Towards content-oriented patent document processing

TL;DR: This paper constructs dynamic patent maps to show technological competition trends and describes the strategic functions of the dynamic maps, which are based on subject-action-object (SAO) structures that are syntactically ordered sentences extracted using the natural language processing of the patent text.

TL;DR: The architecture of the proposed Subject-Action-Object (SAO)-based patent intelligence system is described in detail, and the system's functionalities are illustrated using case studies.

TL;DR: In this article, the concept of function is used to support quantitative analysis for developing a technology roadmap (TRM), which can provide information on the uses and purposes of a technology.

TL;DR: The proposed framework involves the application of document image pre-processing, image feature and textual metadata extraction in order to support effectively content-based image retrieval in the patent domain.

TL;DR: The strategy used to create the current version of the SUMO is outlined, some of the challenges that were faced in constructing the ontology are discussed, and its most general concepts and the relations between them are described.

TL;DR: This work presents an overview of Sesame, an architecture for efficient storage and expressive querying of large quantities of metadata in RDF and RDF Schema, and its implementation and the first experiences with this implementation.

TL;DR: In this work, the techniques developed for a functional analysis of patents and their implementation in the PAT-Analyzer tool are presented and the same technique has been properly tailored and applied to the analysis of software requirements documents.

TL;DR: Relational Models of the Lexicon not only provides an invaluable survey of research in relational semantics, but offers a stimulus for potential research advances in semantics, natural language processing and knowledge representation.