A uto S pec: An Agentic Framework for Automatically Drafting Patent Specification (2025)

Abstract: Patents play a critical role in driving technological innovation by granting inventors exclusive rights to their inventions. However the process of drafting a patent application is often expensive and time-consuming, making it a prime candidate for automation. Despite recent advancements in language models, several challenges hinder the development of robust automated patent drafting systems. First, the information within a patent application is highly confidential, which often prevents the use of closed-source LLMs for automating this task. Second, the process of drafting a patent application is difficult for even the most advanced language models due to their long context, technical writing style, and specialized domain knowledge. To address these challenges, we introduce AutoSpec, a secure, agentic framework for Automatically drafting patent Specification. Our approach decomposes the drafting process into a sequence of manageable subtasks, each solvable by smaller, open-source language models enhanced with custom tools tailored for drafting patent specification. To assess our system, we design a novel evaluation protocol in collaboration with experienced patent attorneys. Our automatic and expert evaluations show that AutoSpec outperforms existing baselines on a patent drafting task.

## introduction drafting a patent application has long been a key component of intellectual property protection. yet, the drafting process remains a difficult and laborious task. inventors face many hurdles to patenting their inventions, including high monetary costs and significant time commitments @xcite . this discourages smaller entities and individual inventors from pursuing patents, stifling innovation and competition. llms offer a promising way to alleviate these issues by automating the patent drafting process. recent work has shown that llms can achieve impressive performance on many complex writing tasks, including ones within the legal domain @xcite . however, there remain several obstacles to the development and deployment of automatic patent drafting systems. a major challenge in automating patent drafting is ensuring the security and confidentiality of sensitive invention details. leakage of this information could compromise the patent's validity or result in an outright rejection of the application. this makes on-premises deployment of patent drafting systems highly desirable, restricting the use of more powerful, proprietary llms for solving this task. these challenges are compounded by the fact that patent drafting remains difficult for even the most advanced language models @xcite . patent specifications often span tens of thousand of words, which is beyond what current llms can output in a single generation. patent applications also integrate highly specialized domain knowledge, using a combination of legal and technical language that is difficult for llms to replicate @xcite . to address these issues we propose autospec, an agentic method for automatically generating patent application specification. given the core details of an invention, autospec produces a full specification by first creating a structured outline. our outline generation method is constructed to emulate the way that patent attorneys decompose the workflow for drafting patent specification. this outline breaks down the drafting process into manageable subtasks, each of which are solvable by smaller, open-source llms in combination with custom-built tools we create specifically for drafting patent specification. these custom tools are designed according to expert input and use a combination of fine-tuning, prompting, and retrieval to effectively draft patent application disclosure. to rigorously assess our system, we introduce a novel evaluation protocol for analyzing patent specifications, developed in collaboration with expert patent attorneys. this protocol is centered on an annotation scheme designed to highlight the critical aspects of high-quality patent disclosure and to standardize the evaluation of machine-generated patent applications. leveraging this protocol, we evaluate our approach on a patent drafting task using both automated metrics and human expert assessments. our results show that autospec outperforms existing baselines. we release our evaluation data which consists of 75 machine-generated patent disclosures annotated according to our evaluation protocol. our contributions are summarized as follows: ## background patent applications are legal documents that define an invention. they consist of a set of claims and a specification (also referred to as the disclosure). the claims serve to define the scope of an invention concisely and unambiguously. the specification is written based on the content in the claims, and typically includes an abstract, background, summary, and detailed description. depending on the field of the invention, the patent specification may also contain drawings and drawing descriptions which are included in the detailed description. figure 1 shows a complete example of a patent application. patent language is often very technical, using specialized terms, legal jargon, and sometimes new terms to describe novel concepts. patents frequently create their own definitions for terms, which can differ significantly from how those words are used in normal language or even within the relevant technical field. these self-defined terms are typically quite artificial and are unlikely to appear in other documents. this makes it difficult for llms, which are typically trained on general internet text, to emulate the kind of language in patent applications @xcite . patent specifications are very long with a length of about 13.5k tokens on average @xcite . the detailed description comprises the bulk of this length with an average of 11.9k tokens. the specification is written primarily based on claim information which is typically around 1.3k tokens. this means the disclosure must elaborate heavily on the content in the claims and incorporate relevant external concepts that are key to explaining the invention. this combination of long generation and elaboration is also difficult for llms which are not extensively trained on long text sequences. for example, despite it's 128k context length llama 3 expends only about 5.5% of it's computational budget training on text sequences longer than 8k tokens @xcite . patents are granted on the basis of novelty, meaning that if any information about the invention is in the public domain the patent application will be rejected. this limits the use of proprietary llms due to privacy concerns @xcite . instead, on premises deployment with open-source models is desirable. however, these models tend to be less capable which further exacerbates difficulty of automatically drafting patent disclosure. ## related work prior work in the field of patent generation has typically been centered around generating shorter sections of the specification such as the abstract or summary @xcite . some additional works have proposed tasks such as next claim generation or creating individual figure descriptions @xcite @xcite . other recent directions of research include paraphrasing disclosure and simplifying/revising claims @xcite . the closest related method to ours is patentformer @xcite . in this work, the authors fine-tune a gpt-j and t5 @xcite model on pairs of claims and their closest related paragraph in the specification. while this method does allow models to generate patent disclosure, it makes several simplifying assumptions that diverge from actual patent drafting practices, for instance, the notion that each paragraph in the specification maps to a single claim @xcite . training on pairs of claims and their closest matching paragraph also disincentivizes the model from elaborating. patent disclosure often reiterates the claims to some extent, which may encourage the model to simply reiterate claim content without including the external information needed for drafting the full disclosure. prior work on evaluating machine-generated patent disclosure has been limited. most existing works use