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 7, 9, and 15-17 objected to as being dependent upon a rejected base claim, but would be allowable over the prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 and 20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because they recite "a storage device" which is broad enough to include signals per se. It is the recommendation of the Examiner to amend these limitations to recite a non-transitory nature of the devices.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1 analysis:
Independent Claims 1 and 20 recite, in part, a system for machine learning development comprising a storage device, which is broad enough to include signals per se. It is the recommendation of the Examiner to amend these limitations to recite a non-transitory nature of the devices. Independent Claim 11 recites, in part, a method for operating a machine learning development platform, therefore falling into the statutory category of process.
Regarding Claim 1:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“determining a natural language description of a machine learning model from the user instructions”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses translating user input into a natural language description.
“identifying a training data set from the user instructions”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses translating user input into a natural language description.
“identifying a sequence of pipeline components based on the natural language description of the machine learning model”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses translating user input into a natural language description.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“a storage device for storing instructions that, when executed, cause the system to perform operations”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (storage) (See MPEP 2106.05(f)).
“acquiring user instructions”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
“the sequence of pipeline components includes a model training component for a first machine learning model and a deployment component for the first machine learning model”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning model components) (See MPEP 2106.05(f)).
“training the first machine learning model using the model training component and the training data set”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (train a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
“deploying the first machine learning model using the deployment component”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (deploy a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “a storage device for storing instructions that, when executed, cause the system to perform operations” and “the sequence of pipeline components includes a model training component for a first machine learning model and a deployment component for the first machine learning model” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (storage and machine learning model components) (See MPEP 2106.05(f)).
The additional element(s) of “acquiring user instructions” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As discussed above, the additional element(s) of “training the first machine learning model using the model training component and the training data set” and “deploying the first machine learning model using the deployment component” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 2:
Step 2A: Prong 1 analysis:
Claim 2 recites in part:
“determining model performance metrics for the machine learning model based on the user instructions”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses determining metrics based on a translated NLP input.
“ranking a set of pipelines for generating the machine learning model based on the natural language description of the machine learning model”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses ranking machine learning components based on a description of the model.
“identifying the sequence of pipeline components based on the ranking of the set of pipelines”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses determining an order of components based on predetermined rankings.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“the sequence of pipeline components includes the model training component for the first machine learning model, the deployment component for the first machine learning model, a preprocessing component, a database management component, a storage management component, and an evaluation of benchmarks component”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning components) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “a storage device for storing instructions that, when executed, cause the system to perform operations” and “the sequence of pipeline components includes a model training component for a first machine learning model and a deployment component for the first machine learning model” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (storage and machine learning model components) (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 3:
Step 2A: Prong 1 analysis:
Claim 3 recites in part:
“the ranking the set of pipelines includes ranking the set of pipelines based on the model performance metrics for the machine learning model”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses ranking machine learning components based model performance metrics.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 4:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“the model performance metrics for the machine learning model include a memory footprint metric for the machine learning model”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (machine learning metrics) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “the model performance metrics for the machine learning model include a memory footprint metric for the machine learning model” is/are directed to particular field(s) of use (machine learning metrics) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 5:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“the model performance metrics for the machine learning model include a run-time metric for the machine learning model”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (machine learning metrics) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “the model performance metrics for the machine learning model include a run-time metric for the machine learning model” is/are directed to particular field(s) of use (machine learning metrics) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 6:
Step 2A: Prong 1 analysis:
Claim 6 recites in part:
“determining input and output requirements for the machine learning model based on the user instructions”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses determining the inputs and outputs of a model.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 7:
Step 2A: Prong 1 analysis:
Claim 7 recites in part:
“the ranking the set of pipelines includes ranking the set of pipelines based on the input and output requirements for the machine learning model”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses ranking machine learning components based on model inputs and outputs.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 8:
Step 2A: Prong 1 analysis:
Claim 8 recites in part:
“ranking the set of pipelines includes ranking the set of pipelines based on the natural language description of the machine learning and code associated with the first machine learning model”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses ranking machine learning components based on model code.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“storing the first machine learning model”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (storage) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “storing the first machine learning model” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (storage and machine learning model components) (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 9:
Step 2A: Prong 1 analysis:
Claim 9 recites in part:
“the ranking the set of pipelines includes generating a similarity score between the natural language description for the machine learning model and a natural language description for the first machine learning model associated with the sequence of pipeline components”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses ranking machine learning components based on a computed similarity score.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 10:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“the user instructions are derived from an audio description of a machine learning pipeline for performing image classification.”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (natural language processing and image classification) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “the user instructions are derived from an audio description of a machine learning pipeline for performing image classification.” is/are directed to particular field(s) of use (natural language processing and image classification) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 11:
Due to claim language similar to that of Claim 1, Claim 11 is rejected for the same reasons as presented above in the rejection of Claim 1.
