Advanced IP Report Generation

Automatically generating structured documents by leveraging a domain model and templates to simplify document creation and reduce errors. The method involves establishing a domain model with entry tags and structure tags representing the data and relationships in a specific domain. Templates are created based on the domain model to define the document structure. When generating a document, the method parses the template using the domain model to replace placeholders with the appropriate data from the input document. This allows generating customized documents with consistent structure and formatting without manual layout or formatting.

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Automated generation of technical documents for business applications to improve efficiency and consistency. The method involves extracting materials like product info, tasks, interfaces from applications, obtaining configuration info, templates, and determining relationships between extracted items and template sections. It fills the extracted materials into the template sections to generate the technical document. This leverages existing materials instead of manual authoring to speed up document creation.

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Database-based document generation system that automates the creation of multiple similar object description documents by capturing attributes, abstracting concepts, storing in a database, and generating documents using templates. The system analyzes required attributes from multiple dimensions, abstracts concepts, stores in a database, and generates documents using templates. This ensures completeness, accuracy, and standardization of object descriptions across multiple documents.

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Automatically generating templated documents from SysML models to simplify and expedite report generation in model-based systems engineering (MBSE). The method involves defining document templates based on SysML metamodels and then generating documents by selecting the templates, models, and output format. The templates have node trees with associations to metamodel paths. The templates are processed to extract model data and generate documents using a template engine.

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Automatic generation of technical documents using natural language processing and knowledge graphs to improve efficiency and consistency of document creation. The system involves a three-part architecture: 1) BOE (Bid, Offer, Execution) subsystem with NLP and knowledge graph for extracting data from documents; 2) SOW (Statement of Work) subsystem for generating documents based on user selections and BOE data; 3) permissions subsystem for access levels. The NLP extracts labeled data from BOE docs into a knowledge graph. The SOW subsystem uses user inputs and BOE data to auto-generate new SOWs.

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Electronic document generation system that allows repeating sections based on related entities in a database, following relationships to generate sections, and nesting relationships to arbitrary depth. The system takes an electronic document template and inserts section identifiers associated with contextual relationships in an entity-relationship model. When generating the document, it detects the section identifiers and generates the document by identifying entities, fields, and data from the related entities. This allows dynamic section generation based on related data in the database.

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Analyzing research literature to provide insights and reports for strategic decision making. It involves indexing patent and research documents, determining topics, finding common phrases, quantifying topic overlap between patents and research, predicting patent classes, and generating customized reports for users based on their roles. The reports help identify emerging research, measure commercialization, and predict exploitable areas.

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