DETAILED 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 .
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 Kang et al. (hereinafter “Kang”), US Patent No. 10,410,114, in view of Padmanabhan et al. (hereinafter “Padmanabhan”), US Pub. No. 2017/0185904.
Regarding claim 1, Kang teaches a computer-implemented method for training a first machine learning model having a different architecture than a second machine learning model (fig. 2), the method comprising: receiving a first data set (fig. 2, student model; the request in Kang implicitly identifies the input type, output type and training data when selecting the student model where these criteria come with the training of the selected student model); performing one or more operations to generate a second data set based on the first data set and the second machine learning model (fig. 2, the implicit data of the student model and the teacher models 205-225), wherein the second data set includes at least one feature associated with one or more tasks that the second machine learning model was previously trained to perform (fig. 2, teacher models 205-225 are all pre-trained models); and performing one or more operations to train the first machine learning model based on the second data set and the second machine learning model (fig. 2, training the student model using the teacher model).
As mentioned above, Kang implicitly teaches receiving a first data set but fails to explicitly teach a first data set.
However, in the same field of endeavor, Padmanabhan teaches explicitly requesting an on-demand building of a machine learning model, wherein a user inputs one or more data sources for building a machine learning model, and receiving training data (see fig. 2, elements 202, 204, and 206; paragraphs [0060-0061].
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Kang to include the feature of Padmanabhan. As such, a person having ordinary skill in the art would appreciate the motivation for doing so would have been to build a more efficient predictive model (Padmanabhan, [0007]).
Regarding claim 2, Kang teaches wherein performing the one or more operations to train the first machine learning model comprises optimizing a loss function that includes a first term that minimizes a distance between an output of the first machine learning model and an output of the second machine learning model and a second term used to train the first machine learning model to perform one or more tasks that the second machine learning model was not previously trained to perform (col. 10, lines 6-30).
Regarding claim 3, Kang teaches wherein the first term comprises a Kullback-Leibler (KL) divergence, and the second term comprises a soft cross entropy (col. 11, lines 16-33).
Regarding claim 4, Kang teaches further comprising performing one or more operations to augment the second data set based on one or more data augmentations (col. 10, lines 31-58).
Regarding claim 5, Padmanabhan teaches wherein the first data set is associated with at least one task that is not included in the one or more tasks that the second machine learning model was previously trained to perform (fig. 2 and accompanying text).
Regarding claim 6, Kang teaches wherein the first data set is associated with the at least one task that is included in the one or more tasks that the second machine learning model was previously trained to perform (fig. 2 and accompanying text).
Regarding claim 7, Kang teaches wherein performing one or more operations to generate the second data set comprises backpropagating one or more gradients to the first data set based on the one or more tasks that the second machine learning model was previously trained to perform (col. 10, lines 12-19).
Regarding claim 8, Padmanabhan teaches wherein the first data set does not include random noise (fig. 2, elements 202-218).
Regarding claim 9, Padmanabhan teaches wherein the one or more operations to train the first machine learning model are further based on the first data set (fig. 2, elements 202-218).
Regarding claim 10, Kang teaches further comprising performing one or more tasks using the first machine learning model subsequent to performing the one or more operations to train the first machine learning model (fig. 4, Pstudent).
Regarding claim 11, it is a non-transitory computer readable medium of claim 1 and is rejected on the same grounds presented above.
Regarding claim 12, it has similar limitations to those of claim 2 and is rejected on the same grounds presented above.
Regarding claim 13, it has similar limitations to those of claim 3 and is rejected on the same grounds presented above.
Regarding claim 14, it has similar limitations to those of claim 3 and is rejected on the same grounds presented above.
Regarding claim 15, it has similar limitations to those of claim 5 and is rejected on the same grounds presented above.
Regarding claim 16, it has similar limitations to those of claim 7 and is rejected on the same grounds presented above.
Regarding claim 17, Padmanabhan teaches wherein the first data set includes one or more images ([0043]).
Regarding claim 18, it has similar limitations to those of claim 9 and is rejected on the same grounds presented above.
Regarding claim 19, it has similar limitations to those of claim 10 and is rejected on the same grounds presented above.
Regarding claim 20, it is a system of claim 1 and is rejected on the same grounds presented above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Lupesko et al. (US Patent No. 11,763,154) teaches automated generation of a machine learning model based in part on a pretrained model.
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/KENNETH B LEE JR/Primary Examiner, Art Unit 2625