Communication Session Vector Encoding for Contextual Response Retrieval
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Solution Overview
Problem
Existing personal digital assistants struggle to identify contextually relevant responses when users refer to entities using different words, limiting their ability to mimic human conversation and provide natural interactions.
Innovation Solution
The system encodes communication sessions as vectors and identifies similar vectors within a vector space to retrieve relevant communications, allowing for contextually appropriate responses even when sessions share few words or entities in common, leveraging human-human communications to enhance personal assistant interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If personal digital assistants use traditional semantic matching to identify responses, then they can find responses with similar words and entities, but they fail to provide contextually relevant responses when users refer to entities using different words
Solution Approach 1:
The patent transforms communication sessions into vector representations, changing the parameter space from textual/semantic features to geometric vector space coordinates. This allows similarity measurement through vector distance metrics rather than traditional semantic matching, enabling the system to capture contextual relevance even when word usage differs between sessions
Solution Approach 2:
The patent introduces vector embeddings as an intermediary representation layer between the raw communication text and the similarity comparison process. This intermediary transformation enables the system to compare the intent and context of communications without being constrained by exact word matching, resolving the contradiction between adaptability to different wording and precision in identifying relevant responses
2Ease of operation
If personal digital assistants rely on exact word and entity matching, then they can ensure precise semantic similarity, but they cannot mimic human conversation when users use different words to refer to the same entities
Solution Approach 1:
By encoding communication sessions as vectors that capture contextual and semantic meaning beyond individual words, the system changes the matching parameters from surface-level text comparison to deeper contextual understanding. This enables more natural interactions while maintaining response relevance through vector-based similarity measurement in the embedded space
3Adaptability or versatility
If the system encodes communication sessions as vectors and searches within vector space, then it can retrieve relevant communications beyond semantic similarity, but it increases computational complexity for vector encoding and distance calculation
Solution Approach 1:
The patent creates vector copies or embeddings of communication sessions that can be stored and compared in vector space. Instead of processing full textual communications during similarity searches, the system uses these pre-computed vector representations, reducing the computational complexity of real-time comparisons while maintaining the ability to retrieve contextually relevant responses
Data Source
AI summary
Systems and methods are disclosed for response retrieval using communication session vectors. In one implementation, a first communication session is received. The first communication session includes a first communication. The first communication session is encoded as a first vector. A second vector is identified within a defined proximity of the first vector. The second vector represents a second communication session that includes a second communication. The second communication is provided within the first communication session in response to the first communication.