to determine adaptive learning rates to train a neural network)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having Zappella and Chen before them to modify Zappella to compute adaptive learning rates based on the previous and current batches of training data. One would be motivated to compute adaptive learning rates using training data to optimize neural network training, see e.g., [0001] and [0034], where Chen teaches that using an adaptive learning optimizer to determine an adaptive learning rate increases the efficiency of training neural networks.
Regarding claims 11 and 12, these claims are similar in scope to claim 1.
Claim Rejections - 35 USC § 103
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Zappella (US 20240112011 A1) in view of Chen (US 20240193429 A1), and further in view of Li (A Zeroth-Order Adaptive Learning Rate Method to Reduce Cost of Hyperparameter Tuning for Deep Learning).
Regarding claim 2, Zappella-Chen fails to explicitly disclose, “computing the adaptive learning rates comprises computing a first adaptive learning rate to be reflected in the first gradient and a second adaptive learning rate to be reflected in the second gradient based on an inner product of the first and second gradients”.
Li teaches “computing the adaptive learning rates comprises computing a first adaptive learning rate to be reflected in the first gradient and a second adaptive learning rate to be reflected in the second gradient based on an inner product of the first and second gradients” (See [Page 6, Section 3.1, Paragraphs 1-3]; Li discloses computing learning rates using an inner product on an expected gradient and an observed gradient to obtain an optimal learning rate).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having Zappella-Chen and Li before them to modify Zappella-Chen to compute the adaptive learning rate by using an inner product. One would be motivated to use an equation or algorithm to obtain the optimal learning rate whenever a learning rate needs to be computed to adapt to the current training data batch, see e.g., [Page 6, Section 3.1, Paragraphs 1-3], where Li teaches obtaining the optimal learning rate with an equation that uses an inner product of two gradients.
Claim Rejections - 35 USC § 103
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Zappella (US 20240112011 A1) in view of Chen (US 20240193429 A1), and further in view of Chaudhry (On Tiny Episodic Memories in Continual Learning).
Regarding claim 10, Zappella-Chen fails to explicitly disclose, “wherein the memory stores the previous training data by using a ring buffer or reservoir sampling method”.
Secondary teaches “wherein the memory stores the previous training data by using a ring buffer or reservoir sampling method” (See [Page 2, Section 1, Paragraph 3], [Pages 3-4, Section 4]; Chaudhry discloses storing previous training data and examples in a ring buffer and a reservoir sampling data as memory of past training tasks).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having Zappella-Chen and Chaudhry before them to modify Zappella-Chen to store previous training data in a ring buffer or reservoir sampling method. One would be motivated to use a more effective method of storing data in a data structure that is designed to act as memory while using an organizational method, such as First In, First Out (FIFO) method, see e.g., [Page 4, Section 4, Ring Buffer], where Chaudhry teaches using a Ring Buffer as a memory buffer due to its ability to allocate equally sized buffers in a FIFO order.
Allowable Subject Matter
Claim 3 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. None of the prior art of recording, including Zappella, Chen, and Li, disclose the limitations of claim 4. Further, a complete and thorough search did not uncover any prior art.
Claim 4 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. None of the prior art of recording, including Zappella, Chen, and Li, disclose the limitations of claim 4. Further, a complete and thorough search did not uncover any prior art.
Claim 5 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. None of the prior art of recording, including Zappella, Chen, and Li, disclose the limitations of claim 5. Further, a complete and thorough search did not uncover any prior art.
Claim 6 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. None of the prior art of recording, including Zappella, Chen, and Li, disclose the limitations of claim 6. Further, a complete and thorough search did not uncover any prior art.
Claim 7 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. None of the prior art of recording, including Zappella, Chen, and Li, disclose the limitations of claim 7. Further, a complete and thorough search did not uncover any prior art.
Claim 8 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. None of the prior art of recording, including Zappella, Chen, and Li, disclose the limitations of claim 8. Further, a complete and thorough search did not uncover any prior art.
Claim 9 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. None of the prior art of recording, including Zappella, Chen, and Li, disclose the limitations of claim 9. Further, a complete and thorough search did not uncover any prior art.
Conclusion
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/D.K./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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