The Global Artificial Intelligence Revolution Challenges Patent Eligibility Laws

This Article examines patent eligibility jurisprudence of artificial intelligence in the United States, Europe, France, Japan, and Singapore. It identifies de facto requirements of patent-eligible artificial intelligence. It also examines the adaptability of patent eligibility jurisprudence to adapt with the growth of artificial intelligence

Automated Identification of National Implementations of European Union Directives With Multilingual Information Retrieval Based On Semantic Textual Similarity

The effective transposition of European Union (EU) directives into Member States is important to achieve the policy goals defined in the Treaties and secondary legislation. National Implementing Measures (NIMs) are the legal texts officially adopted by the Member States to transpose the provisions of an EU directive. The measures undertaken by the Commission to monitor NIMs are time-consuming and expensive, as they resort to manual conformity checking studies and legal analysis. In this thesis, we developed a legal information retrieval system using semantic textual similarity techniques to automatically identify the transposition of EU directives into the national law at a fine-grained provision level. We modeled and developed various text similarity approaches such as lexical, semantic, knowledge-based, embeddings-based and concept-based methods. The text similarity systems utilized both textual features (tokens, N-grams, topic models, word and paragraph embeddings) and semantic knowledge from external knowledge bases (EuroVoc, IATE and Babelfy) to identify transpositions. This thesis work also involved the development of a multilingual corpus of 43 directives and their corresponding NIMs from Ireland (English legislation), Italy (Italian legislation) and Luxembourg (French legislation) to validate the text similarity based information retrieval system. A gold standard mapping (prepared by two legal researchers) between directive articles and NIM provisions was prepared to evaluate the various text similarity models. The results show that the lexical and semantic text similarity techniques were more effective in identifying transpositions as compared to the embeddings-based techniques. We also observed that the unsupervised text similarity techniques had the best performance in case of the Luxembourg Directive-NIM corpus. We also developed a concept recognition system based on conditional random fields (CRFs) to identify concepts in European directives and national legislation. The results indicate that the concept recognitions system improved over the dictionary lookup program by tagging the concepts which were missed by dictionary lookup. The concept recognition system was extended to develop a concept-based text similarity system using word-sense disambiguation and dictionary concepts. The performance of the concept-based text similarity measure was competitive with the best performing text similarity measure. The labeled corpus of 43 directives and their corresponding NIMs was utilized to develop supervised text similarity systems by using machine learning classifiers. We modeled three machine learning classifiers with different textual features to identify transpositions. The results show that support vector machines (SVMs) with term frequency-inverse document frequency (TF-IDF) features had the best overall performance over the multilingual corpus. Among the unsupervised models, the best performance was achieved by TF-IDF Cosine similarity model with macro average F-score of 0.8817, 0.7771 and 0.6997 for the Luxembourg, Italian and Irish corpus respectively. These results demonstrate that the system was able to identify transpositions in different national jurisdictions with a good performance. Thus, it has the potential to be useful as a support tool for legal practitioners and Commission officials involved in the transposition monitoring process

Automated Identification of National Implementations of European Union Directives with Multilingual Information Retrieval based on Semantic Textual Similarity

The effective transposition of European Union (EU) directives into Member States is important to achieve the policy goals defined in the Treaties and secondary legislation. National Implementing Measures (NIMs) are the legal texts officially adopted by the Member States to transpose the provisions of an EU directive. The measures undertaken by the Commission to monitor NIMs are time-consuming and expensive, as they resort to manual conformity checking studies and legal analysis. In this thesis, we developed a legal information retrieval system using semantic textual similarity techniques to automatically identify the transposition of EU directives into the national law at a fine-grained provision level. We modeled and developed various text similarity approaches such as lexical, semantic, knowledge-based, embeddings-based and concept-based methods. The text similarity systems utilized both textual features (tokens, N-grams, topic models, word and paragraph embeddings) and semantic knowledge from external knowledge bases (EuroVoc, IATE and Babelfy) to identify transpositions. This thesis work also involved the development of a multilingual corpus of 43 directives and their corresponding NIMs from Ireland (English legislation), Italy (Italian legislation) and Luxembourg (French legislation) to validate the text similarity based information retrieval system. A gold standard mapping (prepared by two legal researchers) between directive articles and NIM provisions was prepared to evaluate the various text similarity models. The results show that the lexical and semantic text similarity techniques were more effective in identifying transpositions as compared to the embeddings-based techniques. We also observed that the unsupervised text similarity techniques had the best performance in case of the Luxembourg Directive-NIM corpus

Streaming and Sketch Algorithms for Large Data NLP

The availability of large and rich quantities of text data is due to the emergence of the World Wide Web, social media, and mobile devices. Such vast data sets have led to leaps in the performance of many statistically-based problems. Given a large magnitude of text data available, it is computationally prohibitive to train many complex Natural Language Processing (NLP) models on large data. This motivates the hypothesis that simple models trained on big data can outperform more complex models with small data. My dissertation provides a solution to effectively and efficiently exploit large data on many NLP applications. Datasets are growing at an exponential rate, much faster than increase in memory. To provide a memory-efficient solution for handling large datasets, this dissertation show limitations of existing streaming and sketch algorithms when applied to canonical NLP problems and proposes several new variants to overcome those shortcomings. Streaming and sketch algorithms process the large data sets in one pass and represent a large data set with a compact summary, much smaller than the full size of the input. These algorithms can easily be implemented in a distributed setting and provide a solution that is both memory- and time-efficient. However, the memory and time savings come at the expense of approximate solutions. In this dissertation, I demonstrate that approximate solutions achieved on large data are comparable to exact solutions on large data and outperform exact solutions on smaller data. I focus on many NLP problems that boil down to tracking many statistics, like storing approximate counts, computing approximate association scores like pointwise mutual information (PMI), finding frequent items (like n-grams), building streaming language models, and measuring distributional similarity. First, I introduce the concept of approximate streaming large-scale language models in NLP. Second, I present a novel variant of the Count-Min sketch that maintains approximate counts of all items. Third, I conduct a systematic study and compare many sketch algorithms that approximate count of items with focus on large-scale NLP tasks. Last, I develop fast large-scale approximate graph (FLAG), a system that quickly constructs a large-scale approximate nearest-neighbor graph from a large corpus

Asian Yearbook of International Law, Volume 14 (2008)

International la

Special Libraries, March 1980

Volume 71, Issue 3https://scholarworks.sjsu.edu/sla_sl_1980/1002/thumbnail.jp