3D Object Tracking for Voice-Based Entity Resolution
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Solution Overview
Problem
Current speech recognition systems struggle to accurately interpret user commands that involve physical objects or locations, as they lack the ability to dynamically resolve entities in a user's environment.
Innovation Solution
The system employs image data and environment mapping to identify and track physical objects, using 3D coordinates and 2D models to recognize objects and interpret voice commands that reference them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition systems use traditional audio-only processing, then the system complexity remains low, but the system cannot accurately interpret commands involving physical objects or locations
Solution Approach 1:
The patent combines audio processing with image processing and environment mapping to create a multi-modal system. The speech recognition system merges audio data from microphones with visual data from cameras and spatial information from environment maps to accurately identify objects and locations referenced in user commands, thereby improving interpretation accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The patent introduces an environment map as an intermediary data structure that stores spatial relationships and object locations. This environment map acts as a mediator between the audio input and the interpretation process, allowing the system to resolve entity references in speech commands by querying the pre-built spatial model of the environment without requiring complex real-time sensing for every command.
2Measurement precision
If the system builds and maintains detailed environment maps with 3D coordinates, then entity resolution accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs environment mapping and object detection in advance, building a detailed 3D spatial model of the environment before speech commands are processed. By pre-processing the environment and storing object locations, spatial relationships, and environmental features in the environment map, the system eliminates the need for real-time environment scanning during command interpretation, thereby reducing processing time for entity resolution while maintaining high accuracy.
3Adaptability or versatility
If the system uses multiple sensors and processing modules, then the ability to resolve entities improves, but the device complexity increases
Solution Approach 1:
The patent designs the environment map and processing modules to serve multiple functions. The environment map not only stores object locations for entity resolution but also provides spatial context for gesture interpretation, navigation, and scene understanding. The same image processing pipeline supports both object detection and environment modeling, reducing the need for separate dedicated components and managing overall system complexity while enhancing versatility.
Data Source
AI summary
Described herein is a system for tracking objects and performing dynamic entity resolution using image data. For example, the system may build an environment map and populate the map with objects present in the environment. As the devices move about the environment it may capture image data and, based on its position and/or configuration of its components, may determine updated locations of objects that move in the environment. Upon receiving a query from a user, based on the location of the objects relative to the device/user, the system can interpret gestures and voice commands to infer which object is specified by the voice command. To build the environment map, the system performs object detection to generate bounding boxes associated with an object, then clusters the bounding boxes into a three-dimensional (3D) object associated with 3D coordinates. As the system tracks the object using the 3D coordinates while maintaining two-dimensional (2D) information (e.g., bounding boxes and other features), the system can use existing 2D models to process objects in 3D.