Corpus-Scoped Annotation Analysis via Relational Database

The patent segments the annotation data into two distinct storage components: a traditional document-centric storage system for individual document annotations, and a new corpus-scoped storage system using relational databases for cross-document analysis. This segmentation allows each system to specialize - the document-centric system maintains simplicity while the corpus-scoped system enables efficient trend analysis across the entire corpus.

The patent adds a new dimension to data storage by introducing corpus-scoped metadata that transcends individual document boundaries. Instead of only storing annotations within their parent documents, the system creates a second dimension of organization where annotations can be queried and analyzed across the entire corpus, enabling identification of relationships and trends that span multiple documents.

The patent extracts essential annotation information from the full document annotations and stores it in a separate corpus-scoped metadata storage system. This extraction process selects only the most relevant features and characteristics of annotations that are useful for corpus-wide analysis, storing them in a normalized format that enables efficient querying while avoiding the need to store complete annotation details for every document.

The patent performs preliminary processing of annotation data during the annotation creation phase, pre-computing and storing corpus-scoped metadata that will be useful for future queries. This preliminary action includes normalizing annotation formats, extracting key features, and organizing data in a manner optimized for corpus-scoped analysis, so that when queries are executed, the system can quickly retrieve and analyze the pre-processed information without performing complex computations in real-time.