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 5, 6, 8, 10, 15, 16, 18, and 20 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 § 102
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)(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 1-4, 7, 9, 11-14, 17, and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bramble et al. (US Pub. 20220207444).
Referring to claim 1, Bramble discloses A method comprising:
storing a plurality of artificial intelligence (AI) capabilities in a cloud environment [fig. 1; pars. 29-31; AI capabilities (e.g., pre-trained base models, library of algorithms, customized models, trained models) are provided by AutoML, which is a cloud-based ML service; this means that the AI capabilities are stored in the cloud];
while storing the plurality of AI capabilities [fig. 1; pars. 29-31; note the storing of the AI capabilities in the cloud]:
receiving, from a computing device of a user, a request for a particular AI capability of the plurality of AI capabilities [pars. 29-31 and 38; an end user specifies a prediction model (i.e., at least one of the AI capabilities)];
in response to receiving training data based on input from the user, storing the training data in a tenancy associated with the user in the cloud environment [pars. 30, 31, 38, 79, and 82-85; the end user provides input training data for customizing the prediction model; the input training data is imported by a system API at a service endpoint; a multi-tenant model is used to assign the end user computing resources, which means that the data would be imported to cloud storage assigned to a tenancy associated with the user that can be accessed by the system API; also note SaaS, PaaS, IaaS service models];
in response to receiving the request [pars. 29-31 and 38; note the specifying of the prediction model by the end user]:
accessing the particular AI capability [pars. 29-31 and 38; note the prediction model]
training a machine-learned (ML) model based on the particular AI capability and the training data to produce a trained ML model [pars. 29-31; the imported data is used to train or customize the prediction model (i.e., generate a customized prediction model)];
generating an endpoint, in the cloud environment, that is associated with the trained ML model [par. 31; once the customized prediction model is generated, the customized prediction model can be deployed or exported via the system API at the service endpoint, which means that a pointer to a cloud storage location of the customized prediction model is generated];
providing the endpoint to the tenancy associated with the user [pars. 31, 79, and 82-85; the system API at the service endpoint can access the customized prediction model, which is stored in the cloud using the multi-tenant model];
wherein the method is performed by one or more computing devices [figs. 1 and 5; par. 28; AutoML is implemented within a computing and data processing environment].
Referring to claim 2, Bramble discloses The method of Claim 1, wherein: the particular AI capability comprises a pre-trained model; training the ML model comprises fine-tuning the pre-trained model based on the training data [pars. 29-31; note the pre-trained base models that can be customized using the input training data to generate the customized prediction model].
Referring to claim 3, Bramble discloses The method of Claim 1, wherein: the particular AI capability comprises a framework for training the ML model; training the ML model comprising leveraging the framework to train the ML model [fig. 1; pars. 29-31; note AutoML].
Referring to claim 4, Bramble discloses The method of Claim 1, further comprising: while training the ML model, generating a plurality of statistics associated with training the ML model; storing the plurality of statistics in the tenancy associated with the user [pars. 30, 79, and 82-85; the input training data includes application-specific performance metrics used to evaluate the customized prediction model; note the multi-tenant model for assigning cloud storage associated with the end user].
Referring to claim 7, Bramble discloses The method of Claim 1, further comprising: causing to be presented, on a screen of the computing device of the user, a list of multiple Al capabilities; wherein receiving the request comprises receiving input that selects the particular Al capability from among the Al capabilities in the list [fig. 4; pars. 29, 31, 17, and 51; a user interface is provided to the end user for selecting the prediction model; the system API also includes a list models method].
Referring to claim 9, Bramble discloses The method of Claim 1, wherein the user is a first user and the tenancy is a first tenancy, further comprising: receiving, from a second computing device of a second user that is different than the first user, a second request for the particular Al capability of the plurality of Al capabilities; in response to receiving second training data based on second input from the second user, storing the second training data in a second tenancy that is different than the first tenancy and that is associated with the second user in the cloud environment; in response to receiving the second request: retrieving the particular Al capability; training a second ML model based on the particular Al capability and the second training data; generating a second endpoint, in the cloud environment, that is associated with the second ML model; providing the second endpoint to the second tenancy associated with the second user [see the rejection for claim 1; also note the multi-tenant model, which means that a customized prediction model can be generated for a second end user].
Referring to claim 11, see at least the rejection for claim 1. Bramble further discloses One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause the claimed steps to be performed [fig. 5, program modules 11, processor(s) 12].
Referring to claim 12, see the rejection for claim 2.
Referring to claim 13, see the rejection for claim 3.
Referring to claim 14, see the rejection for claim 4.
Referring to claim 17, see the rejection for claim 7.
Referring to claim 19, see the rejection for claim 9.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Lin et al. (US Pub. 20210004696) discloses delivery of models from a model warehouse with an authentication service involving encryption.
Singh et al. (US Pub. 20240095077) discloses storing pre-trained models in a model registry, user selection of a pre-trained model, and training of the selected pre-trained model using training data provided by the user.
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/Grace Park/Primary Examiner, Art Unit 2144