Computer-generated content based on text classification, semantic relevance, and activation of deep learning large language models

A system for automatically generating natural language text for a response document that satisfies requirements in a requirement specification, comprising:

• one or more processors programmed to:
• access a plurality of discrete requirements obtained from a requirement specification, each discrete requirement specifying a respective requirement to be satisfied in the response document that is generated responsive to the requirement specification;
• for each discrete requirement:
• identify a relevant response section from among previously generated response sections, the previously generated response sections having been identified and labeled from a corpus of previously generated response documents;
• generate a language model writing matrix based on the identified relevant response section and the discrete requirement, the language model writing matrix comprising natural language text from the identified relevant response section and the discrete requirement that is used as a basis to automatically generate unique natural language text;
• activate, using the language model writing matrix, an artificial intelligence (AI) deep learning language model pretrained with natural language content to automatically generate unique text;
• obtain, based on the activated AI deep learning language model, a candidate response portion that is predicted to address the discrete requirement,
• the candidate response portion comprising natural language text;
• and
• generate the response document based on each candidate response portion obtained for each discrete requirement, wherein the response document is automatically generated to satisfy the plurality of discrete requirements.