Multi-Modal Embeddings for Real-Time User Intent Disambiguation
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Solution Overview
Problem
Computing devices struggle to accurately determine user intent due to colloquialisms, homonyms, polysemes, local dialects, idiomatic expressions, and slang, as well as difficulties in correlating synonymous interrelated subjects, leading to confusion in semantic analysis of user-inputted text.
Innovation Solution
Employing multi-modal embeddings using time-sequenced historic user data, generated through modal-specific encoders and a multi-modal embeddings generator, to map user interactions into a shared vector space, enabling accurate real-time user intent prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional semantic analysis is used to interpret user input, then the system can process text quickly, but it fails to accurately determine user intent due to colloquialisms, homonyms, polysemes, and dialects
Solution Approach 1:
The system performs preliminary actions by collecting and storing user interaction history, profile information, and contextual data before analyzing new user input. This pre-prepared data is then used to disambiguate colloquialisms, homonyms, and polysemes in real-time, improving intent determination accuracy without adding complexity to the core analysis engine
Solution Approach 2:
The patent introduces an intermediary layer that bridges traditional semantic analysis and user intent determination. This intermediary uses multi-modal embeddings to translate various data types (text, audio, profile information) into a unified representation space, enabling accurate intent prediction while keeping the system architecture modular and manageable
2Reliability
If computing devices use standard text analysis methods, then processing speed is maintained, but the ability to correlate synonymous interrelated subjects is lost
Solution Approach 1:
The system merges multiple data sources including user interaction history, profile information, and contextual data into a unified multi-modal embedding representation. This consolidation enables the system to correlate synonymous interrelated subjects by finding their relationships in the unified vector space, improving reliability without requiring separate processing pipelines that would increase time loss
Solution Approach 2:
The patent transforms various data types (text, audio, metadata) into a standardized vector representation format through multi-modal embeddings. This parameter transformation allows different data types to be processed uniformly and correlated efficiently, maintaining processing speed while enabling sophisticated subject correlation through vector similarity computations
Data Source
AI summary
Aspects described herein may improve predictions of real-time user intent by generating multi-modal embeddings based on previous user interactions. The previous user interaction data may be received from multiple different modalities. The interaction data may be analyzed and labeled with user intent for each previous user interaction, then sequentially organized. The multi-modal embeddings may be generated by modal-specific encoders and a multi-modal embeddings generator. A trained machine learning model may use, as input, the multi-modal embeddings and personal data to determine real-time user intent at the beginning of an interaction. The multi-modal embeddings may act as a set of unique and specific output vectors that allow a computing device to recognize and correlate synonymous terms and interrelated subjects, usually a difficult task for computing devices, thereby improving the technological process of analyzing, in real-time, user intent.