Multimodal Context Embeddings for Real-World Semantic Interpretation
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Solution Overview
Problem
Conventional content delivery systems lack the ability to understand and leverage the deeper context of a user's real-world environment, limiting their effectiveness in providing relevant content.
Innovation Solution
Utilizing machine-learning techniques to convert multi-modal sensory input into a standardized embedding, enabling the determination of a context for the real-world environment and delivering semantically relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional content delivery systems use online context from displayed content, then content delivery can be implemented, but the system cannot understand the user's real-world environment context
Solution Approach 1:
The patent combines multiple data modalities (images, audio, text, location, device sensors) into a unified multimodal context representation. This merging allows the system to capture both online content context and offline real-world environment context, resolving the contradiction by integrating previously separate information sources into a comprehensive context model that enables both content delivery and real-world understanding.
Solution Approach 2:
The patent introduces multimodal embeddings as an intermediary representation that bridges the gap between raw multimodal sensory input and semantic context understanding. These embeddings serve as a mediator that transforms diverse real-world data (camera feeds, microphone input, sensor readings) into a standardized format that can be processed to derive meaningful environmental context, enabling the system to understand real-world settings without losing information.
2Adaptability or versatility
If location-aware applications provide direction-based recommendations, then real-world data can be leveraged, but the understanding of real-world context remains narrow
Solution Approach 1:
The patent transitions from unimodal location data to multimodal sensory input by adding new dimensions of data collection. Instead of relying solely on GPS coordinates and direction data, the system incorporates camera images, audio recordings, text inputs, and device sensor data. This dimensional expansion allows the system to understand deeper contextual information about the real-world environment, such as recognizing objects in the environment, detecting ambient sounds, and understanding user activities, thereby overcoming the narrow context understanding of traditional location-aware applications.
3Loss of information
If multiple modalities of sensory input are collected, then real-world context understanding is enhanced, but data processing complexity increases
Solution Approach 1:
The patent transforms the parameter representation of multimodal data by converting diverse input types (images, audio, text, sensor readings) into a unified embedding space. This parameter transformation allows the system to maintain complete context information from multiple modalities while simplifying processing through standardized vector representations. The embedding approach changes the data format from heterogeneous raw inputs to homogeneous numerical vectors, reducing processing complexity while preserving information completeness.
Data Source
AI summary
A computer-implemented method for semantically interpreting a real-world environment may include: receiving, via a user device, multimodal input that includes a plurality of modalities of data regarding an environment associated with a user of the user device; standardizing the plurality of modalities into a uniform data format to generate uniform multimodal context data; generating an embedding of the uniform multimodal context data; and determining a content entry predicted to be relevant to the environment associated with the user based on the generated embedding.