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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6 and 17-18 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Perez Vallejo et al (U.S. 2024/0020535), hereinafter Perez.
With regard to claim 1, Perez teaches a system for incremental domain adaptation, the system comprising: an iterative knowledge distillation module configured to adapt machine learning models to new tasks sequentially through multiple iterations of knowledge distillation ([0016], fig. 1, a transformer model 130; trained to learn parameters for improved parallel output token prediction); and an external memory bank configured to store parameters of the machine learning models pertaining to the new tasks (fig. 1, training data 140, [0024], 140 training data which are parameters used to train the mode).
With regard to claim 2, Perez teaches the system of claim 1. Perez further teaches wherein the machine learning models comprise a transformer architecture ([0016], transformer model architecture; [0022], architecture of the transformer model).
With regard to claim 3, Perez teaches the system of claim 2. Perez further teaches wherein a layer in the transformer architecture comprises a multi- head attention module ([0034], multiheaded attention) and a feed-forward layer downstream (fig. 2, feed-forward layer) from the multi-head attention module, and wherein the external memory bank is attached to the transformer architecture between the multi-head attention module and the feed-forward layer via a residual connection (fig. 2, input data; [0024], input token sequence of the training data; fig. 1, connection to training data 140).
With regard to claim 4, Perez teaches the system of claim 1. Perez further teaches wherein for each of the multiple iterations the machine learning models comprise a current machine learning model and an adapted machine learning model, and wherein the iterative knowledge distillation module is further configured to adapt the current machine learning model to a given one of the new tasks using a training dataset that represents the given new task to produce the adapted machine learning model ([0074], new input tokens for inference, corresponding to new tasks using training dataset; teacher-student model for learning).
With regard to claim 5, Perez teaches the system of claim 4. Perez further teaches wherein the iterative knowledge distillation module is further configured to distill the parameters of the adapted machine learning model pertaining to the given new task to the external memory bank ([0016], teacher-student distillation outputs tokens in fewer iterations).
With regard to claim 6, Perez teaches the system of claim 5. Perez further teaches wherein the parameters of the adapted machine learning model pertaining to the given new task are stored in memory slots of the external memory bank ([0058], the token distributions are stored for each selected token, inherently in memory slots; fig. 4/5).
Claim 17 corresponds to claim 1, and is analyzed accordingly.
With respect to claim 18, Perez teaches the method of claim 17. Perez further teaches: adapting, for each of the multiple iterations, a current one of the machine learning models to a given one of the new tasks using a training dataset that represents the given new task to produce an adapted one of the machine learning models; and distilling the parameters of the adapted one of the machine learning models pertaining to the given new task to the external memory bank ([0016], iterative model with teacher-student distillation to output the tokens in few iterations).
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 7-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Perez Vallejo et al (U.S. 2024/0020535), in view of Riley et al (U.S. 2022/0253338), hereinafter Riley.
With respect to claims 7, 8, and 9, Perez teaches the system of claim 6. Perez fails to specifically teach wherein the memory slots, when filled, are frozen, wherein the external memory bank is further configured to add additional memory slots for the new tasks, and wherein a number of the memory slots added is varied on a task- by-task basis. Riley teaches wherein the memory slots, when filled, are frozen, wherein the external memory bank is further configured to add additional memory slots for the new tasks, and wherein a number of the memory slots added is varied on a task- by-task basis ([0018], [0019], memory is locked when executing a task, new tasks can use memory, and the tasks are varied on how much memory used. It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the system of Perez which uses memory to execute tasks with the teaching of Riley of adding/locking/varying memory for tasks in order to efficiently use the memory available as taught by Riley in the cited section and throughout.
Claim 10 corresponds to claims 1 and 9, and is analyzed accordingly.
Claim 11 corresponds to claims 10 and 3, and is analyzed accordingly.
Claim 12 corresponds to claims 10 and 4, and is analyzed accordingly.
Claim 13 corresponds to claims 12 and 5, and is rejected accordingly.
With respect to claim 14, Perez in view of Riley teach the system of claim 12. Perez further teaches wherein the number of the memory slots allocated to each of the new tasks is a function of at least one of a number of instances of the given new task in the training dataset, and discrepancy in zero-shot performance and fine-tuning performance on the given new task ([0016], teacher-student model for iterative learning and training for improved prediction; [0049], performed by setting the contribution of the subsequent tokens to zero when combining their respective values).
Claims 15-16 correspond to claims 10 and 7-8, and are analyzed accordingly.
Claim 19 corresponds to claim 17 and 8-9, and is analyzed accordingly.
Claim 20 corresponds to claims 19, 9, and 14, and is rejected accordingly.
Conclusion
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/MARK D FEATHERSTONE/Supervisory Patent Examiner, Art Unit 2111