Intent-Based Task Embeddings From Weak Supervision
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Solution Overview
Problem
Existing task management systems struggle to understand and represent the meaning of tasks due to their under-specified nature, limiting their ability to reason about tasks and provide intelligent assistance across various applications.
Innovation Solution
A method and system for generating a general task embedding using an intent-based task representation model trained with weak supervision and multi-task learning, which encodes task data to predict task intents and attributes, leveraging auxiliary tasks and diverse data sources for semantic augmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If task management systems use simple text representation for tasks, then ease of operation is improved, but understanding and reasoning capability deteriorates
Solution Approach 1:
The patent introduces task embeddings as an intermediary representation that bridges simple user input and complex task understanding. The embedding model transforms brief task descriptions into dense vector representations that capture semantic meaning, enabling downstream applications to reason about tasks without requiring complex structured input from users.
Solution Approach 2:
The patent transforms task representations from sparse text to dense continuous vector space. By changing the parameter space from discrete tokens to continuous embeddings, the system preserves ease of text input while gaining rich semantic representation capabilities that enable reasoning and classification.
2Measurement precision
If task management systems apply application-specific task definitions, then measurement precision for specific tasks is improved, but adaptability to different applications deteriorates
Solution Approach 1:
The patent creates a universal task embedding model that can serve multiple downstream applications. The same embedding infrastructure supports various tasks including classification, similarity search, and reasoning across different domains, eliminating the need for application-specific representation models while maintaining high precision through task-specific fine-tuning.
3Ease of operation
If task data is kept minimal and under-specified, then ease of operation is improved, but productivity of intelligent assistance deteriorates
Solution Approach 1:
The patent performs preliminary semantic processing by generating task embeddings upfront from minimal user input. This preliminary action enriches the representation before it reaches downstream applications, enabling intelligent assistance features like prioritization and recommendation to operate effectively without requiring users to provide detailed specifications.
Data Source
AI summary
Systems and methods are described that are generally directed to generating a general task embedding representing task information. In examples, the generated task embedding may include predicted task information such that, rather being underspecified, the task embedding representative of the task may include additional specified information, where the task embedding can then be utilized in many different models and applications. Thus, task data may be received and at least a portion of the task data may be encoded using an encoder. Based on one or more outputs generated by the encoder and a type embedding associated with the task data, a task intent may be extracted or otherwise predicted based on the task data and one or more type encodings associated with the task data. The intent extractor may be trained on multiple auxiliary tasks with weak supervision that provide semantic augmentation to under-specified task texts.