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 .
Allowable Subject Matter
Claims 4 and 5 are 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 3, and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Blevins et al. US 2011/0288837 (hereinafter Blevins) in view of Song et al. US 2021/0234689 (hereinafter Song).
Regarding claim 1, Blevins teaches: a method for predicting product assembly quality based on longitudinal unified learning (Fig. 15, [0109] - - predict the final output quality; Fig. 4, [0073] - - process data during a time period along the time axis is longitudinal data), comprising:
performing sample alignment on a data sample of each participant ([0010] - - “the data for each stage, operation, or phase of a batch must be aligned with comparable data from the same stage, operation or phase of the other batches used to create the model”),;
training the aligned data sample of each participant to obtain a local model of the participant, and extracting and aggregating, based on the local model, data features generated by different devices in the corresponding participant ([0022] - - train a model of the first process stage; [0104] - - extract data from a data historian containing data from previous batch runs);
performing gradient security aggregation on the local model of each participant by using a multi-player computation, to obtain a global mode (Fig. 14,Fig. 15 show a multi-layer computation; [0104]-[0109] - - predict the final output quality); and
merging the extracted and aggregated data features generated by different devices in each participant, and training the global model with merged data; and finally predicting the product assembly quality by using the trained global model (Fig. 14,Fig. 15, [0104]-[0109] - - predict the final output quality; the multi-stage model is a global model).
But Blevins does not explicitly teach:
an encryption policy
a homomorphic encipherment
However, Song teaches:
an encryption policy (Abstract - - a homomorphic encryption algorithm)
a homomorphic encipherment (Abstract - - a homomorphic encryption algorithm)
Blevins and Song are analogous art because they are from the same field of endeavor. They all relate to data analysis system.
Therefore before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Blevins, and incorporating encrypt data, as taught by Song.
One of ordinary skill in the art would have been motivated to do this modification in order to improve security, as suggested by Song ([0033]).
Claim 6 is substantially similar to claim 1 and is rejected for the same reasons and rationale as above.
Regarding claim 2, the combination of Blevins and Song teaches all the limitations of the base claims as outlined above.
Blevins further teaches: performing alignment operation on the data sample ([0010] - - “the data for each stage, operation, or phase of a batch must be aligned with comparable data from the same stage, operation or phase of the other batches used to create the model”).
Song further teaches: performing homomorphic encipherment operation on the data sample of each participant by using the homomorphic encipherment method, and then performing operation on the data sample in a ciphertext state (Abstract - - using a homomorphic encryption algorithm to generate a cipher text).
Blevins and Song are combinable for the same rationale as set forth.
Regarding claim 3, the combination of Blevins and Song teaches all the limitations of the base claims as outlined above.
Blevins further teaches: subjecting the aligned data sample of each participant to a multi-player parallel structure two rounds in the local model, and extracting and aggregating the data features (Fig. 12, [0100] - - illustrated in the right-hand portion of Fig. 12, model M1 is constructed using data blocks X1, X2,…Xn; model M2 is constructed using data blocks X2, X3,…Xn. Thus data X2 is used both in model M1 and M2. Therefore it is used two rounds in the local model. M1, M2 are local models)
Conclusion
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/YUHUI R PAN/Primary Examiner, Art Unit 2116
