Advanced IP Report Generation

Legal document assembly and sharing system using text scripts to improve efficiency, accuracy, collaboration, and security in creating, editing, reviewing, and sharing legal documents. The system allows quick document creation using templates with placeholders that are dynamically filled with variables. It also provides features for data input, variable management, document preview, storage, collaboration, reporting, and user management.

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AI-powered system for generating legal documents using natural language processing (NLP) and chatbots to streamline document creation, customization, and compliance for legal professionals. The system has a chatbot interface that analyzes user input, an algorithmic processing module to generate personalized documents in text and image formats, a database of templates and clauses, a compliance module to flag legal issues, version control, and customization options.

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Automated authoring system for scientific documents like clinical study reports using AI and NLP to significantly reduce manual effort and time in creating these lengthy documents. The system configures a template based on requirements, extracts content from source documents, maps sections, and generates the document by rendering content into predicted sections. It also converts tense, summarizes tables, and interprets in-text tables.

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Efficiently generating and executing documents in workflows using templates that can be customized based on context. The system stores templates for documents and components. It selects the appropriate version of a template based on factors like workflow steps and participant organizations. It uses machine learning to predict variable values in templates based on workflow context. This allows instantiation of tailored documents without manual template creation.

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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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Automatically generating document templates for a text management system using big data and text recognition. The method involves creating documents and attributes, retrieving sample documents based on attributes, recognizing text, generating labeling rules, labeling samples, importing labeled samples, structuring, parsing rules, and generating templates. It leverages techniques like OCR, scene rule matching, and table parsing to extract and classify text from sample documents for template generation.

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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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A method and system for processing technical documents that improves efficiency, consistency, and accuracy by providing tools and templates to standardize technical document preparation. The method involves displaying technical document solutions and templates, reviewing electronic documents, retrieving related content, managing data modules, and integrating tools for verification and translation. It aims to software-ify standards, knowledge, and skills related to technical document preparation to reduce requirements for document writers. The system includes features like data guidance, automatic extraction, terminology management, and interface to third-party software.

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A document management system for generating, querying, validating, and customizing electronic agreement documents using document models. The system allows defining customized document structures and requirements for agreement types. It generates documents based on these models, queries documents using model attributes, validates documents against requirements, and lets users customize model objects. This improves efficiency by reducing processing time and resources for document search, validation, and editing compared to working with unstructured text documents.

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A system for generating decentralized applications, such as smart contracts and legal documents, using decentralized identity and data aggregation to improve efficiency and reduce costs in transactions involving assets, services, and contracts. The system employs decentralized identifiers (DIDs) derived from asymmetric key cryptography to identify parties and assets, enabling peer-to-peer transactions and client-side data aggregation of legal, financial, and accounting information. Legal documents are generated using a hybrid approach that combines structured data models with natural language legal prose. The structured data is aggregated from client-side data associated with the decentralized identifiers, ensuring accurate and efficient document creation.

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A data-driven method for generating legal documents that overcomes the inconsistent input data format, variable judgment standards, and high customization costs of traditional intelligent document generation systems. The method involves extracting data from sources, analyzing templates with rules, batch generating documents, online review, and electronic filing. It allows consistent input by extracting data instead of manually entering. The templates are customized through rules instead of custom code. This enables consistent generation across diverse cases and judges. The online review allows checking before finalizing. The electronic filing enables digitized storage and retrieval.

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Online editable document generation system and method to improve efficiency of creating legal documents. The system uses dynamic field labeling and browser-based document editing. It generates document templates with customizable fields. Users can fill out the fields to generate documents quickly without manual formatting. The system converts and parses files, checks document integrity, manages templates, and provides a document editing interface. It saves time and resources compared to starting from blank templates.

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Automated generation of property dispute documents using a template-based approach that simplifies configuration, improves efficiency, reusability, and ease of use compared to manual template generation. The method involves defining a corpus of historical documents, extracting paragraphs, and generating new documents by substituting relevant data into the template paragraphs. This allows reusable paragraph templates to be built from the corpus instead of reinventing the wheel for each document. The paragraphs can also have custom styles and formats.

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Method, system, medium and equipment for efficiently creating legal documents like mediation records in a smart judicial system. It involves uploading template documents with fillable elements, analyzing the elements and text structure, then automatically backfilling system data into the template to generate completed documents. This saves time compared to manually filling in forms and documents.

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A method for automatically extracting patent document templates from a patent corpus. This involves identifying sets of similar patent documents and extracting the common text shared between them. This approach enables the creation of standardized document templates by analyzing and finding the shared elements across a collection of similar patents.

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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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Method, device, and system for generating documents with flexible formats and scalability without needing custom development or online design engines. The method involves receiving structured external data with item IDs and a template ID, extracting the template and parsing the item IDs to process the external data, then filling it into the template to generate the target document. This separates document format, data, and program logic. The description rule converts data rather than modifying system code. The device supports various document formats and formats can change without rewriting code.

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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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A method for creating a patent document summary using natural language processing and machine learning techniques. This method analyzes the text of a patent document to generate a concise summary that captures key information. It involves identifying repetitive word sequences in the document, replacing subsequent occurrences of those sequences with shorter substitutes, and generating the summary based on the modified text.

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A method for managing the authoring of electronic documents to enable effective monitoring, analysis, and verification. This method involves collecting designated sections of authored content and converting them, along with their correlations and relational attributes, into a structured dataset called RD (relation data). This allows tracking changes, analyzing relationships, and verifying consistency between content sections. The RD is stored in a relational database, while the original electronic document is stored separately.

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