DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 8/6/2025 for application number 17/499,271.
Claims 1-3, 8-10, and 15-17 are pending.
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.
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.
Claim(s) 1-3, 8-10, and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mueller et al. (US 2021/0326717 A1) in view of Rawat et al. (US 2022/0198222 A1) and Capelo et al. (US 2022/0108223 A1).
In reference to claim 1, Mueller teaches a system comprising: a memory storing processor-executable program code; a processing unit to execute the processor-executable program code (para. 0082) to cause the system to: receive a first request for a first inference result (request made for a deployed ML model to make an inference, para. 0089-91, fig. 9) generated by a first composite machine learning scenario comprising … a first plurality of machine learning models (configuration information can specify an ML model / pipeline, and the model can comprise an ensemble model; an ensemble model comprises a plurality of ML models, para. 0045, 0072); in response to the first request: determine first inferences from each of the first plurality of machine learning models (model can make an inference, para. 0089-91, fig. 9); populate a first instance of [an] … inference object based on the first inferences; and return the first populated instance of the … inference object (inference data can be stored in object storage, make it an inference object, para. 0091, 0052-55, and then returned to the user or other applications, para. 0091); receive a second request for a second inference result generated by a second composite machine learning scenario different from the first composite machine learning scenario, the second composite machine learning scenario … including a second plurality of machine learning models different from the first plurality of machine learning models; and in response to the second request: determine second inferences from each of the second plurality of machine learning models … ; populate a second instance of the … inference object based on the second inferences; and return the second populated instance of the … inference object (a plurality of ML models can be deployed and used, para. 0089, so it would be obvious that the above process would be repeated for a second ensemble model).
However, Mueller does not explicitly teach a first composite machine learning scenario comprising a first plurality of machine learning scenarios including a first plurality of machine learning models; and the second composite machine learning scenario comprising a second plurality of machine learning scenarios including a second plurality of machine learning models.
Rawat teaches a first composite machine learning scenario comprising a first plurality of machine learning scenarios including a first plurality of machine learning models; and the second composite machine learning scenario comprising a second plurality of machine learning scenarios including a second plurality of machine learning models (ensemble model comprises a plurality of ML pipeline components, which are scenarios, para. 0017-22, and the ML pipeline components comprise a plurality of ML models, para. 0020, 22, 34-38).
It would have been obvious to one of ordinary skill in art, having the teachings of Mueller and Rawat before the earliest effective filing date, to modify the scenario as disclosed by Mueller to include the composite scenario as taught by Rawat.
One of ordinary skill in the art would have been motivated to modify the scenario of Mueller to include the composite scenario of Rawat because the composite scenarios of Rawat can help generate better ML models (Rawat, para. 0002-06).
However, Mueller and Rawat do not explicitly teach each of the first inferences having different respective first structures; populate a first instance of a generic inference object; each of the second inference having different respective second structures and each of the second structures being different from each of the first structures; populate a second instance of the generic inference object.
Capelo teaches each of the first inferences having different respective first structures (each model in ensemble can have incompatible or different outputs, para. 0024-26, 0067, called a model specific output or MSO, para. 0021); populate a first instance of a generic inference object (model-specific output is converted to standardized output, para. 0119-27); each of the second inference having different respective second structures and each of the second structures being different from each of the first structures; populate a second instance of the generic inference object (user can request outputs from two different model subsets that do not intersect, para. 0086-87, i.e. the two ensembles will each have different models with different MSOs).
It would have been obvious to one of ordinary skill in art, having the teachings of Mueller, Rawat, and Capelo before the earliest effective filing date, to modify the outputs as disclosed by Mueller to include the generic inference object as taught by Capelo.
One of ordinary skill in the art would have been motivated to modify the outputs of Mueller to include the generic inference object of Capelo because it helps allow disparate ML models to be interoperable (Capelo, para. 0023-26).
In reference to claim 2, Mueller and Rawat do not explicitly teach a system according to Claim 1, wherein at least two of the plurality of machine learning scenarios are trained on different machine learning platforms.
Capelo teaches a system according to Claim 1, wherein at least two of the first plurality of machine learning scenarios are trained on different machine learning platforms and at least two of the second plurality of machine learning scenarios are trained on different machine learning platforms (each model may be trained by different entities on different hardware, para. 0033, which are different platforms).
It would have been obvious to one of ordinary skill in art, having the teachings of Mueller, Rawat, and Capelo before the earliest effective filing date, to modify the training as disclosed by Mueller to include the different platforms as taught by Capelo.
One of ordinary skill in the art would have been motivated to modify the training of Mueller to include the different platforms of Capelo because it helps allow disparate ML models to be interoperable (Capelo, para. 0023-26).
In reference to claim 3, Rawat discloses a System according to Claim 1, wherein at least two of the plurality of machine learning scenarios are trained on same platform and based on a same training dataset, and wherein the processing unit is to execute the processor-executable program code to cause the system to transmit the same training dataset to the same platform only once (see process in fig. 2: dataset is sent to platform only once at step 202).
In reference to claim 8, this claim is directed to a method associated with the system claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 9, this claim is directed to a method associated with the system claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 10, this claim is directed to a method associated with the system claimed in claim 3 and is therefore rejected under a similar rationale.
In reference to claim 15, this claim is directed to a non-transitory computer-readable medium associated with the system claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 16, this claim is directed to a non-transitory computer-readable medium associated with the system claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 17, this claim is directed to a non-transitory computer-readable medium associated with the system claimed in claim 3 and is therefore rejected under a similar rationale.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 8, and 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Specifically, please see new reference Capelo above, which teaches the new limitations.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144