Patent Response System Optimised for Faithfulness: Procedural Knowledge Embodiment with Knowledge Graph and Retrieval Augmented Generation (2024)

Abstract: A successful response to Office Action is crucial for an invention to obtain a patent. While previous attempts have applied generalised LLMs, such as GPT-4, in the response process, there remains significant room for improvement in generating faithful, unbiased, and practically valuable responses. To address this issue, we propose the Patent Response System Optimised for Faithfulness (PRO). PRO explicitly incorporates procedural knowledge used by patent agents during drafting arguments in response. This framework comprises several key components: (1) Our proposed PRLLM is a LLM tailored for patent responses, designed to have comprehensive patent domain-specific knowledge. (2) Our proposed PPNet encodes legal interpretations and relationships between technical components from judicial sources through a knowledge graph. (3) The augmented generation processes retrieve relevant information from both the patent text and PPNet to augment the PRLLM’s input and generate faithful responses. Results show that PRO significantly reduces unfaithfulness across six error types compared to several settings. For instance, PRO outperforms GPT-4 by an average of 39% in terms of faithfulness. This demonstrates the effectiveness of our domain-specific approach in improving the quality of automated patent responses.

## introduction large language models (llms), such as @xcite and llama2 @xcite , are deemed generalised and not domainspecific, posing challenges in the patent field. in the intellectual property field, patents filed with the united states patent and trademark office (uspto) are continuously evolving and growing, with new technologies and legal terms requiring complex analysis @xcite . recently, research has focused on developing or applying language models (lms) and llms tailored for patent language to address tasks such as patent drafting @xcite , prior art search @xcite , and semantic analysis @xcite . although these efforts have been made, llms have not significantly improved the office action (oa; e.g., rejection) and response (e.g., argument or amendment) process. this process involves detailed communication and extensive exchanges of technical and legal knowledge between examiners and patent agents to ensure the inventions' novelty and non-obviousness. @xcite have started investigating the use of lms/llms and recommender systems to automate patent responses. however, due to the concern of privacy, the distinctive nature of patent language, the uniqueness of each invention, and the intricacy of formulating responses, considerable improvements are still needed in patent response systems. this leads us to our first research question: can we develop a domain-specific patent response llm (prllm)? to investigate this, we constructed a dataset comprising patents and their corresponding oa-response histories over 10 years. this dataset also includes a wide range of types, domains, and tasks, ensuring comprehensive coverage. incorporating previous data during training helps retain knowledge from earlier training phases, thus preventing the forgetting issue @xcite . for the model, we selected llama2 as the base model for continual pretraining among open-source llms. for supervised fine-tuning (sft), we used paired oa-responses. the zeroshot results showed that while the model performs well in terms of formatting responses, identifying key legal and technical terms, it struggles with analysing examiners' rejections (e.g., novelty or non-obviousness analysis), even when additional information is provided (see section 2.1 and section 5). this raises another question: how can we enhance the faithfulness of prllm in developing arguments? empirically, patent agents utilise a series of procedural knowledge during response analysis. upon receiving an oa, they first identify the points of contention (e.g., rejections). from there, they follow dual paths. the first path involves finding the core inventive concept related to the point of contention, which could be reflected in the patent and/or independent claims. this includes identifying the patent's key features and its problem-solution. agents then search for relevant past precedents to support their arguments @xcite . the second path addresses rejections that the invention is similar to prior art. agents analyse the relevant paragraphs in the patent that relate to the prior art, using them as the basis for their arguments. combining these foundations, agents develop arguments and/or amendments to address the deficiency in the examiner's broadest reasonable interpretation (bri) and/or in the patent's claim. this type of procedural knowledge is not present in the previous llms, as it is implicit knowledge that agents use during the response process. hence, we propose a novel framework: patent response system optimized for faithfulness (pro). this framework aims to explicitly incorporate procedural knowledge into the model. specifically, the framework includes a patent precedents kg (ppnet) that represents the external precedents patent agents might refer to during developing ar-guments. this kg characterises the relationships between invention technologies, not only in common or dictionary definitions but with legal interpretations that include judicial logic and specificity. additionally, the framework involves multiple retrieval-augmented generation (rag) processes, where retrievers use points of contention or prior art to retrieve relevant information in the patent, and the generator uses ppnet/prllm-retrieved results to produce key features, problem-solution statements, and the resulting response. experimental results demonstrate that this framework significantly reduces unfaithfulness compared to baselines. we make several key contributions: ## architecture overview considering the domain-specific nature of patent responses, we first developed the patent response llm. this model is designed to run locally for security reasons and is well-versed in patent language, various technical terms, relevant legal terminology, and the structure, format, and analysis required for responses. this llm forms the core foundation of our entire technical architecture and can function as both a generator and a retriever within our framework (see section 3 for its training details). ## prllm training details we followed the approach outlined by @xcite in training our prllm models. using llama2 as the base model, we trained models with parameters of 13 billion (13b) and 70 billion (70b), naming the series prllm-13b and prllm-70b respectively. the training process was divided into two main stages: continual pretraining and supervised fine-tuning (sft). ## conclusion we introduced a domain-specific patent response llm (prllm) that can run locally to ensure privacy during patent responses, as well as a kg of