Code Unit Generator for Multimodal Q&A Retrieval
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Solution Overview
Problem
Existing project management and document management systems lack the capacity to autonomously comprehend and navigate structured software environments without extensive manual guidance, hindering the seamless integration and efficient performance of artificial intelligence tools.
Innovation Solution
A Q&A assistant that utilizes a block data model to automatically search, retrieve, and synthesize multimodal content, including text, images, audio, and video, within a unified search framework, leveraging AI-generated queries and neural networks to enhance data organization and privacy while providing automatic generative capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI tools are integrated into project management and document management systems, then automation capability and productivity are improved, but the complexity of system integration and navigation increases
Solution Approach 1:
The patent introduces a code unit generator as an intermediary component that translates natural language questions into executable code units. This mediator handles the complexity of system navigation and data retrieval, allowing AI tools to interact with project management systems through simple language rather than complex integration protocols, thereby improving automation capability without increasing user-facing system complexity
Solution Approach 2:
The code unit generator enables AI tools to autonomously comprehend and navigate structured software environments by automatically generating the necessary code units for data retrieval and processing. This self-service mechanism eliminates the need for extensive manual guidance or configuration, allowing AI tools to independently integrate and operate within project management systems
2Reliability
If manual guidance is provided to AI tools for navigating software environments, then integration reliability is improved, but the amount of time and effort required increases
Solution Approach 1:
The system performs preliminary action by pre-defining code units that represent common operations in project management systems. These pre-configured code units are stored in a database and can be automatically selected and executed based on natural language questions, eliminating the need for manual guidance during runtime while maintaining integration reliability through proven, pre-tested operation sequences
Solution Approach 2:
The code unit generator enables AI tools to autonomously generate and execute code units without requiring manual configuration or guidance. The system self-serves by automatically translating questions into executable operations, retrieving relevant data, and generating answers, thereby maintaining reliable integration while eliminating time-consuming manual intervention
3Ease of operation
If extensive manual guidance is required for AI tool operation, then ease of operation is maintained, but the extent of automation decreases
Solution Approach 1:
The code unit generator serves as an intelligent intermediary that translates natural language questions into executable code units, enabling AI tools to operate autonomously while maintaining ease of use. Users interact through simple language rather than complex commands, and the intermediary handles the automation of code generation and execution, thereby achieving both ease of operation and high extent of automation simultaneously
Data Source
AI summary
A multimodal content management system having a block-based data structure can include an artificial intelligence (AI)-based code unit generator that can generate code units executable against the block-based data structure to provide information requested by users. For example, the code units can be generated in response to natural language prompts received via a question and answer Q&A assistant engine. A neural network can be trained on block types, block dependencies, block content values, block content types, and/or block format. The neural network can receive a set of tokens generated based on a natural language prompt and generate one or more query strings to be included in a particular code unit. The tokens can be indicative of block properties, content, or other items in the block-based data structure. The code unit can be structured to execute more than one query against the block-based data structure such that a particular result set can include content items of different modalities.