Personalized Response Prediction for Mobile Messaging
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Solution Overview
Problem
Portable electronic devices face challenges in message input due to their small form factors, which result in sub-optimal user interfaces for typing, leading to increased cognitive burden and inefficiency.
Innovation Solution
The development of techniques for predicting a user's likely response to an incoming message and presenting these predictions for user selection, reducing the need for manual input and enhancing the efficiency of the human-machine interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual typing is used on reduced-size keyboards, then message input can be achieved, but user cognitive burden increases and typing efficiency decreases
Solution Approach 1:
The system performs preliminary actions by analyzing conversation history, context attributes, and user behavior patterns beforehand to generate predicted response suggestions before the user needs to type. This allows the most likely responses to be prepared in advance, reducing the user's typing burden and cognitive load during actual message composition.
Solution Approach 2:
The system serves itself by automatically generating and updating personalized prediction models based on user interaction data without requiring manual configuration. The machine learning model continuously learns from user responses and conversation patterns, automatically improving prediction accuracy over time while reducing the need for user intervention in system setup and maintenance.
2Ease of operation
If more keyboard space is allocated for typing, then typing comfort improves, but device form factor increases
Solution Approach 1:
The patent replaces the mechanical typing system with an intelligent prediction system that uses machine learning algorithms and natural language processing. Instead of relying on physical keyboard space and manual typing mechanics, the system substitutes computational processes that analyze context and generate predictions, effectively eliminating the need for large keyboard layouts while maintaining or improving typing comfort.
3Measurement precision
If personalized prediction models are trained with extensive user data, then prediction accuracy improves, but processing power and time requirements increase
Solution Approach 1:
The system applies local quality by training and maintaining specialized prediction models for different contexts such as different conversation partners, message types, and communication scenarios. Instead of using a single monolithic model that processes all data uniformly, the system creates context-specific models that focus computational resources on relevant patterns, improving prediction accuracy for each context while reducing overall processing requirements.
Solution Approach 2:
The system performs preliminary data processing and feature extraction during data collection phases, preparing training data in advance to reduce computational burden during model training and inference. By preprocessing conversation data, extracting relevant features, and organizing training datasets beforehand, the system reduces the processing power and time required for actual model training and prediction generation.
Data Source
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AI summary
At an electronic device with a display screen, display a message transcript, where the message transcript includes an incoming message. Determine, based at least in-part on the message, a plurality of suggested one or more characters. Determine if the user of the device has, in the past, frequently inputted a response different from the plurality suggested one or more characters when the plurality were presented for user selection. Optionally, determine if the frequently inputted response is synonymous with a suggested one or more characters. Display the frequently-inputted response, in place of at least one of the plurality of suggested one or more characters, under some circumstances based on these determination(s).