With more than 4.5 million patent documents, HUPD is two to three times larger than comparable corpora.

This paper presents a simple yet effective contrastive learning framework for learning patent embeddings by leveraging multiple views from within the same document. We first identify a patent-specific failure mode of SimCSE style dropout augmentation: it produces overly uniform embeddings that lose semantic cohesion. To remedy this, we propose section-based augmentation, where different sections of a patent (e.g., abstract, claims, background) serve as complementary views. This design introduces natural semantic and structural diversity, mitigating over-dispersion and yielding embeddings that better preserve both global structure and local continuity. On large-scale benchmarks, our fully self-supervised method matches or surpasses citation-and IPC-supervised baselines in prior-art retrieval and classification, while avoiding reliance on brittle or incomplete annotations. Our analysis further shows that different sections specialize for different tasks-claims and summaries benefit retrieval, while background sections aid classification-highlighting the value of patents' inherent discourse structure for representation learning. These results highlight the value of exploiting intra-document views for scalable and generalizable patent understanding.

Patent drafting presents significant challenges due to its reliance on the extensive experience and specialized expertise of patent attorneys, who must possess both legal acumen and technical understanding of an invention to craft patent applications in a formal legal writing style. This paper presents a demonstration of Patentformer, an AI-powered automated patent drafting platform designed to support patent attorneys by rapidly producing high-quality patent applications adhering to legal writing standards.

In 2000 TIME's editors sat down to select three inventions of the year, one each in consumer technology, medical science, and basic industry. They found so many interesting ones along the way that they included dozens of others, from an unbreakable lightbulb to paper that was easier to recycle. It was the start of our annual hunt for the most exciting innovations changing our lives, and the future. Since then, TIME has covered hundreds of inventions, from the esoteric (clouds featured more than once) to essential, including life-changing medicines, technological breakthroughs, new foods, nearly every new Apple product category, and even a few great ideas that didn't quite catch on. As TIME publishes the 2025 list, we're also assembling the Best Inventions Hall of Fame: the 25 most iconic inventions we covered in the past quarter century. Almost all women in the U.S. use contraception at some point in their lives, and in 2001 a new option came on the market, the vaginal ring. As TIME wrote when including it among the year's best inventions, "Some women hate taking pills. In early October the FDA approved use of the NuvaRing, a thin flexible plastic ring that women can flatten like a rubber band and insert once a month into the vagina."

For each of the past 25 years, TIME editors have highlighted the most impactful new products and ideas in TIME's Best Inventions issue. The first, published under a cover featuring the protracted Bush v. Gore presidential vote count in December 2000, covered about 35 inventions, including some that feel a world away: the Ricoh RDC-i700 (a digital camera that could post photos to the internet), the first 3D ultrasound imaging for pregnant parents, and a bike with two pontoons that intrepid cyclists could ride on a lake. Others could just as easily be on the 2025 list. Medtronic's Activa Tremor Control Therapy was featured in the 2000 issue as one of the first forays into deep brain stimulation as a treatment for Parkinson's. This year's issue includes the same company's newly FDA-approved upgrade to the same technology, BrainSense, which continually adjusts to patients' unique tremors.

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.

The humble inventions that power our modern world wouldn't have been possible without decades of support for early-stage research. In December 1947, three physicists at Bell Telephone Laboratories--John Bardeen, William Shockley, and Walter Brattain--built a compact electronic device using thin gold wires and a piece of germanium, a material known as a semiconductor. Their invention, later named the transistor (for which they were awarded the Nobel Prize in 1956), could amplify and switch electrical signals, marking a dramatic departure from the bulky and fragile vacuum tubes that had powered electronics until then. They were asking fundamental questions about how electrons behave in semiconductors, experimenting with surface states and electron mobility in germanium crystals. Over months of trial and refinement, they combined theoretical insights from quantum mechanics with hands-on experimentation in solid-state physics--work many might have dismissed as too basic, academic, or unprofitable. Their efforts culminated in a moment that now marks the dawn of the information age.

A pioneering rule-based mechanical translation system (precursor of modern RBMTs) was first presented in December 1929 by its inventor, Federico Pucci, who later published the full method in a book titled "Il traduttore meccanico ed il metodo per corrispondersi fra Europei conoscendo ciascuno solo la propria lingua: Parte I", in Salerno (Italy), in 1931. This study illustrates how AI breathes new life into the system of international keys and ideograms devised by Pucci to translate from/into any Romance language (at least as a first step). The methodology involves having the AIs retranslate, following Pucci's method, the two text excerpts originally translated in 1931 and clearly documented in his publication: a passage from Dante's La Vita Nuova, translated from Italian into French, and a passage from Voltaire's Zadig, translated from French into Italian. The result is notable: the two texts, translated 94 years apart using the same method--by Pucci in 1931 and by AIs in 2025--show a low average difference, with only minor variations observed. With Pucci's system thus validated, it became feasible to have the AIs reproduce the excerpts in English, Spanish, and German according to his method. The results were consistent, and Pucci--via Artificial Intelligence--was tasked with translating more modern and technical texts, thereby reviving, nearly a century later, an invention that had remained almost entirely unknown and never applied beyond its creator, now brought to wider attention and opened to possible experimentation. Such a demonstration would not only affirm Pucci's historical status but also place him among the precursors and intellectual contributors to machine translation, whose work merits examination alongside figures such as Troyanskij, Booth, and Weaver, with possible consequences for how the history of the field is understood.

In an age of fast-paced technological change, patents have evolved into not only legal mechanisms of intellectual property, but also structured storage containers of knowledge full of metadata, categories, and formal innovation. This chapter proposes to reframe patents in the context of information science, by focusing on patents as knowledge artifacts, and by seeing patents as fundamentally tied to the global movement of scientific and technological knowledge. With a focus on three areas, the inventions of AIs, biotech patents, and international competition with patents, this work considers how new technologies are challenging traditional notions of inventorship, access, and moral accountability.The chapter provides a critical analysis of AI's implications for patent authorship and prior art searches, ownership issues arising from proprietary claims in biotechnology to ethical dilemmas, and the problem of using patents for strategic advantage in a global context of innovation competition. In this analysis, the chapter identified the importance of organizing information, creating metadata standards about originality, implementing retrieval systems to access previous works, and ethical contemplation about patenting unseen relationships in innovation ecosystems. Ultimately, the chapter called for a collaborative, transparent, and ethically-based approach in managing knowledge in the patenting environment highlighting the role for information professionals and policy to contribute to access equity in innovation.