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 .
This action is in response to the arguments filed on 12/19/2024. Claims 1-7 and 9-19 are pending in the application and have been considered below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7 and 9-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The specification does not provide any support for the bold terms:
Claims 1, 10 and 19
“generating a job at an application server in communication with the computer
application based on the remaining datasets of the plurality of datasets;
calculating, by the application server, resource requirements of the job;
sending, based on the comparison, the job to either a central
processing unit queue in communication with a first machine learning
model stored in a first virtual container of a container system or a graphics
processing unit queue in communication with a second machine learning
model stored in a second virtual container of the container system;
training, either the first machine learning model within the first virtual container or the second machine learning model within the second virtual container to produce a custom predictive model, wherein training either the first machine learning model or the second machine learning model includes providing the remaining plurality of datasets with the probabilities, the respective features, and the respective importance scores, to the first or second machine learning model as training data, and training the first or second machine learning model on the training data for at least an epoch, wherein the training of either the first machine learning model or the second machine learning model includes minimizing a loss function; and
detecting a user interaction with the model inference module, wherein upon a detection of the user interaction with the model inference module the computer application is programmed to perform the step of:
determining a probability of a prediction of a dataset using the trained custom predictive model.”
Appropriate correction is required.
Examiner’s comments
For the record a complete prior art search was made for claims 1-19. No art rejection is made for these claims, they are only rejected under 35 USC 101 as explained above in this office action.
Below is the list of the closest prior arts disclosing different aspects of the claimed
invention:
Gray et al. (US 2016/0232457 A1) discloses a method for providing various user interfaces unified data science platform to visually prepare, build, deploy, visualize and manage models, their results and dataset but fails to teach various aspect of the claimed invention such as calculating, by the application server, resource requirements of the job, probability of prediction compared to probability threshold, exception queue and virtual container.
NAIR et al. (US 2020/0372342 A1) discloses a method for training a neural network but fails to teach various aspect of the claimed invention such as calculating, by the application server, resource requirements of the job, probability of prediction compared to probability threshold, exception queue and virtual container.
Swan et al. (US 12045693 B2) discloses a method for utilizing a scoring algorithm utilizing container for analyzing flexible machine learning inference for exchanging data for a computing device over a wireless network e.g. global system for mobile communication (GSM). However, Swan fails to teach various aspect of the claimed invention such as calculating, by the application server, resource requirements of the job, probability of prediction compared to probability threshold, exception queue and virtual container.
3) Faulhaber, Jr. et al. (US 11977958 B2) discloses a method for training and hosting network-accessible machine learning models but fails to teach various aspect of the claimed invention such as calculating, by the application server, resource requirements of the job, probability of prediction compared to probability threshold, exception queue and virtual container.
4) Faulhaber, Jr. et al. (US 10621019 B1) discloses a method for web services provider to interact with client on remote job execution but fails to teach various aspect of the claimed invention such as calculating, by the application server, resource requirements of the job, probability of prediction compared to probability threshold, exception queue and virtual container.
5) Faulhaber, Jr. et al. (US11948022 B2) discloses a method for a web services provider to interact with a client on remote job execution but fails to teach various aspect of the claimed invention such as calculating, by the application server, resource requirements of the job, probability of prediction compared to probability threshold, exception queue and virtual container.
6) Stefani et al. (US 11170309 B1) discloses a system used for routing machine learning model inferences but fails to teach various aspect of the claimed invention such as calculating, by the application server, resource requirements of the job, probability of prediction compared to probability threshold, exception queue and virtual container.
However, there is no prior art to cover the following limitation:
Claims 1, 10 and 19:
“comparing each probability of prediction of each dataset to a probability threshold;
sending, based on the comparison, datasets of the plurality of datasets to an
exception queue;
generating a job at an application server in communication with the computer
application based on the remaining datasets of the plurality of datasets;
calculating, by the application server, resource requirements of the job;
sending, based on the resource requirements of the job, the job to either a central
processing unit queue in communication with a first machine learning
model stored in a first virtual container of a container system or a graphics
processing unit queue in communication with a second machine learning
model stored in a second virtual container of the container system;
training, either the first machine learning model within the first virtual
container or the second machine learning model within the second virtual
container to produce a custom predictive model, wherein training either
the first machine learning model or the second machine learning model
includes providing the remaining plurality of datasets with the probabilities,
the respective features, and the respective importance scores, to the first or second machine learning model as training data, and training, either the first machine learning model within the first virtual container or the second machine learning model within the second virtual container to produce a custom predictive model, wherein training either the first machine learning model or the second machine learning model includes providing the remaining plurality of datasets with the probabilities, the respective features, and the respective importance scores to the first or second machine learning model as training data, and training the first or second machine learning model on the training data for at least an epoch, and
detecting a user interaction with the model inference module, wherein upon a detection
of the user interaction with the model inference module the computer application
is programmed to perform the step of:
determining a probability of a prediction of a dataset using the trained custom predictive model.”
Response to Applicant’s arguments
Applicant's arguments on file on 12/19//2024 with respect to 101 rejection of claims 1-7 and 9-19 have been considered and are persuasive.
Claim Rejections - 35 USC§ 101: Claims 1-19
The rejection under 35 U.S.C. §101 is respectfully withdrawn.
Conclusion
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/ABABACAR SECK/Examiner, Art Unit 2122
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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