Intellectual Property and Patents

Neueste Paper

• From Rules to Neural Graphs: Scalable Structured Prediction for Patent Prior Art Search

  Nikolai Zenovkin, Sebastian Bj\"orkqvist · 2. Oktober 2026

  Patent search requires processing documents routinely exceeding tens of thousands of tokens. Most neural retrieval approaches operate on truncated inputs, limiting their effectiveness. Graph-based retrieval addresses this by representing each patent as a structured invention graph, but constructing …

• Learning and Predicting Patent Technology Reuse Trajectories from Emergence-Time Signals

  Ayham Yousef, Qiang Ye, Qiang Cheng · 2. Oktober 2026

  Forecasting how a newly emerged patent technology will be reused is central to technology intelligence, but reuse-pattern labels do not exist in advance: they must be constructed from the trajectories themselves, and how they are constructed determines what a forecast means. We study $201{,}710$ nov…

• LogicTree-RAG: Logic Tree-guided Retrieval-Augmented Generation for Long-form Patent Drafting

  Jiaqi Zhu, Naili Xing, Hexiang Pan, Haotian Gao, Jianwei Yin, Xiaokui Xiao, Beng Chin Ooi · 28. September 2026

  Long-form technical text generation underpins knowledge-intensive workflows, yet remains challenging for large language models (LLMs) due to the need for globally consistent logical structuring and faithful technical reasoning beyond local coherence. Patent drafting is a canonical instance of this c…

• Vibe Patenting: Evaluating LLM Judges for Professional Patent-Drafting Agents

  Toshiaki Koike-Akino, Vlad Blaykhman, Ye Wang, Jing Liu, Gene V. Vinokur · 15. September 2026

  LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We study this problem through Vibe Patenting, an end-to-end patent-drafting testbed for AI-agent evaluation. A separately-invoked LLM judge evaluates ge…

• Modeling Claim Dependency Structure for Patent Litigation Prediction with Graph Attention Networks

  Takao Arai, Hiroyasu Inoue · 25. August 2026

  Patent litigation imposes substantial costs on firms and distorts R&D incentives, making early risk identification a practically important task. While prior work has applied BERT-based models to patent claim text, two fundamental limitations remain: flat sequence encoding loses the dependency struct…

• Self-Knowledge Retrieval Augmented Generation Framework for Patent Matching

  Jian Zhang, Songlin Lei, Zhuohao Yang, Bangli Liu, Ziwei Wang, Xufeng Weng, Gehan Amaratunga, Yu Lin, Hongwei Wang · 12. August 2026

  Patent retrieval and matching based on large language models (LLMs) play a vital role in intellectual property protection. However, due to the complex structure of patent documents, dense technical terminology, and multi-modal information, traditional methods struggle to accurately identify subtle d…

• Pointer-Augmented Autoregressive Generation of Patent Claims with Joint Topology and Content Decoding

  Yongmin Yoo, Zhangkai Wu, Longbing Cao · 28. Juli 2026

  Autoregressive decoders emit flat token sequences and cannot enforce hierarchical constraints across output segments, a limitation that becomes acute in patent claim generation, where a claim set forms a dependency forest whose scope must narrow monotonically with depth. Topology and content are mut…

• Measuring AI innovation with trademark data

  C. Castaldi, F. Castellacci, A. Fronzetti Colladon, L. Segneri, F. Venturini · 22. Juli 2026

  Researchers, managers and policymakers are exploring different approaches and data sources to map the development and the diffusion of Artificial Intelligence (AI). In this research note, we illustrate the opportunities offered by trademark data. We argue that AI trademarks can complement AI patents…

• When Reasoning Hurts Legal Drafting: The Verbalization Bottleneck in Patent Claim Generation

  Lekang Jiang, Wenjun Sun, Stephan Goetz · 14. Juli 2026

  Patent claim drafting is a challenging legal drafting task that requires technical expertise, precise linguistic control, strict adherence to formal conventions, and the preservation of complex logical relationships among claim elements. While Chain-of-Thought (CoT) prompting has been widely used to…

• Profiling and Evolution of Intellectual Property

  Bowen Yu, Yingxia Shao, Ang Li · 14. Juli 2026

  In recent years, with the rapid growth of Internet data, the number and types of scientific and technological resources are also rapidly expanding. However, the increase in the number and category of information data will also increase the cost of information acquisition. For technology-based enterp…

• From Patent Expiry to Business Pathways: AI Workflows for Activating Innovation Archives

  Sidney Shapiro, Mark Price · 14. Juli 2026

  Patent databases represent one of the largest public archives of technical knowledge, yet much of this knowledge remains difficult to identify, interpret, and reuse once patent rights expire or lapse. This paper proposes an AI-enabled framework for discovering expired and lapsing patents, identifyin…

• Orchestrating the Twin Transition in Multinational Corporations: Technology Roadmapping for Green and Digital Global Business Services

  Han-Teng Liao, Karen Ang · 12. Juni 2026

  Global Business Services (GBS) have emerged as a "living laboratory" for the Twin Transition of Green and Digital Transformation, as multinational corporations (MNCs) face increasing pressure to harmonize digital efficiency with environmental stewardship. Aiming to derive a socio-technical framework…

• A Framework for Graph-Conditioned Hierarchical Shapley Attribution in Patent Valuation

  Joy Bose · 2. Juni 2026

  Estimating the economic contribution of a single patent inside a product that embodies tens of thousands of patents is a long-standing unsolved problem in intellectual property economics. We propose PatentXAI, a framework that treats patent valuation as a problem of explainable AI: given a character…

