US 2024/0256791 A1 · MACHINE LEARNING EXECUTION FRAMEWORK

Abstract

In examples, an execution chain used to process includes one or more blocks, where each block includes at least one of a machine learning (ML) definition and/or a set of programmatic operations. As an example, an ML definition of an ML block includes a prompt to be processed by an ML model. As another example, the ML definition includes a prompt template, which may be populated based on a previous block of the execution chain. Further, a programmatic block of the execution chain can include any of a variety of operations, for example to obtain data from a data source and/or to prompt a user for input, thereby obtaining additional data that may be used to ground an ML model for a subsequent machine learning block of the execution chain.

Claims20

Dependent claims

A system comprising:

at least one processor;
and
memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, the set of operations comprising:
obtaining input to process according to a machine learning execution chain, wherein the machine learning execution chain includes a machine learning block and a programmatic block;
generating, based on the input and a prompt of the machine learning block, model output;
processing, based on the programmatic block, the generated model output to generate programmatic output for the programmatic block of the machine learning execution chain;
and
providing an indication of output for the machine learning execution chain in response to the obtained input.

5 more elements

Dependencies are inferred from the claim text.

Machine learning execution framework

Life of this patent

Machine learning execution framework

Life of this patent

Abstract

Claims20