Context-Aware Text Suggestions Using Environmental Image Sensors
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Solution Overview
Problem
Existing text suggestion systems do not incorporate environmental context, leading to irrelevant suggestions when a user is entering text, as they rely solely on previously entered text without considering the user's surroundings.
Innovation Solution
A method and system that utilize an image sensor to obtain images of the physical environment, determine semantic labels associated with that environment, and generate text suggestions based on both the entered text and these labels, thereby providing context-aware suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If text suggestion systems rely solely on previously entered text, then the system complexity is low, but the relevance of suggestions to user needs deteriorates
Solution Approach 1:
The system segments the context information into multiple independent components: image data from image sensors, audio data from microphones, location data from GPS, and previously entered text. Each component is processed separately to extract semantic labels, and these segmented components are then integrated to generate text suggestions. This segmentation allows the system to manage complexity by handling discrete data types independently while achieving comprehensive context awareness.
Solution Approach 2:
The patent introduces semantic labels as an intermediary layer between raw environmental data (images, audio, location) and text suggestion generation. The semantic labels serve as a mediator that translates diverse environmental contexts into meaningful categories that can be effectively combined with entered text to produce relevant suggestions, without requiring direct complex integration of all raw data streams.
2Reliability
If the system incorporates environmental context through image sensors and semantic labels, then the relevance of text suggestions is improved, but the device complexity increases
Solution Approach 1:
The system employs a multi-functional device architecture where a single computing device integrates multiple sensor functions (image sensing, audio sensing, location tracking) and processing capabilities (semantic label extraction, text analysis, suggestion generation). This universal device approach consolidates what could be separate complex systems into one integrated platform, reducing overall system complexity while maintaining comprehensive environmental context awareness.
Solution Approach 2:
The system performs preliminary processing of environmental data by extracting semantic labels from images, audio, and location information before integrating them with entered text. This preliminary action prepares the raw environmental data in advance, transforming it into structured semantic representations that are easier to combine with text data, thereby reducing the complexity of the final suggestion generation process.
Data Source
AI summary
In one implementation, a method of displaying text suggestions is performed at a device including an input device, a display, an image sensor, one or more processors, and non-transitory memory. The method includes obtaining, using the image sensor, one or more images of a physical environment. The method includes obtaining one or more semantic labels associated with the physical environment based on the one or more images of the physical environment. The method includes receiving, via the input device, text. The method includes determining one or more text suggestions based on the one or more semantic labels associated with the physical environment and the text. The method includes displaying, on the display, the one or more text suggestions.