Multimodal AI Intent Inference for Adaptive Construction Planning
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Solution Overview
Problem
Current AEC software solutions are unable to adapt or make real-time decisions to dynamic construction project factors, failing to comprehend user intent and provide meaningful insights or actionable guidance due to reliance on manual and rule-based approaches and limited input types, especially when faced with natural language inputs.
Innovation Solution
An AI-based system that analyzes user intent through multiple input streams, including text, image, video, and audio, determines project objectives, and generates system optimization recommendations using ensemble learning and machine executable instructions to simulate construction scenarios in virtual or augmented reality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AEC software uses manual and rule-based approaches with limited input types, then the system structure remains simple and manageable, but the system cannot comprehend user intent or provide meaningful insights in real-time
Solution Approach 1:
The patent introduces an AI-based intermediary system that sits between the user inputs (natural language, images, videos, audio) and the construction planning processes. This intermediary uses natural language processing, computer vision, and speech recognition to translate diverse inputs into structured data, then applies machine learning models to generate optimization recommendations. This mediator layer enables the system to comprehend user intent without requiring direct integration of complex processing capabilities into the core AEC software.
Solution Approach 2:
The patent replaces manual and rule-based mechanical processing approaches with AI-based cognitive systems. Instead of relying on predefined rules and manual data entry, the system uses natural language processing to understand user queries, computer vision to analyze construction site images and videos, and speech recognition to process audio inputs. These AI systems automatically generate optimization recommendations by learning from historical data and real-time ecosystem influencers, substituting mechanical processing with intelligent decision-making capabilities.
2Productivity
If AEC software processes multiple input streams and generates real-time optimization recommendations, then the system provides actionable guidance and adaptive solutions, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing ecosystem influencer data (weather patterns, traffic conditions, regulatory requirements, material availability) in structured formats before they are needed for decision-making. The system pre-trains machine learning models on historical construction data to enable faster inference. When users provide natural language inputs or upload media files, the system can quickly retrieve relevant pre-processed data and generate optimization recommendations without performing heavy computations in real-time, thus reducing processing time while maintaining comprehensive analysis.
3Adaptability or versatility
If AEC software interfaces with external natural language processors and parsers, then the system can handle diverse user inputs, but the interface complexity and integration costs increase
Solution Approach 1:
The patent merges multiple external processing capabilities (natural language processing, computer vision, speech recognition) into a unified AI-based interface layer. Instead of maintaining separate interfaces for each input type, the system combines these capabilities into integrated models that can simultaneously process text, images, videos, and audio inputs. This unified interface uses a common architecture with shared components for data normalization, feature extraction, and recommendation generation, reducing the overall interface complexity while maintaining the ability to handle diverse user inputs effectively.
Data Source
AI summary
A method for analyzing an intent of a user in a computing environment is described. The method includes receiving an input from a user for executing at least one intended task by the user, analyzing the received input based on one or more ecosystem influencers, determining an intent of the user based on the analyzed input and one or more project objectives associated with the at least one intended task, determining system optimization recommendations based on the one or more project objectives, and generating a feature set by correlating the intent of the user and the determined system optimization recommendations.