Action Fulfillment via Classification Valency
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Solution Overview
Problem
Current action fulfillment methods rely on single text classification, often resulting in under-training of classifiers due to insufficient text input, leading to poor precision and recall, and ultimately customer dissatisfaction.
Innovation Solution
A system that analyzes both text and additional data types, such as media, to derive a classification model, which is then used to determine additional inferences based on classification valency, enhancing action fulfillment by incorporating weighted contributions from various data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If single text classification is used for action fulfillment, then the system is simple to implement, but the precision and recall of classification results deteriorate due to insufficient training data
Solution Approach 1:
The patent combines multiple data sources including text messages, media content, user profiles, and interaction histories into a unified classification framework. This merging of diverse data types enriches the training dataset, enabling more accurate classification models without significantly increasing implementation complexity
Solution Approach 2:
The system creates a multi-functional classification approach where a single classification model can process various data types (text, media, user data) and serve multiple purposes including action fulfillment, user understanding, and context analysis, thereby improving precision without requiring separate specialized systems
2Productivity
If only text data is used for classification, then the data processing is fast and efficient, but the recall of action fulfillment deteriorates due to limited information
Solution Approach 1:
The system performs preliminary analysis and preprocessing of multiple data types including media content and user profiles before the main classification task. This preliminary action prepares enriched feature sets that improve recall without significantly impacting processing speed during actual action fulfillment
Solution Approach 2:
The patent introduces intermediary processing layers that aggregate and synthesize information from multiple sources (text, media, user data) into consolidated feature representations. These intermediaries enable efficient processing by pre-processing complex data relationships before final classification, maintaining speed while improving recall
3Loss of time
If classifiers are trained with limited text input, then the training process is quick and resource-efficient, but the classification results suffer from poor precision and recall
Solution Approach 1:
The patent merges multiple data sources including media content, user profiles, and interaction histories with text data to create a comprehensive training dataset. This combination provides richer features for classifier training, improving precision and recall without requiring excessively long training times
Solution Approach 2:
The system dynamically adjusts training parameters and data sampling strategies based on the available data types and quality. By changing parameters such as learning rates, batch sizes, and data weighting, the system achieves optimal classification performance with efficient training time
Data Source
AI summary
A method, system and computer program product for providing enhanced action fulfillment using classification valency. At least a first message from a user in a channel is analyzed, the first message containing first data of a first data type, and a classification of the first message is inferred based on the first data's content. At least a second message from the user in the channel is analyzed, the second message containing second data of a second data type different from the first data type, and a classification model of the second message is derived based on the second data's content. The classification of the first message and the classification model of the second message are used to determine whether an additional inference is available based on the classification valency. The additional inference is incorporated to determine the enhanced action fulfillment, and the enhanced action is presented to the user for fulfillment.