Machine-Learning Natural Language Programming With Constrained Semantics

The patent introduces an intermediary system consisting of a machine learning model and a constrained language compiler that mediates between natural language descriptions and executable software programs. The machine learning model translates natural language into constrained language statements, which are then compiled into executable programs, eliminating the need for human programmers to directly translate while maintaining reliability through the structured constrained language intermediate representation

The patent replaces the mechanical process of human programming with an automated machine learning-based translation system. Instead of human programmers manually interpreting and coding natural language descriptions, the system uses trained machine learning models to automatically generate constrained language statements and compile them into executable software, significantly reducing development time while maintaining implementation accuracy

The patent changes the parameter of language constraint level, introducing a constrained language that sits between natural language and programming languages. This constrained language maintains enough structure to be reliably compiled into executable programs while remaining close enough to natural language to be generated by machine learning models, enabling domain-specific systems to be created without expensive human programming while preserving adaptability to complex requirements

The constrained language acts as an intermediary representation that enables automated translation while preserving domain-specific capabilities. The machine learning model learns to map natural language descriptions to this constrained language, which then serves as a reliable intermediate form that can be systematically compiled into executable programs, reducing development costs while maintaining the ability to handle complex domain requirements