Regarding Claim 12:
Due to claim language similar to that of Claim 2, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 13:
Due to claim language similar to that of Claim 3, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 14:
Due to claim language similar to that of Claim 4, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 15:
Step 2A: Prong 1 analysis:
Claim 15 recites in part:
“determining an input and output interface schema for the machine learning model based on the user instructions”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses determining an I/O schema for a model.
“identifying the sequence of pipeline components based on the input and output interface schema for the machine learning model”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses determining a sequence of components for a model based on a predetermined schema.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 16:
Step 2A: Prong 1 analysis:
Claim 16 recites in part:
“the identifying the sequence of pipeline components includes identifying the sequence of pipeline components based on a first similarity score between the natural language description for the machine learning model and a natural language description for the first machine learning model associated with the sequence of pipeline components or based on a second similarity score between the natural language description for the machine learning model and a natural language description for the first machine learning model associated with the sequence of pipeline components”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses identifying machine learning components based on a computed similarity score.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 17:
Step 2A: Prong 1 analysis
Claim 17 recites in part:
“the identifying the sequence of pipeline components includes identifying the sequence of pipeline components based on a similarity score between an input and output interface schema for the machine learning model and input and output requirements for the first machine learning model”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses identifying machine learning components based on a computed similarity score.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 18:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“the user instructions are derived from a text-based description of the machine learning model”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (natural language processing) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “the user instructions are derived from a text-based description of the machine learning model” is/are directed to particular field(s) of use (natural language processing) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 19:
Due to claim language similar to that of Claim 10, Claim 19 is rejected for the same reasons as presented above in the rejection of Claim 10.
Regarding Claim 20:
Due to claim language similar to that of Claims 1 and 11, Claim 20 is rejected for the same reasons as presented above in the rejection(s) of Claims 1 and 11, with the exception of the limitation(s) covered below.
Step 2A: Prong 1 analysis:
Claim 20 recites in part:
“determine an interface schema for the machine learning model using the user prompt”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses determining a model schema based on user instructions.
“rank a set of pipelines for generating the machine learning model based on the description of the machine learning model and the interface schema for the machine learning model”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses ranking a model schema based on user instructions.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“a storage device configured to store a first machine learning model”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (storage) (See MPEP 2106.05(f)).
“a processing system in communication with the storage device”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (processor) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “a storage device configured to store a first machine learning model” and “a processing system in communication with the storage device” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (storage and processor) (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1, 11, and 18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Skerry-Ryan et al (US 20240104394 A1, hereinafter Ryan).
