2024/11/20 by Ryan Lee, Alexander Spangher, Lee, Ryan +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Business, Management and Accounting · Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Genetics, Bioinformatics, and Biomedical Research #Information Retrieval (cs.IR) #Intellectual Property and Patents #Law, AI, and Intellectual Property
paper · pdf · doi:10.48550/arxiv.2411.13477
openalex publication_date 2024/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A patent must be deemed novel and non-obvious in order to be granted by the US Patent Office (USPTO). If it is not, a US patent examiner will cite the prior work, or prior art, that invalidates the novelty and issue a non-final rejection. Predicting what claims of the invention should change given the prior art is an essential and crucial step in securing invention rights, yet has not been studied before as a learnable task. In this work we introduce the PatentEdits dataset, which contains 105K examples of successful revisions that overcome objections to novelty. We design algorithms to label edits sentence by sentence, then establish how well these edits can be predicted with large language models (LLMs). We demonstrate that evaluating textual entailment between cited references and draft sentences is especially effective in predicting which inventive claims remained unchanged or are novel in relation to prior art.