Artificial intelligence (AI) is set to transform many aspects of our lives, including our home and health. AI is already widely used in internet searches, and home devices with speech recognition, but in the near future we will see AI become even more widespread. This will have significant repercussions as AI performs many tasks that until now could only be undertaken by humans. AI will remove human intervention from much of the picture. This will particularly affect intellectual property law.
In May 2015, The Simpsons voice actor Harry Shearer – who plays a number of key characters including, quite incredibly, both Mr Burns and Waylon Smithers – announced that he was leaving the show. By then, the animated series had been running for more than 25 years, and the pay of its vocal cast had risen from $30,000 an episode in 1998 to $400,000 an episode from 2008 onwards. But Fox, the producer of The Simpsons, was looking to cut costs – and was threatening to cancel the series unless the voice actors took a 30 per cent pay cut. Most of them agreed, but Shearer (who had been critical of the show's declining quality) refused to sign – after more than two decades, he wanted to break out of the golden handcuffs, and win back the freedom and the time to pursue his own work. Showrunner Al Jean said Shearer's iconic characters – who also include Principal Skinner, Ned Flanders and Otto Mann – would be recast.
In this paper we present a method to concatenate patent claims to their own description. By applying this method, BERT trains suitable descriptions for claims. Such a trained BERT (claim-to-description- BERT) could be able to identify novelty relevant descriptions for patents. In addition, we introduce a new scoring scheme, relevance scoring or novelty scoring, to process the output of BERT in a meaningful way. We tested the method on patent applications by training BERT on the first claims of patents and corresponding descriptions. BERT's output has been processed according to the relevance score and the results compared with the cited X documents in the search reports. The test showed that BERT has scored some of the cited X documents as highly relevant.
Existing deep learning models applied to reaction prediction in organic chemistry can reach high levels of accuracy (> 90% for Natural Language Processing-based ones). With no chemical knowledge embedded than the information learnt from reaction data, the quality of the data sets plays a crucial role in the performance of the prediction models. While human curation is prohibitively expensive, the need for unaided approaches to remove chemically incorrect entries from existing data sets is essential to improve artificial intelligence models' performance in synthetic chemistry tasks. Here we propose a machine learning-based, unassisted approach to remove chemically wrong entries from chemical reaction collections. We applied this method to the collection of chemical reactions Pistachio and to an open data set, both extracted from USPTO (United States Patent Office) patents. Our results show an improved prediction quality for models trained on the cleaned and balanced data sets. For the retrosynthetic models, the round-trip accuracy metric grows by 13 percentage points and the value of the cumulative Jensen Shannon divergence decreases by 30% compared to its original record. The coverage remains high with 97%, and the value of the class-diversity is not affected by the cleaning. The proposed strategy is the first unassisted rule-free technique to address automatic noise reduction in chemical data sets.
The history of invention is a history of knowledge spillovers. There is persistent evidence of knowledge flowing from one firm, industry, sector or region to another, either by accident or by design, enabling other inventions to be developed.1,6,9,13 For example, Thomas Edison's invention of the "electronic indicator" (US patent 307,031: 1884) spurred the development by John Fleming and Lee De Forest in early 20th century of early vacuum tubes which eventually enabled not just long-distance telecommunication but also early computers (for example, Guarnier10). Edison, in turn, learned from his contemporaries including Frederick Guthrie.11 It appears that little of this mutual learning and knowledge exchange was paid for and can thus be called a "spillover," that is, an unintended flow of valuable knowledge, an example of a positive externality. Information technologies have been a major source of knowledge spillovers.a Information is a basic ingredient of invention, and technologies that facilitate the manipulation and communication of information should also facilitate invention.
Spotify is currently working on an algorithm that could let musicians know whether their latest compositions copy parts of existing songs, reports the specialist magazine, Music Business Worldwide. Patent applications were apparently filed at the end of November in the US and in Europe, for a new technology named "Plagiarism Risk Detector and Interface." The invention is said to analyze so-called "lead sheets" -- a kind of musical score for songs denoting melody, chords and sometimes more -- to detect whether they copy any elements of any other tracks featured on the Spotify platform. These could be harmonies, sequences of chords or fragments of melody, for example. It could also provide a link to the track resembling the creation analyzed by the AI in order to facilitate rewriting.
The World Intellectual Property Organization's (WIPO) first report of a series called WIPO Technology Trends, an extensive study of patent applications and other scientific documents, offers clues to the next big thing in AI. Rather than treating'AI' as a single homogeneous discipline (see our guide to AI terminology), the WIPO report divides it into AI techniques, AI functional applications and AI application fields, offering a finer-grained analysis. AI techniques are advanced forms of statistical and mathematical models used in AI, including machine learning, logic programming, ontology engineering, probabilistic reasoning and fuzzy logic. Machine learning is included in more than one third of all identified inventions and represents 89 per cent of AI filings, the report finds. Between 2013 and 2016, filings related to deep learning rocketed by about 175 per cent.
I find them incredibly irritating. Those images you have to click on to prove that you are not a robot. If you are just one click away from a nice weekend away, you first have to figure out where you can see the traffic lights on 16 tiny fuzzy squares. Google makes grateful use of these puzzling attempts. For one thing, the company uses artificial intelligence to train its image recognition software.
The World Intellectual Property Office (WIPO) held its third "Conversation on Intellectual Property and Artificial Intelligence" on November 4, 2020, to discuss its revised issues paper on Intellectual Property Policy and Artificial Intelligence. Public bodies in the United States, United Kingdom, and European Union have each recently published reports on the interrelationship of AI on IP policy. In October 2020, the United States Patent and Trademark Office (USPTO) published a report, Public Views on Artificial Intelligence and Intellectual Property Policy, on two formal requests for comments, and the European Parliament published a report on intellectual property rights for the development of AI technologies. In September 2020, the UK's Intellectual Property Office (UKIPO) published a call for views on the policy considerations and future relationship between AI and IP. Courts in each jurisdiction have so far rejected the suggestion that AI has its own legal personality.