Regarding Claim 1:
Ryan teaches
A system for providing a machine learning development platform, comprising:
a storage device for storing instructions that, when executed, cause the system to perform operations comprising: (Ryan [0010]: "According to another example embodiment, one or more non-transitory computer-readable media can collectively store instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations")
acquiring user instructions (Ryan [0041]: “FIG. 1 depicts an example origination ML pipeline 14. The example origination ML pipeline 14 illustrated in FIG. 1 can be configured to receive training data 12 and, optionally, a problem statement 13 from a user”);
determining a natural language description of a machine learning model from the user instructions (Ryan [0041]: “FIG. 1 depicts an example origination ML pipeline 14. The example origination ML pipeline 14 illustrated in FIG. 1 can be configured to receive training data 12 and, optionally, a problem statement 13 from a user”; [0068]: "In another example implementation, Tuner component 212 can be configured to infer, based on problem statement 13, one or more parameters of the optimization domain to identify such a candidate model having a certain ML model architecture (e.g., certain parameters, hyperparameter, model hyperparameters 30, etc.)"; (EN): it can be seen in Fig. 1 that the system takes in training data and a problem statement from the user, the problem statement is analogous to a natural language description of the model, Ryan Claim 3, “wherein the problem statement is expressed in a natural language”);
identifying a training data set from the user instructions (Ryan [0022]: "Thus, a user can provide only minimal input information such as the training dataset and, in return, the computing system can supply the user (or another system specified by the user) with a trained model");
identifying a sequence of pipeline components based on the natural language description of the machine learning model, the sequence of pipeline components includes a model training component for a first machine learning model and a deployment component for the first machine learning model (Ryan [0041]: "FIG. 1 depicts an example origination ML pipeline 14. The example origination ML pipeline 14 illustrated in FIG. 1 can be configured to receive training data 12 and, optionally, a problem statement 13 from a user. Execution of origination ML pipeline 14 can result in generation and exportation of a trained model 26 and a deployment ML pipeline 28 that is configured to enable deployment of the trained model");
training the first machine learning model using the model training component and the training data set (Ryan [0042]: "In this example implementation, origination ML pipeline 14 can further export trained model 26 and/or deployment ML pipeline 28 (e.g., including model hyperparameters 30) to such an ML platform user via the programmatic API, where origination ML pipeline 14 can export trained model 26 and/or deployment ML pipeline 28 (e.g., including model hyperparameters 30) for deployment of trained model 26 with (e.g., using) deployment ML pipeline");
and deploying the first machine learning model using the deployment component (Ryan [0042]: "In this example implementation, origination ML pipeline 14 can further export trained model 26 and/or deployment ML pipeline 28 (e.g., including model hyperparameters 30) to such an ML platform user via the programmatic API, where origination ML pipeline 14 can export trained model 26 and/or deployment ML pipeline 28 (e.g., including model hyperparameters 30) for deployment of trained model 26 with (e.g., using) deployment ML pipeline").
Regarding Claim 11:
Due to claim language similar to that of Claim 1, Claim 11 is rejected for the same reasons as presented above in the rejection of Claim 1.
Regarding Claim 18:
Ryan teaches
The method of claim 11, wherein: the user instructions are derived from a text-based description of the machine learning model (Ryan [0041]: “FIG. 1 depicts an example origination ML pipeline 14. The example origination ML pipeline 14 illustrated in FIG. 1 can be configured to receive training data 12 and, optionally, a problem statement 13 from a user”; [0118]: "As another example, the machine-learned model(s) can process the text or natural language data to generate a classification output.").
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.
Claim(s) 2, 3, 6, 8, 12, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryan as applied to claims 1 and 11 above, and further in view of Appel et al (US 20230419162 A1, hereinafter Appel).
Regarding Claim 2:
Ryan teaches
The system of claim 1, further comprising instructions that, when executed, cause the system to perform operations comprising: determining model performance metrics for the machine learning model based on the user instructions (Ryan [0027]: "As examples, the metadata associated with each dataset can include: (a) properties of the dataset; (b) problem statements; (c) feature engineering transformations; (d) hyperparameter search space; (e) training logs and signals; and/or (f) model quality metrics associated with each combination of hyperparameters");
identifying the sequence of pipeline components based on the ranking of the set of pipelines, the sequence of pipeline components includes the model training component for the first machine learning model, the deployment component for the first machine learning model, a preprocessing component, a database management component, a storage management component, and an evaluation of benchmarks component (Ryan [0084]: "As illustrated in the example implementation depicted in FIG. 3, deployment ML pipeline 28 can include ExampleGen component 202, StatisticsGen component 204, SchemaGen component 206, Example Validator component 208, Transform component 210, Tuner component 212, Trainer component 214, Evaluator component 216, Infra Validator component 218, and/or Pusher component 220, which can perform their respective operations in the same manner as described above with reference to FIG. 2. The example implementation depicted in FIG. 3 illustrates how data can flow between such components of deployment ML pipeline").
Ryan does not distinctly disclose
ranking a set of pipelines for generating the machine learning model based on the natural language description of the machine learning model;
However, Appel teaches
ranking a set of pipelines for generating the machine learning model based on the natural language description of the machine learning model (Appel [0003]: "In addition, AutoAI may test a variety of tuning options to reach the best result as it generates, then ranks, model-candidate pipelines.")
