AI Document Review Annotation System for Meeting Feedback
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Solution Overview
Problem
Current methods face challenges in accurately capturing and consolidating content suggestions from document review meetings, as they often rely on manual note-taking and struggle to integrate feedback from various sources, such as physical notes, electronic documents, and media recordings, into the original document content.
Innovation Solution
An apparatus comprising processors and memory that identifies and generates annotations for content suggestions from multiple sources, including physical and electronic documents, and media content, associating these suggestions with their corresponding document portions and displaying them electronically, thereby facilitating efficient integration and review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual note-taking methods are used to capture content suggestions during document review meetings, then participants can provide feedback on physical and electronic documents, but the accuracy and completeness of capturing all comments and suggestions deteriorates due to human error and inconsistency
Solution Approach 1:
The patent replaces the mechanical manual note-taking process with an automated optical character recognition (OCR) system. The OCR technology scans physical documents and handwritten notes, automatically converting them into digital text that can be integrated with electronic document feedback, thereby eliminating human error and inconsistency in capturing comments.
Solution Approach 2:
The patent creates digital copies of physical documents and handwritten notes through scanning and OCR technology. These copies are then processed and integrated with electronic document feedback, allowing all comments to be captured accurately without requiring manual transcription while preserving the original information from multiple sources.
2Loss of information
If multiple sources of feedback (physical notes, electronic documents, media recordings) are integrated manually into the original document, then comprehensive review coverage is achieved, but the time and complexity required to consolidate all comments deteriorates
Solution Approach 1:
The patent merges multiple feedback sources (physical notes, electronic documents, media recordings) into a single integrated digital platform. The system automatically processes and combines comments from all sources, correlating them with specific document portions, which eliminates the time-consuming manual consolidation process while ensuring comprehensive feedback integration.
Solution Approach 2:
The patent performs preliminary processing of feedback from multiple sources before integration. The system pre-processes physical notes through OCR, transcribes media recordings, and organizes electronic comments in advance, so that when consolidation is needed, the work is already substantially complete, significantly reducing the time required for final integration.
3Productivity
If automated systems are used to process and integrate feedback from multiple sources, then the efficiency of consolidating comments improves, but the complexity of the system increases
Solution Approach 1:
The patent implements a universal platform that handles multiple feedback sources (physical documents, electronic files, media recordings) through a single integrated system. This multi-functional approach consolidates what would otherwise require separate processing tools into one system, improving efficiency without proportionally increasing complexity, as the same core technologies (OCR, speech-to-text, document parsing) serve multiple purposes.
4Ease of operation
If all feedback is captured and displayed in electronic form, then the accessibility and organization of comments improve, but the difficulty of correlating external feedback with original document content increases
Solution Approach 1:
The patent uses an intermediary correlation system that automatically links feedback from external sources to specific portions of the original document. The system employs technologies such as OCR for physical documents, speech-to-text conversion for media recordings, and metadata analysis for electronic files to create automatic correlations between feedback and document content, making the matching process transparent and accurate without manual intervention.
Data Source
AI summary
Artificial intelligence is introduced into document review to identify content suggestions from input to generate suggested annotations for the reviewed document. An approach is provided for receiving an electronic document that contains original content from an original electronic document for review and electronic mark-ups provided by a first user. One or more electronic mark-ups that represent content suggestions proposed by the first user are identified from the electronic document. For each electronic mark-up of the one or more electronic mark-ups identified a document portion of the original content that corresponds to the electronic mark-up is identified, and an annotation is generated for the electronic mark-up comprising the electronic mark-up and a first user ID for the first user and associating the annotation to the document portion identified. The original content with one or more annotations generated from the one or more electronic mark-ups is displayed, in electronic form, within a display window.