The patent domain is gaining attention in natural language processing research, offering practical applications in streamlining the patenting process and providing challenging benchmarks for large language models (LLMs). However, the generation of the description sections of patents, which constitute more than 90% of the patent document, has not been studied to date. We address this gap by introducing the task of outline-guided paper-to-patent generation, where an academic paper provides the technical specification of the invention and an outline conveys the desired patent structure. We present PAP2PAT, a new challenging benchmark of 1.8k patent-paper pairs with document outlines, collected using heuristics that reflect typical research lab practices. Our experiments with current open-weight LLMs and outline-guided chunk-based generation show that they can effectively use information from the paper but struggle with repetitions, likely due to the inherent repetitiveness of patent language. We release our data and code.

In patent prosecution, timely and effective responses to Office Actions (OAs) are crucial for acquiring patents, yet past automation and AI research have scarcely addressed this aspect. To address this gap, our study introduces the Patent Office Action Response Intelligence System (PARIS) and its advanced version, the Large Language Model Enhanced PARIS (LE-PARIS). These systems are designed to expedite the efficiency of patent attorneys in collaboratively handling OA responses. The systems' key features include the construction of an OA Topics Database, development of Response Templates, and implementation of Recommender Systems and LLM-based Response Generation. Our validation involves a multi-paradigmatic analysis using the USPTO Office Action database and longitudinal data of attorney interactions with our systems over six years. Through five studies, we examine the constructiveness of OA topics (studies 1 and 2) using topic modeling and the proposed Delphi process, the efficacy of our proposed hybrid recommender system tailored for OA (both LLM-based and non-LLM-based) (study 3), the quality of response generation (study 4), and the practical value of the systems in real-world scenarios via user studies (study 5). Results demonstrate that both PARIS and LE-PARIS significantly meet key metrics and positively impact attorney performance.

It may be smart, but it's not that clever. Artificial intelligence is nothing without human input. The algorithms that drive AI rely on the expertise of programmers and it's still no more than a tool – albeit a powerful one – that scientists and engineers can use to solve problems. Yet this is not to say that AI isn't the fastest-growing deep technology in the world, with the potential to transform people's lives and boost nations' economies. Facilitating AI innovation has even become a priority for the UK government, as laid out in the National AI Strategy it published in September.

It may be smart, but it's not that clever. Artificial intelligence is nothing without human input. The algorithms that drive AI rely on the expertise of programmers and it's still no more than a tool – albeit a powerful one – that scientists and engineers can use to solve problems. Yet this is not to say that AI isn't the fastest-growing deep technology in the world, with the potential to transform people's lives and boost nations' economies. Facilitating AI innovation has even become a priority for the UK government, as laid out in the National AI Strategy it published in September.

Give us Innovative Puzzle and we will solve that puzzle. Intellectual Property Right scenario is to work in holistic view for knowledge exchange and visioning a world to solve massive issues which needs urgent attention to increase GDP of India aka Bharat. Are you looking for Chief Innovation Officier? Are you looking for Invention Harvesting and Invention Management? Doing international patent information researches, Patent Mapping to products to identify infringers, Intellectual Property Rights management for universities, drafting patent specifications, preparing response to notification of reasons for innovation refusal, go to person to facilitate patent licensing and initiating patent infringement proceedings before the Delhi High Court.

Most of us know that DATA, the beloved android from Star Trek, The Next Generation, is an artificial intelligence (AI) life form from the distant future with a high capacity to problem solve and innovate. But, if DATA were present today and invented a new technology, could he be an inventor on a patent for his invention? The question of whether AI can legally be an inventor on a patent was recently addressed by the European Patent Office (EPO) and The United Kingdom Intellectual Property Office (UKIPO). The same question is still being evaluated by U.S. Patent and Trademark Office (USPTO) along with solicitation for comments to the patent community. A group from the University of Surrey, in the United Kingdom (UK), recently challenged the definition of "inventor" in Europe and the United States by filing two separate patent applications designating an AI entity as an inventor.

Patents are filed in China for international protection of inventions under PCT route after domestic patent filing. The first patent application is filed in home country to secure a patent priority date. Thereafter, within 12 months of the priority date, a PCT international phase application is filed with the WIPO. The PCT international patent application provides a time period of almost 30 to 31 months from the priority date to file the same patent application in multiple countries, including, India, China, Singapore, and the like. China and India amended their patent laws to fulfill the obligation of TRIPS agreement.

The overall cost to obtain a utility patent for an invention is generally $8000 to $18000, depending upon the country where patent filing is desired. A utility patent application includes patent claims, drawings and description of the invention. The patent application cost in India covers the official filing fee and the patent attorney charges for drafting a patent application. The cost of patent application in India may also include charges for conducting a patent prior art search. The results of patent search are used to perform a patentability analysis.

The deep learning algorithms of artificial intelligence can identify patterns that help inventors think laterally, make connections between nonobvious ideas, pinpoint hidden invention features, and exploit new science and technology-based opportunities. "To invent, you need a good imagination and a pile of junk." So said Thomas Edison, America's most prolific inventor. Yet the march of technology is now changing the great man's inventive equation: powerful algorithmic advisory systems are now giving inventors far more fertile imaginations, even if they don't have very much of one themselves. After being fed vast datasets of information on a field of inventive endeavor, deep learning algorithms identify patterns that help inventors think laterally, make connections between nonobvious ideas, pinpoint hidden invention features that rivals have missed, and exploit new science and technology-based opportunities from, say, patents and journals.

Marc Kaufman typically needs 20 hours to create one patent application for a software innovation. But the Washington-based patent attorney says he's saving valuable time with a little high-tech help. Kaufman, a partner at Rimon PC, has been using an artificial intelligence tool called Specifio that analyzes claims--words that define an invention--and generates a bare-bones draft with a description and illustrations in a couple of minutes. He says the tool he started using six months ago has given him five extra hours to fine-tune his clients' applications to boost their likelihood of approval. "Saving hours allows me to really understand the client's business and be very strategic with the patent application, while coming in at a price that clients are willing to pay," Kaufman said.