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for automatic production of machine learning models and pipelines of Ryan with the systems for composing machine learning models for complex data sources of Appel in order to provide a method for ranking machine learning pipeline components (Appel [0031]: "In an embodiment, model composer program 106 combines the selected two or more models to compose a machine learning model consisting of an optimal combination of models for the data sub-types. An optimal combination may be defined as the combination of models that result in the best accuracy, based on one or more chosen metrics for the machine learning task, such as precision, recall, F1 score, mean square for error (MSE), mean absolute error (MAE), root mean square error (RMSE), etc. In an embodiment, model composer program 106 generates a plurality of model combinations, where each combination may be optimal for a different metric."; [0034]: "In an embodiment, model composer program 106 ranks the results for the plurality of model combinations based on a calculated error and displays the ranking.")
Regarding Claim 3:
Ryan does not distinctly disclose
The system of claim 2, wherein: the ranking the set of pipelines includes ranking the set of pipelines based on the model performance metrics for the machine learning model.
However, Appel teaches
The system of claim 2, wherein: the ranking the set of pipelines includes ranking the set of pipelines based on the model performance metrics for the machine learning model (Appel [0031]: "In an embodiment, model composer program 106 combines the selected two or more models to compose a machine learning model consisting of an optimal combination of models for the data sub-types. An optimal combination may be defined as the combination of models that result in the best accuracy, based on one or more chosen metrics for the machine learning task, such as precision, recall, F1 score, mean square for error (MSE), mean absolute error (MAE), root mean square error (RMSE), etc. In an embodiment, model composer program 106 generates a plurality of model combinations, where each combination may be optimal for a different metric."; [0034]: "In an embodiment, model composer program 106 ranks the results for the plurality of model combinations based on a calculated error and displays the ranking.").
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for automatic production of machine learning models and pipelines of Ryan with the systems for composing machine learning models for complex data sources of Appel in order to provide a method for ranking machine learning pipeline components (Appel [0031]: "In an embodiment, model composer program 106 combines the selected two or more models to compose a machine learning model consisting of an optimal combination of models for the data sub-types. An optimal combination may be defined as the combination of models that result in the best accuracy, based on one or more chosen metrics for the machine learning task, such as precision, recall, F1 score, mean square for error (MSE), mean absolute error (MAE), root mean square error (RMSE), etc. In an embodiment, model composer program 106 generates a plurality of model combinations, where each combination may be optimal for a different metric."; [0034]: "In an embodiment, model composer program 106 ranks the results for the plurality of model combinations based on a calculated error and displays the ranking.")
Regarding Claim 6:
Ryan teaches
The system of claim 2, further comprising instructions that, when executed, cause the system to perform operations comprising: determining input and output requirements for the machine learning model based on the user instructions (Ryan [0066]: "In some implementations, to perform the above-described ML model architecture search to identify a candidate model, Tuner component 212 can be configured to employ an algorithm that can search the optimization domain to identify the relatively best ML model architecture (e.g., parameters, hyperparameters, model hyperparameters 30, etc.) based on a certain objective (e.g., an objective that can be defined by a user in problem statement 13).").
Regarding Claim 8: Ryan teaches
The system of claim 2, further comprising instructions that, when executed, cause the system to perform operations comprising: storing the first machine learning model, the ranking the set of pipelines includes ranking the set of pipelines based on the natural language description of the machine learning and code associated with the first machine learning model (Ryan [0045]: "The example origination ML pipeline 14 and deployment ML pipeline 28 depicted in FIG. 2 can each include computer-readable code that automates the workflow it takes to produce and/or run trained model 26 (e.g., to define, launch, and/or monitor trained model 26)"; (EN): it is reasonable to one skilled in the art that storing the ML model and the code of said model also includes storing the ranking(s) of pipelines according to Appel).
