Machine Learning Model for Task-Relevant Content Identification
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Solution Overview
Problem
Digital assistants face challenges in identifying relevant content from large documents, often producing false positives due to the complexity of natural language inputs, which increases processing costs and inefficiencies in tasks like scheduling meetings.
Innovation Solution
A machine learning model with multiple layers is used to process natural language inputs, including an embedding layer, distinct sentence aggregation layer, contextual aggregation layer, and scoring layer, to accurately identify and rank relevant sentences based on their relevance to specific task types, reducing false positives and enhancing task completion efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital assistants analyze large documents to identify relevant content, then task completion capability is improved, but false positives increase and processing costs rise
Solution Approach 1:
The patent segments the analysis process into multiple sequential layers: embedding layer for word representations, distinct sentence aggregation layer for sentence-level features, contextual aggregation layer for document-level context, and scoring layer for relevance ranking. This multi-layer segmentation allows progressive refinement of relevance assessment, reducing false positives while maintaining task completion capability.
Solution Approach 2:
The patent transforms the relevance assessment from a single-dimension binary classification to a multi-dimensional continuous scoring system. By generating contextual embeddings and applying attention mechanisms across multiple layers, the system evaluates relevance along multiple dimensions (semantic similarity, contextual relevance, task specificity), enabling more nuanced discrimination between true positives and false positives.
2Productivity
If digital assistants analyze large documents to identify relevant content, then task completion capability is improved, but processing costs increase
Solution Approach 1:
The patent segments the document processing into hierarchical layers that process progressively refined representations. The embedding layer processes individual words, the distinct sentence aggregation layer processes sentences independently, and the contextual aggregation layer processes the full document context. This segmentation allows efficient parallel processing at each layer while maintaining overall task completion capability.
Solution Approach 2:
The patent applies partial action by selectively processing only the most relevant portions of the document. The scoring layer identifies and ranks sentences by relevance, allowing the system to focus computational resources on high-scoring candidates rather than uniformly processing all content, thereby reducing overall processing costs while maintaining task completion effectiveness.
3Productivity
If simple relevance identification is used, then processing efficiency is improved, but accuracy in identifying relevant content deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-computing contextual embeddings for the entire document before sentence-level relevance scoring. The contextual aggregation layer generates a comprehensive contextual representation that captures document-level semantics, which is then used to guide the scoring of individual sentences. This preliminary contextualization improves accuracy without significantly increasing processing time during the scoring phase.
Solution Approach 2:
The patent introduces contextual embeddings as an intermediary representation between the raw document and the relevance scoring process. These embeddings serve as a mediator that captures complex contextual relationships, enabling the scoring layer to make more accurate relevance assessments without directly analyzing all raw text, thereby maintaining processing efficiency while improving accuracy.
Data Source
AI summary
In non-limiting examples of the present disclosure, systems, methods and devices for identifying relevant content in a natural language input are presented. An email may be received. A machine learning model may be applied to the email. The machine learning model may have been trained to rank sentences based on their relevance to a schedule meeting task. The machine learning model may comprise: an embedding layer for generating an embedding for each word in the email; a distinct sentence aggregation layer for aggregating the embeddings for each word in the email into a distinct embedding for each of the sentences in the email; a contextual aggregation layer for aggregating each distinct embedding for each of the sentences into a contextual embedding for each of the sentences; and a scoring layer for scoring and ranking each of the sentences based on their relevance to the schedule meeting task.