公开(公告)号：US20110137923A1

公开(公告)日：2011-06-09

申请号：US12634635

申请日：2009-12-09

申请人： VLADIMIR KOROTEYEV ,  MAKSIM KOROTEYEV

发明人： VLADIMIR KOROTEYEV ,  MAKSIM KOROTEYEV

IPC分类号： G06F17/30

CPC分类号： G06F17/2705 ,  G06F17/2247 ,  G06F17/241

摘要： A method and computer program for automatic mapping of Extensible Business Reports Language (XBRL) Data to corresponding locations in an initial business document. The program takes XBRL filing, together with text of the initial report, and starts a data mapping engine based on Evolutionary Optimization. The engine searches for the most plausible locations in the document for every data item. After the data locations have been identified, the program tags them in the document and creates visualization forms so a user could easily see and verify correspondence between 2 formats of the same data: saved in XBRL filing and presented in the document.

摘要翻译： 一种用于将可扩展业务报告语言（XBRL）数据自动映射到初始业务文档中相应位置的方法和计算机程序。 该程序与XBRL文件一起提交初始报告文本，并启动基于Evolutionary Optimization的数据映射引擎。 引擎为每个数据项搜索文档中最合理的位置。 在数据位置被识别之后，程序将它们标记在文档中并创建可视化表单，以便用户可以轻松地查看和验证相同数据的两种格式之间的对应关系：保存在XBRL文件中并呈现在文档中。

公开(公告)号：US20110231384A1

公开(公告)日：2011-09-22

申请号：US12634627

申请日：2009-12-09

申请人： MAKSIM KOROTEYEV ,  VLADIMIR KOROTEYEV ,  OLEKSANDR PASICHNYK

发明人： MAKSIM KOROTEYEV ,  VLADIMIR KOROTEYEV ,  OLEKSANDR PASICHNYK

IPC分类号： G06F17/30 ,  G06N3/12 ,  G06F15/18

CPC分类号： G06F16/367

摘要： The invention is a process, system, workflow system for data retrieval processes, software, Web Site, service and SaaS (Software as a Service) created to support a data retrieval process from various document types to custom or preset retrieval data structures. The program supports manual, automatic and semiautomatic data retrieval using its internal features or external add-ons. It links data points in the structure to the corresponding data points in the document, stores documents, structures and links between them and outputs results in various formats. Links between a document and a retrieval data structure are established either automatically or manually by the user. After all required links are set, results can be retrieved from the program as an XML (Extensible Markup Language) structure with required data or as a PDF (Portable Document Format) or HTML (Hypertext Format Language), in MS Office formats and others containing a/the retrieval data structure, the original document or both with links between corresponding data points.The system incorporates a Text Mining engine, which provides automatic information retrieval capabilities. The engine implements Text mining technology that is based on Evolutionary Bayesian Ontology Classification. This technology uses Bayesian Ontology for modeling the problem's domain and applies Evolutionary Search for the most plausible classification decision.The ability to learn from data is a key feature of Bayesian Ontology, and for our embodiment. The complexity and size of semantic and format dependencies between elements in a natural language text is too high for analytical descriptions. Plus, we intend to save the user the trouble of building their own data retrieval models. Instead, we rely on an algorithm that automatically links user's data selections to the closest categories in pre-built ontologies and generates selection specific classifiers. Every individual ontology keeps learning from user corrections during its life cycle. The system is specifically built with the ability to accumulate data models learned from various types of documents. The more documents have been processed by the system, the higher generalization capabilities it possesses for automatic processing of new, unseen documents.