Regarding Claim 12:
Due to claim language similar to that of Claim 2, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 13:
Due to claim language similar to that of Claim 3, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 20:
Ryan teaches
A system, comprising: a storage device configured to store a first machine learning model (Ryan [0010]: "According to another example embodiment, one or more non-transitory computer-readable media can collectively store instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations")
a processing system in communication with the storage device (Ryan [0010]: "According to another example embodiment, one or more non-transitory computer-readable media can collectively store instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations")
identify a first pipeline of the set of pipelines based on the ranking of the set of pipelines, the first pipeline includes a model training component for the first machine learning model and a deployment component for the first machine learning model (Ryan [0041]: "FIG. 1 depicts an example origination ML pipeline 14. The example origination ML pipeline 14 illustrated in FIG. 1 can be configured to receive training data 12 and, optionally, a problem statement 13 from a user. Execution of origination ML pipeline 14 can result in generation and exportation of a trained model 26 and a deployment ML pipeline 28 that is configured to enable deployment of the trained model");
determine an interface schema for the machine learning model using the user prompt (Ryan [0050]: “For example, the StatisticsGen component 204 or Transform component 210 discussed below can provide a user interface by which a user can explore different feature crosses and their respective correlations to different labels, enabling the user to unlock additional levels of data insight, understanding, and interpretability. In addition, users can be enabled to use a relational database (e.g., paired with a structured query language) to create custom features on the fly.”);
Ryan does not distinctly disclose
rank a set of pipelines for generating the machine learning model based on the description of the machine learning model and the interface schema for the machine learning model (Appel [0003]: "In addition, AutoAI may test a variety of tuning options to reach the best result as it generates, then ranks, model-candidate pipelines."; [0031]: "In an embodiment, model composer program 106 combines the selected two or more models to compose a machine learning model consisting of an optimal combination of models for the data sub-types. An optimal combination may be defined as the combination of models that result in the best accuracy, based on one or more chosen metrics for the machine learning task, such as precision, recall, F1 score, mean square for error (MSE), mean absolute error (MAE), root mean square error (RMSE), etc. In an embodiment, model composer program 106 generates a plurality of model combinations, where each combination may be optimal for a different metric."; [0034]: "In an embodiment, model composer program 106 ranks the results for the plurality of model combinations based on a calculated error and displays the ranking.");
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for automatic production of machine learning models and pipelines of Ryan with the systems for composing machine learning models for complex data sources of Appel in order to provide a method for ranking machine learning pipeline components (Appel [0031]: "In an embodiment, model composer program 106 combines the selected two or more models to compose a machine learning model consisting of an optimal combination of models for the data sub-types. An optimal combination may be defined as the combination of models that result in the best accuracy, based on one or more chosen metrics for the machine learning task, such as precision, recall, F1 score, mean square for error (MSE), mean absolute error (MAE), root mean square error (RMSE), etc. In an embodiment, model composer program 106 generates a plurality of model combinations, where each combination may be optimal for a different metric."; [0034]: "In an embodiment, model composer program 106 ranks the results for the plurality of model combinations based on a calculated error and displays the ranking.")
Claim Rejections - 35 USC § 103
Claim(s) 4, 5, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryan and Appel as applied to claims 1 and 11 above, and further in view of Cohen et al (US 12657452 B2, hereinafter Cohen).
Regarding Claim 4:
Ryan + Appel does not distinctly disclose
The system of claim 3, wherein: the model performance metrics for the machine learning model include a memory footprint metric for the machine learning model.
However, Cohen teaches
The system of claim 3, wherein: the model performance metrics for the machine learning model include a memory footprint metric for the machine learning model (Cohen [Col 10 lines 37-45]: "Collected information may include (but not be limited to) CPU speed, the number of CPU cores (physical or virtual), the number and type of GPUs (physical or virtual), an amount of available system memory and/or GPU memory, a number of remote processing nodes available for the pipeline deployment, type and version of the operating system installed on computing device 102 (or type/version of guest operating system instantiated on the virtualized environment), and the like").
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for automatic production of machine learning models and pipelines of Ryan with the systems for training and deploying models of Cohen in order to provide a machine learning system that considers different hardware metrics of the environment in which the system is built (Cohen [Col 10 lines 19-30]: “Deployment engine 170 of the configurable pipeline (e.g., CP 100 of FIG. 1) may implement the pipeline on user-accessible hardware resources (the target platform). The user may have access to (e.g., local) computing device 102 having a number of CPUs, GPUs, and memory devices. Alternatively or additionally, the user may have access to one or more cloud computing servers providing virtualization services. Deployment engine 170 may allow the user to input the description or identification of the user-accessible target platform resources 262 which may include identification of available computational, memory, network, etc., resources.”).
Regarding Claim 5:
Ryan + Appel does not distinctly disclose
The system of claim 3, wherein: the model performance metrics for the machine learning model include a run-time metric for the machine learning model.