• Benchmarking Patent Embeddings: A Multi-Task Evaluation of 22 Models Across Retrieval, Classification, and Clustering

  Amirhossein Yousefiramandi, Ciaran Cooney · 26. Mai 2026

  Which fine-tuning signals improve patent embedding models, and do gains transfer across patent landscapes? We benchmark 22 embedding models, from 22M-parameter encoders to 12B instruction-tuned LLMs, on retrieval, classification, and clustering. The study uses 113,148 WIPO assistive-technology paten…

• When Does Synthetic Patent Data Help? Volume-Fidelity Trade-offs in Low-Resource Multi-Label Classification

  Amirhossein Yousefiramandi, Ciaran Cooney · 26. Mai 2026

  We study when LLM-generated synthetic data helps low-resource multi-label patent classification, separating true synthetic value from the confound that larger augmented sets can win by volume alone. Across six open-source LLMs (3.8-12B), four real-data regimes, 64 WIPO assistive-technology labels, t…

• The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale

  Peter A. Jansen · 15. Mai 2026

  Scientific contributions rarely develop in isolation, but instead build upon prior discoveries. We formulate the task of automated technological roadmapping as extracting scientific contributions from scholarly articles and linking them to their prerequisites. We present the Scientific Contribution …

• IdeaForge: A Knowledge Graph-Grounded Multi-Agent Framework for Cross-Methodology Innovation Analysis and Patent Claim Generation

  Joy Bose · 14. Mai 2026

  Current AI-assisted innovation systems typically apply a single ideation methodology (such as TRIZ or Design Thinking) using sequential prompt-based workflows that do not preserve intermediate reasoning structure. As a result, insights generated across methodologies remain fragmented, limiting trace…

• Unintended Negative Impacts of Promotional Language in Patent Evaluation

  Bingkun Zhao, Chenwei Zhang, Hao Peng · 7. Mai 2026

  Promotional language has been increasingly used to aid the communication of innovative ideas in science. Yet, less is known about its role in the context of technological innovation. Here, we use a validated and domain-diagnosed lexicon of 135 promotional words to study the association between promo…

• Anticipating Innovation Using Large Language Models

  Enrico Maria Fenoaltea, Filippo Santoro, Giordano De Marzo, Segun Taofeek Aroyehun, Andrea Tacchella · 7. Mai 2026

  Forecasting innovation, intended as the emergence of new technological combinations, is a fundamental challenge for science and policy. We show that forthcoming combinations leave an early trace in the collective language of patents, with predictive signals detectable even decades in advance. We sho…

• PatRe: A Full-Stage Office Action and Rebuttal Generation Benchmark for Patent Examination

  Qiyao Wang, Xinyi Chen, Longze Chen, Hongbo Wang, Hamid Alinejad-Rokny, Yuan Lin, Min Yang · 6. Mai 2026

  Patent examination is a complex, multi-stage process requiring both technical expertise and legal reasoning, increasingly challenged by rising application volumes. Prior benchmarks predominantly view patent examination as discriminative classification or static extraction, failing to capture its inh…

• Is It Novel and Why? Fine-Grained Patent Novelty Prediction Based on Passage Retrieval

  Valentin Knappich, Anna H\"atty, Simon Razniewski, Annemarie Friedrich · 6. Mai 2026

  Novelty assessment is a critical yet complex task in the examination process for patent acceptance, requiring examiners to determine whether an invention is disclosed in a prior art document. The process involves intricate matching between specific features of a patent claim and passages in the prio…

• Citation-Driven Multi-View Training for Patent Embeddings: QaECTER and Sophia-Bench

  Younes Djemmal (ALMAnaCH), You Zuo (ALMAnaCH), Kim Gerdes (LISN, Qatent), Kirian Guiller · 29. April 2026

  Patent retrieval underpins critical decisions in innovation, examination, and IP strategy, yet progress has been hampered by the absence of benchmarks that reflect the diversity of real world search scenarios. We address this gap with two contributions. First, we introduce Sophiabench, a large-scale…

• Aggregate vs. Personalized Judges in Business Idea Evaluation: Evidence from Expert Disagreement

  Wataru Hirota, Tomoki Taniguchi, Tomoko Ohkuma, Kosuke Takahashi, Takahiro Omi, Kosuke Arima, Takuto Asakura, Chung-Chi Chen, Tatsuya Ishigaki · 27. April 2026

  Evaluating LLM-generated business ideas is often harder to scale than generating them. Unlike standard NLP benchmarks, business idea evaluation relies on multi-dimensional criteria such as feasibility, novelty, differentiation, user need, and market size, and expert judgments often disagree. This pa…

• Formally Verified Patent Analysis via Dependent Type Theory: Machine-Checkable Certificates from a Hybrid AI + Lean 4 Pipeline

  George Koomullil · 22. April 2026

  We present a formally verified framework for patent analysis as a hybrid AI + Lean 4 pipeline. The DAG-coverage core (Algorithm 1b) is fully machine-verified once bounded match scores are fixed. Freedom-to-operate, claim-construction sensitivity, cross-claim consistency, and doctrine-of-equivalents …

• Market Dynamics, Governance and Open Research Metadata in the AI Era

  Daniel W. Hook · 22. April 2026

  The debate about scholarly knowledge infrastructure has long been framed as a contest between openness and commercial enclosure. This framing distorts both policy and practice. The real tension lies between the persistent cost of producing and refining structured metadata under deep technological fr…