Novelty (patent) in the context of "Patentability"

An invention is a unique or novel device, method, composition, idea, or process. An invention may be an improvement upon a machine, product, or process for increasing efficiency or lowering cost. It may also be an entirely new concept. If an idea is unique enough either as a stand-alone invention or as a significant improvement over the work of others, it can be patented. A patent, if granted, gives the inventor a proprietary interest in the patent over a specific period of time, which can be licensed for financial gain.

An inventor creates or discovers an invention. The word inventor comes from the Latin verb invenire, invent-, to find. Although inventing is closely associated with science and engineering, inventors are not necessarily engineers or scientists. The ideation process may be augmented by the applications of algorithms and methods from the domain collectively known as artificial intelligence.

Under United States law, a patent is a right granted to the inventor of a (1) process, machine, article of manufacture, or composition of matter, (2) that is new, useful, and non-obvious. A patent is the right to exclude others, for a limited time (usually, 20 years) from profiting from a patented technology without the consent of the patent holder. Specifically, it is the right to exclude others from: making, using, selling, offering for sale, importing, inducing others to infringe, applying for an FDA approval, and/or offering a product specially adapted for practice of the patent.

Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005), there are three perspectives of text mining: information extraction, data mining, and knowledge discovery in databases (KDD). Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).

Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via the application of natural language processing (NLP), different types of algorithms and analytical methods. An important phase of this process is the interpretation of the gathered information.