However, Cohen teaches
The system of claim 3, wherein: the model performance metrics for the machine learning model include a run-time metric for the machine learning model. (Cohen [Col 10 lines 37-45]: "Collected information may include (but not be limited to) CPU speed, the number of CPU cores (physical or virtual), the number and type of GPUs (physical or virtual), an amount of available system memory and/or GPU memory, a number of remote processing nodes available for the pipeline deployment, type and version of the operating system installed on computing device 102 (or type/version of guest operating system instantiated on the virtualized environment), and the like."; (EN): it is reasonable to one skilled in the art to infer that "and the like" includes a measure of runtime)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for automatic production of machine learning models and pipelines of Ryan with the systems for training and deploying models of Cohen in order to provide a machine learning system that considers different hardware metrics of the environment in which the system is built (Cohen [Col 10 lines 19-30]: “Deployment engine 170 of the configurable pipeline (e.g., CP 100 of FIG. 1) may implement the pipeline on user-accessible hardware resources (the target platform). The user may have access to (e.g., local) computing device 102 having a number of CPUs, GPUs, and memory devices. Alternatively or additionally, the user may have access to one or more cloud computing servers providing virtualization services. Deployment engine 170 may allow the user to input the description or identification of the user-accessible target platform resources 262 which may include identification of available computational, memory, network, etc., resources.”).
Regarding Claim 14:
Ryan + Appel does not distinctly disclose
The method of claim 13, wherein: the model performance metrics for the machine learning model include a memory size for the machine learning model.
However, Cohen teaches
The method of claim 13, wherein: the model performance metrics for the machine learning model include a memory size for the machine learning model (Cohen [Col 10 lines 37-45]: "Collected information may include (but not be limited to) CPU speed, the number of CPU cores (physical or virtual), the number and type of GPUs (physical or virtual), an amount of available system memory and/or GPU memory, a number of remote processing nodes available for the pipeline deployment, type and version of the operating system installed on computing device 102 (or type/version of guest operating system instantiated on the virtualized environment), and the like").
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for automatic production of machine learning models and pipelines of Ryan with the systems for training and deploying models of Cohen in order to provide a machine learning system that considers different hardware metrics of the environment in which the system is built (Cohen [Col 10 lines 19-30]: “Deployment engine 170 of the configurable pipeline (e.g., CP 100 of FIG. 1) may implement the pipeline on user-accessible hardware resources (the target platform). The user may have access to (e.g., local) computing device 102 having a number of CPUs, GPUs, and memory devices. Alternatively or additionally, the user may have access to one or more cloud computing servers providing virtualization services. Deployment engine 170 may allow the user to input the description or identification of the user-accessible target platform resources 262 which may include identification of available computational, memory, network, etc., resources”).
Claim Rejections - 35 USC § 103
Claim(s) 10 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryan as applied to claim 1 above, and further in view of Yuan et al (US 12675518 B1, hereinafter Yuan).
Regarding Claim 10:
Ryan alone does not distinctly disclose
The system of claim 1, wherein: the user instructions are derived from an audio description of a machine learning pipeline for performing image classification.
However, Ryan with the addition of Yuan teaches
The system of claim 1, wherein: the user instructions are derived from an audio description of a machine learning pipeline for performing image classification (Yuan [Col 5 lines 31-40]: “The input information may a natural language description (e.g., the user may describe an aspect of the input information in plain language). The description, in some embodiments, may be a formatted description, where the user system 105 processes the user input in plain language into a format accepted by, or more easily processed by, the content summarization system 120. In addition, the input information may also include content that is in a form of (but not limited to), audio information”; Ryan [0117]: “As an example, the machine-learned model(s) can process the image data to generate an image recognition output (e.g., a recognition of the image data, a latent embedding of the image data, an encoded representation of the image data, a hash of the image data, etc.).”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for automatic production of machine learning models and pipelines of Ryan with the systems for natural language summarization of Yuan in order to provide a method of natural language processing that utilizes audio inputs for machine learning models (Yuan [Col 5 lines 31-40]: “The input information may a natural language description (e.g., the user may describe an aspect of the input information in plain language). The description, in some embodiments, may be a formatted description, where the user system 105 processes the user input in plain language into a format accepted by, or more easily processed by, the content summarization system 120. In addition, the input information may also include content that is in a form of (but not limited to), audio information”).
Regarding Claim 19:
Due to claim language similar to that of Claim 10, Claim 19 is rejected for the same reasons as presented above in the rejection of Claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY M SACKALOSKY whose telephone number is (703)756-1590. The examiner can normally be reached M-F 7:30am-3:30pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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