metrics such as perplexity or bleu for evaluation @xcite . while this can be somewhat effective for short texts, these measures have been shown to struggle evaluating longer sequences @xcite . the most comprehensive work in this area is patenteval @xcite which proposes an error typology for generating new claims based on previous ones and for generating the abstract based on the claims. to our knowledge, there is no existing protocol for evaluating full patent specifications. ## method in this section we outline autospec, a novel agentic method for generating full patent specifications. our method is designed to take in the claims of a patent application, along with optional ocrextracted figure text, and generate the specification. autospec consists of three main components: the orchestrator, generator, and merger. an overview of the autospec workflow is illustrated in figure 2 . ## evaluation protocol to establish a consistent framework for evaluating patent disclosures, we developed a novel evaluation protocol in collaboration with experienced patent attorneys. this protocol is based on an annotation scheme that identifies key elements of highquality patent specification. disclosures are assessed across five categories, each rated on a scale from one to five. the category definitions and scoring guidelines are detailed below. language style evaluates how closely the language and word choice of the specification matches the style a human would use when writing a description of an invention. the disclosure should be dry and factual, avoiding excessive promotional or advocating language. it should not directly reference the claims (for example, by saying "as given in claim 1") and should avoid "patent profanity," which are overly specific words like "crucial" or "critical" when describing the invention. these terms unnecessarily limit the scope of the invention and reduce its enforceability. a score of one reflects pervasive issues, including excessive advocacy, use of patent profanity, and frequent claim references. a score of three indicates a mix of inappropriate and acceptable language, with substantial portions written in a suitable legal tone. a score of five signifies that the specification is almost entirely written in the proper style, using dry, factual language with minimal issues. minimal advocating language is acceptable. elaboration assesses how well the specification expands on the content of the claims. good disclosure should not simply repeat the claim language but should explain and elaborate on the key technical concepts contained in the claims to help the reader better understand the scope of invention. a score of one indicates that the specification simply restates the claims with little or no additional detail. a score of three suggests that some elaboration is present, but much of the disclosure closely mirrors the claim content. a score of five reflects thorough elaboration on all key technical concepts needed to adequately understand the invention. diversity score evaluates the diversity of language and content in the disclosure. good specification should not repeat the same content or unnecessarily extend its length by restating the same points. it should also avoid repeating long strings of text and should use some variation in language throughout. a score of one indicates a high level of repetition, with substantial duplication of content or long strings of identical text. a score of three suggests moderate repetition, though significant portions of the specification show adequate variation. a score of five indicates that the specification has little to no unnecessary repetition, resembling the writing style of a human. factual accuracy evaluates how accurate the content in the disclosure is. all of the content should be factual without hallucinations. the disclosure should also not contain any references to nonexistent figures, claims, other sections not present in the patent specification. a score of one indicates frequent inaccuracies, including false or misleading statements and references to nonexistent elements. a score of three reflects occasional issues, but the majority of the content is accurate and consistent with the claims. a score of five signifies that the disclosure is entirely accurate, with no hallucinated content or invalid references. coverage of claims evaluates whether or not there is any content missing in the disclosure. all of the claims should be addressed in the disclosure without any important information omitted. a score of one indicates that the specification covers few, if any, of the claims, with significant omissions. a score of three means that only partial coverage is provided, approximately half of the claim content is addressed. a score of five indicates that the specification fully incorporates all information from the claims, with no important elements missing. ## experiments we evaluate the effectiveness of our agent through both automated metrics and expert human assessments. the results demonstrate that autospec outperforms multiple baseline approaches in generating patent specifications. additionally, we perform an error analysis comparing autospec with two baseline models, using feedback provided by our expert evaluators. to support further research, we release the expert evaluation dataset, which includes 75 machine-generated patent disclosures annotated according to the protocol described in section 5. ## expert evaluation for our expert evaluation, we compared the performance of autospec against patentformer and gpt-4o (multi-gen). we generated patent disclosures for 25 biotechnology patents, selected by two experienced patent professionals. each disclosure was evaluated using the annotation scheme described in section 5. in addition, the experts were asked to rank the disclosures based on their usefulness to a patent attorney as a first draft. to the best of our knowledge, this represents the first expert evaluation conducted on full, machine-generated patent specifications. we measured inter-annotator agreement using kendall's tau @xcite and obtained a score of 0.15, indicating a statistically significant correlation between expert ratings (see appendix a for details). the results of our expert evaluation are presented in ## future work one promising direction of future work is to extend autospec to draft other sections of patent applications such as the claims. existing approaches to claim drafting typically generate claims either from prior claims or directly from the specification. however, this does not reflect the real-world drafting process, where patent attorneys often base claims on input provided by inventors. incorporating this workflow into autospec could lead to a more effective agent which can effectively generate both the claims and specification based on inventor-provided invention details. another potential direction is to develop more robust automatic metrics based on our evaluation protocol. in particular, assessing how closely llmgenerated annotations align with expert ratings could improve evaluation quality. this, in turn, could support the use of online training methods, such as reinforcement learning, to further refine patent drafting models. ## conclusion patent applications are key to protecting intellectual property and driving technological innovation. however, many smaller entities and individual inventors face obstacles to patenting their inventions due to the significant costs associated with drafting a patent application. to alleviate these issues we proposed autospec, an agentic framework for automatically drafting patent specifications. au-tospec's design is informed by expert input and mirrors the structured approach patent attorneys use to draft disclosures. to evaluate our framework we developed a novel, expert informed evaluation protocol for evaluating full patent disclosures. we evaluated our method using automatic and expert evaluations and found that our autospec agent outperforms existing baselines on a patent drafting task. additionally, we release a dataset of machinegenerated patent disclosures annotated according to our evaluation protocol, providing a valuable resource for further research.