Interpreting patents using retrieval-augmented generation and language models

A system for determining patent coverage, wherein the system comprises one or more modules, comprising:

• at least one first computational model trained with a collection of patent data, wherein said at least one first computational model is configured to interpret a patent document and extract one or more claim elements;
• wherein said one or more modules are configured to generate at least one scope representation based on the interpretation, wherein said at least one scope representation comprises said one or more claim elements corresponding to the patent document;
• at least one second computational model trained with data on products and services in relation to the collection of patent data, wherein said at least one second computational model is configured to map said at least one scope representation to a product or service, said mapping comprising at least one data structure associated with a claim format;
• and
• wherein said one or more modules are further configured to output a data representation of the patent coverage for the patent document based on the mapping.

A method for training a computational model to interpret patent coverage of a patent document, comprising:

• receiving training data comprising one or more of patent documents, prior art references, publications, opinions, examination reports, and product or service catalogs;
• processing the training data based on one or more criteria associated by using one or more techniques of tokenization, entity recognition, and vector embedding generation, wherein said one or more criteria comprise patent classification and technical terminology;
• training the computational model using the processed training data, wherein the computational model is configured to distinguish claim elements, legal concepts, and technical descriptions, and generate a structured mapping between them, wherein the structured mapping is represented as a semantic relationship model or vector space embedding;
• wherein said training the computational model comprises one or more of multi-layer attention mechanisms, transformer-based embeddings, and supervised learning with annotated patent datasets;
• wherein said computational model is configured to learn based on teaching and disclosed documents cited in the examination report with respect to claim interpretation, wherein the claim interpretation learned by the computational model can be used to assess patent coverage;
• and
• generating a trained computational model configured to interpret the patent coverage of the patent document.