Ask Detection Model for Electronic Message Classification
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Solution Overview
Problem
Users face challenges in identifying messages that contain asks (requests) requiring a commit (action or pledge) within electronic communications, as existing methods rely heavily on manual labeling, which is costly and inefficient, and struggle to accurately distinguish between messages with and without commits.
Innovation Solution
The development of machine learning models, such as the original message ask model and reply commit model, that automatically identify messages likely to contain asks or commits by analyzing features of electronic communications, allowing for the generation of training examples without human intervention and improving the accuracy and coverage of message classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling methods are used to identify messages containing asks, then accuracy in distinguishing messages with and without commits can be achieved, but the process becomes costly and inefficient
Solution Approach 1:
The patent replaces manual labeling (mechanical process) with machine learning models that automatically classify messages. The ask detection model and commit detection model process electronic communications automatically, eliminating the need for human annotators while maintaining classification accuracy through trained neural networks.
Solution Approach 2:
The system performs self-service by automatically generating training data through iterative processes. The ask detection model generates candidate ask messages, which are then used to train the commit detection model, creating a self-sustaining system that improves without external human intervention.
2Productivity
If machine learning models are used to automatically identify messages with asks, then productivity and efficiency improve, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex task into separate specialized models: an ask detection model that identifies messages containing asks, and a commit detection model that identifies commitments in responses. This segmentation allows each model to be simpler and more specialized, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The patent introduces an intermediary training data generation process where the ask detection model serves as a mediator to create training examples for the commit detection model. This intermediary layer simplifies the system by allowing automatic training data generation without requiring manual annotation of complex communication patterns.
3Device complexity
If existing manual methods are used, then system complexity remains low, but the ability to accurately distinguish between messages with and without commits is limited
Solution Approach 1:
The patent replaces simple manual inspection with machine learning models that automatically detect asks and commits. The neural network models process linguistic patterns, context, and communication history to achieve high measurement precision in distinguishing message types, far exceeding human capability.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously improves its accuracy by using generated training data to refine the models. The iterative process where ask detection informs commit detection creates a feedback loop that progressively improves measurement precision without increasing operational complexity.
4Reliability
If manual labeling is performed, then training data quality can be controlled, but the time and resources required for data preparation increase significantly
Solution Approach 1:
The patent implements self-service data generation where the ask detection model automatically creates training examples for the commit detection model. The system generates its own training data by processing electronic communications and creating labeled examples through automated inference, eliminating the need for time-consuming manual annotation while maintaining data quality through model-based consistency.
Solution Approach 2:
The patent performs preliminary action by using the ask detection model to pre-identify and segment communications before training the commit detection model. This preliminary classification creates organized training data structures that reduce the overall time required for data preparation and model training.
Data Source
AI summary
Methods and apparatus related to generating an original message ask model that can be utilized to determine, based on an original message sent to a user, whether a commit is likely to be present in a yet to be formulated new reply message that is responsive to the original message. In some of those implementations, an indication may be provided for presentation to the user via a computing device of the user in response to determining that a commit is likely to be present in the yet to be formulated new reply message that is responsive to the original message sent to the user.