The Patent Act requires an "inventor" to be a natural person, the US Court of Appeals for the Federal Circuit said, rejecting computer scientist Stephen Thaler's bid for patents on two inventions he said his DABUS system created. Thaler said in an email Friday that DABUS, which stands for "Device for the Autonomous Bootstrapping of Unified Sentience," is "natural and sentient." His attorney Ryan Abbott of Brown Neri Smith & Khan said the decision "ignores the purpose of the Patent Act" and has "real negative social consequences." He said they plan to appeal. The US Patent and Trademark Office declined to comment on the decision.
Thaler had asked for patents on behalf of his AI system Court affirms ruling that patent'inventor' must be human being Court affirms ruling that patent'inventor' must be human being The Patent Act requires an "inventor" to be a natural person, the U.S. Court of Appeals for the Federal Circuit said, rejecting computer scientist Stephen Thaler's bid for patents on two inventions he said his DABUS system created. Thaler said in an email Friday that DABUS, which stands for "Device for the Autonomous Bootstrapping of Unified Sentience," is "natural and sentient." His attorney Ryan Abbott of Brown Neri Smith & Khan said the decision "ignores the purpose of the Patent Act" and has "real negative social consequences." He said they plan to appeal. The U.S. Patent and Trademark Office declined to comment on the decision.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. If there is one thing common to all legal cases, it is documents. In decades past, the evidence collected in litigation was often confined to digging through folders and filing cabinets, in a process called discovery. Today, electronic discovery, or'ediscovery,' is the name of the game – with paper documents replaced by millions of emails, Slack messages and Zoom calls. MarketsandMarkets estimates the global ediscovery market size to grow from $9.3 billion in 2020 to $12.9 billion by 2025.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Artificial intelligence and machine learning are common phrases nowadays, and very few people are unaware of them. However, any time a new idea launches, people are pretty reluctant to accept it. Lawyers and legal professionals are no exception. Artificial intelligence (AI) and machine learning are already transforming the work of lawyers and law firms in many ways and there are enormous opportunities for the future. Let's discuss how artificial intelligence and machine learning have gradually transformed law firms (both in positive and negative ways) and how they can continue to improve. First, let's discuss the positive effects of machine learning and AI on the legal industry. Breaking down legal procedures or duties traditionally handled by legal practitioners and embedding some of those parts in technology is how legal automation is accomplished.
The Final Report also makes specific recommendations for the introduction of legislation which regulates the use of facial recognition and other biometric technology, and for a moratorium on the use of this technology in AI-informed decision-making until such legislation is enacted. The recommendations of the AHRC have been submitted to the Australian Government. The Australian Government has the ability to determine whether to adopt the recommendations of the Report or not. The adoption of the AHRC's recommendations for the introduction of specific legislation governing the use of AI would signal a change in the approach to the regulation of AI and other emerging technologies that has been adopted in Australia to date. Free data access is an issue in the use of AI tools in the provision of legal services in Australia. The success of an AI tool will be determined by the size and diversity of the sample data which is used to train that tool. There are a number of factors that contribute to free data access in Australia and generally these factors apply across the spectrum of different categories of AI tools discussed in question 2 (being litigation, transactional and knowledge management tools).
Intel Corp,. on Dec. 27, the U.S. District Court for the Western District of Texas found claims of machine-learning patents invalid under Title 35 of the U.S. Code, Section 101, in a motion to dismiss filed under Federal Rule of Civil Procedure 12(b)(6). This decision, on one hand, provides a road map that skilled counsel can follow to draft patents that are more likely to withstand eligibility challenges, but, on the other hand, could make patenting artificial intelligence inventions more nuanced absent due care.
Amazon revealed it has filed suit against a pair of services that "orchestrate the posting of incentivized and misleading product reviews, in exchange for money or free products." The duo in question, AppSally and Rebatest, allegedly provide their services for product listings on Amazon.com, as well as on eBay, Walmart, and Etsy listings. The online retailer made it clear that the goal of its suit is to "shut down two major fake review brokers." Dharmesh Mehta, VP of WW Customer Trust & Partner Support, at Amazon noted that his company prevents "millions of suspicious reviews from ever appearing," but it still filed these lawsuits as a way to "target the source." Amazon currently uses a combination of its machine learning technology and human investigators to track and remove false reviews.
Artificial Intelligence (AI) is disrupting almost every industry and profession, some faster and more profoundly than others. Unlike the industrial revolution that automated physical labor and replaced muscles with hydraulic pistons and diesel engines, the AI-powered revolution is automating mental tasks. While it may be merely optimizing some blue-collar jobs, AI is bringing about a more fundamental change to many white-collar roles previously thought safe from automation. Some of these professions are being completely transformed by the superhuman capabilities of AI to do things that were not possible before, augmenting -- and to some degree replacing -- their human colleagues in offices. In this way, AI is having a profound effect on the practice of law.
In populous countries, pending legal cases have been growing exponentially. There is a need for developing techniques for processing and organizing legal documents. In this paper, we introduce a new corpus for structuring legal documents. In particular, we introduce a corpus of legal judgment documents in English that are segmented into topical and coherent parts. Each of these parts is annotated with a label coming from a list of pre-defined Rhetorical Roles. We develop baseline models for automatically predicting rhetorical roles in a legal document based on the annotated corpus. Further, we show the application of rhetorical roles to improve performance on the tasks of summarization and legal judgment prediction. We release the corpus and baseline model code along with the paper.
We introduce Active Predictive Coding Networks (APCNs), a new class of neural networks that solve a major problem posed by Hinton and others in the fields of artificial intelligence and brain modeling: how can neural networks learn intrinsic reference frames for objects and parse visual scenes into part-whole hierarchies by dynamically allocating nodes in a parse tree? APCNs address this problem by using a novel combination of ideas: (1) hypernetworks are used for dynamically generating recurrent neural networks that predict parts and their locations within intrinsic reference frames conditioned on higher object-level embedding vectors, and (2) reinforcement learning is used in conjunction with backpropagation for end-to-end learning of model parameters. The APCN architecture lends itself naturally to multi-level hierarchical learning and is closely related to predictive coding models of cortical function. Using the MNIST, Fashion-MNIST and Omniglot datasets, we demonstrate that APCNs can (a) learn to parse images into part-whole hierarchies, (b) learn compositional representations, and (c) transfer their knowledge to unseen classes of objects. With their ability to dynamically generate parse trees with part locations for objects, APCNs offer a new framework for explainable AI that leverages advances in deep learning while retaining interpretability and compositionality.