3D Object Tracking for Voice and Gesture Entity Resolution
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Solution Overview
Problem
Current speech recognition systems face challenges in accurately interpreting user commands that involve physical objects or locations, as they struggle to dynamically update object information and resolve entities in complex environments, leading to inefficiencies in processing voice commands and gestures.
Innovation Solution
The system employs dynamic entity resolution using image data to build an environment map, track objects in 3D coordinates, and update their positions, allowing it to interpret voice commands and gestures by identifying objects through object detection and clustering bounding boxes into 3D entities, enabling accurate navigation and response to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition systems use traditional methods to interpret user commands, then processing is simpler and faster, but accuracy in identifying physical objects and locations deteriorates
Solution Approach 1:
The patent combines speech recognition with computer vision by merging audio data processing with image data processing. The system integrates object detection models and bounding box clustering algorithms with speech interpretation, creating a unified system that processes both modalities together to accurately identify physical objects and locations mentioned in user commands.
Solution Approach 2:
The patent introduces an intermediary entity resolution system that acts as a bridge between speech recognition and object identification. This intermediary layer processes speech commands, cross-references them with detected objects in the environment, and resolves ambiguities by matching verbal references to specific physical entities, thereby improving accuracy without requiring complete system redesign.
2Measurement precision
If the system dynamically updates object information using image data, then object tracking accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary object detection and environment mapping before processing user commands. By pre-processing image data to identify and track objects in advance, the system creates an ready-to-use object database that can be quickly queried during command interpretation, reducing real-time processing delays while maintaining high tracking accuracy.
Solution Approach 2:
The patent implements dynamic entity resolution that adapts processing intensity based on system state. The object tracking updates occur at variable rates, intensifying when objects are in motion or when commands require precise location data, and reducing intensity during static periods, thereby optimizing the balance between accuracy and processing time.
3Adaptability or versatility
If the system clusters bounding boxes into 3D entities, then spatial understanding improves, but computational complexity increases
Solution Approach 1:
The patent transforms 2D bounding box data into 3D spatial entities by adding depth information and spatial relationships. This dimensionality transformation enables the system to understand object locations, distances, and spatial configurations, allowing for more accurate interpretation of commands involving physical space while using efficient algorithms to manage the computational load.
4Measurement precision
If the system uses multiple data sources for entity resolution, then interpretation accuracy improves, but data processing burden increases
Solution Approach 1:
The patent creates a universal entity resolution framework that handles multiple data types (speech, image, object detection) through a single integrated system. This multi-functional approach allows the same processing architecture to handle different data sources efficiently, reducing redundant computations and energy consumption compared to separate specialized systems for each data type.
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.