Block-Based Q&A Ranking Engine Using Authority Signals
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Solution Overview
Problem
Existing artificial intelligence tools lack the capacity to autonomously comprehend and navigate structured software environments within project management and document management systems without extensive manual guidance, hindering seamless task performance.
Innovation Solution
A machine learning-based question and answer (Q&A) assistant that utilizes a block data model to automatically search, retrieve, analyze, and synthesize multimodal content, including text, images, audio, and video, by training on block properties rather than content, enhancing predictive capabilities and data privacy while enabling automatic generation of responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If AI tools are integrated into project management and document management systems, then task automation capability is improved, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: a data processing module that handles document management operations, an AI model module that performs natural language processing, and a result output module that delivers answers. This segmentation allows each module to specialize in specific tasks, improving automation capability while managing overall system complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary layer between the document management system and the AI model. This intermediary processes user queries, retrieves relevant documents, and prepares data for the AI model, thereby bridging the gap between structured software environments and AI capabilities without requiring direct complex integration.
2Reliability
If AI tools navigate structured software environments with manual guidance, then operational reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The AI model is equipped with self-service capabilities to autonomously navigate the document management system. It can independently retrieve documents, extract relevant information, and generate answers without requiring manual guidance or intervention, thereby maintaining operational reliability while significantly improving ease of operation.
Solution Approach 2:
The system performs preliminary actions by pre-processing and indexing documents within the structured software environment before AI queries are submitted. This preliminary organization of data enables the AI model to efficiently locate and process relevant information autonomously, reducing the need for manual navigation guidance.
3Measurement precision
If AI models are trained on content data, then predictive capability is improved, but data privacy is compromised
Solution Approach 1:
The patent extracts only the essential structural and semantic features from document content for training the AI model, while excluding sensitive and private information. This extraction process allows the model to learn predictive patterns from document structures and metadata without compromising data privacy, achieving a balance between predictive capability and privacy protection.
Solution Approach 2:
Instead of training directly on original content data, the system creates anonymized copies or synthetic representations of documents for model training. These copies retain the structural and semantic characteristics needed for predictive capability while removing or obscuring sensitive information, thereby protecting data privacy during the training process.
Data Source
AI summary
A multimodal content management system having a block-based data structure can include a question and answer (Q&A) assistant (e.g., a chatbot). The system can receive a natural language prompt and generate a result set. The result set can include blocks (e.g., blocks that include responsive content, including content in different modalities). The system can apply a set of authority signals to items in the result set to generate a ranked result set. The authority signals can be generated using aspects of the block-based data structure, such as block properties. The system can cause the Q&A assistant to return a set of hyperlinks to the ranked result set items. The hyperlinks can be operable to enable navigation to block content without closing the Q&A assistant.