DETAILED ACTION
Claims 1-20 are presented for examination.
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 .
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 .
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 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.
Claim Rejections - 35 USC § 103
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.
Claims1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US Patent Application No. US 20230326191 A1) (Hereinafter Li) in view of Koneru et al. (US Patent Application No. US 20240282298 A1) (Hereinafter Koneru).
As per claim 1, Li discloses a computer-implemented method for deploying machine learning (ML) models for inference in production, the method being executed by one or more processors and comprising:
providing, for a set of ML models, a set of training metrics determined using test data during a training phase of ML models in the set of ML models (para 13, the weights for the first ML classification model and the second ML classification model are each determined based on a performance score for the first ML classification model and a performance score for the second ML classification model that are both evaluated using the same set of test data);
providing, for a production-use ML model, a set of inference metrics based on predictions generated by the production-use ML model (para 5-13, a first prediction outputted by a first machine learning (ML) classification model which is provided with production data as the input, wherein the first ML classification model is a few-shot learning model having a first feature extractor followed by a metric-based classifier); and
deploying a user-selected ML model to an inference runtime for production use (para 5-12, few-shot learning model having a first feature extractor followed by a metric-based classifier; obtain a second prediction outputted by a second ML classification model which is provided with the production data).
Li does not explicitly disclose generating, by a prompt generator, a set of few-shot examples using the set of training metrics and the set of inference metrics;
inputting, by the prompt generator, the set of few-shot examples to a large language model (LLM) as prompts, the set of few-shot examples providing context to the LLM for queries associated with ML model selection; transmitting, to the LLM a query; displaying, to a user, a recommendation that is received from the LLM and responsive to the query; and receiving input from a user indicating a user-selected ML model responsive to the recommendation.
However, Koneru discloses generating, by a prompt generator, a set of few-shot examples using the set of training metrics and the set of inference metrics (para 53, the prompt generator may be an artificial intelligence-machine learning (AI-ML) model that generates one or more prompts in text form for the selected LLM to generate a required output.);
inputting, by the prompt generator, the set of few-shot examples to a large language model (LLM) as prompts, the set of few-shot examples providing context to the LLM for queries associated with ML model selection (para 51-53, selected LLM that collects the required information) the prompt generator 174 may be an artificial intelligence-machine learning (AI-ML) model that generates one or more prompts in text form for the selected LLM to generate a required output ….the virtual assistant server the conversation context, one or more business rules, one or more conversation rules, customer context, one or more exit scenarios, a few-shot sample conversations);
transmitting, to the LLM a query (para 69, the selected LLM or to request information from the selected LLM in one or more predefined formats);
displaying, to a user, a recommendation that is received from the LLM and responsive to the query (para 51, the LLM generating a response that may be sent to the user; para 69, displayed in the properties panel 212 to configure the information provided to the selected LLM or to request information from the selected LLM in one or more predefined formats); and
receiving input from a user indicating a user-selected ML model responsive to the recommendation (para 54, the prompt generator may be trained on a dataset of input-output pairs ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li and Koneru. The motivation would have been to allow flexible NLP based model recommendations using weighted sum and prompt generator LLM architecture.
The Examiner notes that this motivation applies to all dependent and/or otherwise subsequently addressed claims.
As per claim 2, claim is rejected for the same reasons and motivations as claim 1, above, In addition, Li discloses wherein deploying a user-selected ML model to an inference runtime for production use at least partially comprises transmitting the user-selected ML model from a ML model store to the inference runtime (para 12, memory for storing instructions; and one or more processing units, model store is a standard functional equivalent to ML model store).
As per claim 3, claim is rejected for the same reasons and motivations as claim 1, above, In addition, Li discloses wherein the set of training metrics comprises, for each ML model in the set of ML models, a sub-set of training metrics comprising a model identifier, a code, a proposal rate, an accuracy, and a threshold (para 5, 12, first ML classification model … second ML classification model).
As per claim 4, claim is rejected for the same reasons and motivations as claim 1, above, In addition, Li discloses wherein the set of inference metrics comprises sub-sets of inference metrics each comprising an auto-task accuracy, a proposal rate, and a confidence threshold (para 6, 13, performance score).
As per claim 5, claim is rejected for the same reasons and motivations as claim 1, above, In addition, Li discloses wherein the auto-task accuracy indicates an accuracy of automatic execution of a task in response to a prediction of the production-use ML model (para 3, the usage of classification models has greatly improved the efficiency of many operations such as quality inspection, process control, anomaly detection, and so on, facilitating the rapid progress of industrial automation, para 13, a performance score for the first ML classification model and a performance score for the second ML classification model that are both evaluated using the same set of test data).
As per claim 6, claim is rejected for the same reasons and motivations as claim 1, above, In addition, Koneru discloses wherein the query comprises a code and at least one target metric (para 51, 54, one or more business rules that dictate how the LLM should behave).
As per claim 7, claim is rejected for the same reasons and motivations as claim 1, above, In addition, Koneru discloses wherein generating, by the prompt generator, the set of few-shot examples comprises populating a prompt template (para 54, the developer at the developer device 130(1) may pre-configure one or more prompt templates; the prompt generator 174 may use one of the one or more prompt templates to provide one or more prompts to the selected LLM).
As per claims 8 and 15, claim is rejected for the same reasons and motivations as claim 1, above.
As per claims 9 and 16, claim is rejected for the same reasons and motivations as claim 2, above.
As per claims 10 and 17, claim is rejected for the same reasons and motivations as claim 3, above.
As per claims 11 and 18, claim is rejected for the same reasons and motivations as claim 4, above.
As per claims 12 and 19, claim is rejected for the same reasons and motivations as claim 5, above.
As per claims 13 and 20, claim is rejected for the same reasons and motivations as claim 6, above.
As per claim 14, claim is rejected for the same reasons and motivations as claim 7, above.
Conclusion
Please see the attached PTO-892 for the prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD A SIDDIQI whose telephone number is (571)272-3976. The examiner can normally be reached Monday-Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carl G Colin can be reached at 571-272-3862. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMAD A SIDDIQI/Primary Examiner, Art Unit 2493