Context-Aware User Action Prediction via Semantic Vector Indexing
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Solution Overview
Problem
Conventional mobile communication devices lack context awareness and predictive capabilities, presenting users with static and irrelevant options, failing to adapt to their current situation or context.
Innovation Solution
A processor-based system that detects user actions, translates them into semantic vectors, and uses a random index algorithm to predict next actions by comparing current context information with past data, allowing for dynamic and context-aware predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional mobile devices present pre-defined static choices to users, then the device operation is simple and straightforward, but the device lacks context awareness and cannot adapt to user situations
Solution Approach 1:
The system performs preliminary actions by continuously monitoring user actions, time, and location data in the background, building a knowledge base of user behavior patterns before predictions are needed. This allows the device to proactively prepare prediction models rather than reacting only when predictions are requested.
Solution Approach 2:
The patent introduces an intermediary prediction system that acts as a mediator between raw sensor data (user actions, time, location) and the user interface. This intermediary layer processes and interprets data to generate contextual predictions, shielding the complexity of the prediction algorithm from both the hardware and the user.
2Loss of information
If all possible choices are presented to the user, then the user has complete information, but the user is overwhelmed with irrelevant information in certain situations
Solution Approach 1:
The system applies local quality by customizing the information presentation based on the specific context (location, time, user action). Instead of uniformly presenting all possible choices everywhere, the interface adapts locally to show only relevant options for the current situation, making the information both complete and contextually appropriate.
Solution Approach 2:
The patent implements partial action by selectively presenting only a subset of all possible choices - specifically those most relevant to the current context. Rather than showing all available options (excessive), the system shows just enough information (partial) to help the user make informed decisions without overwhelming them.
3Measurement precision
If the device monitors multiple parameters (user action, time, location) to improve prediction accuracy, then prediction quality improves, but the processing complexity and energy consumption increase
Solution Approach 1:
The system applies partial action by monitoring all relevant parameters (user action, time, location) but using them selectively based on context. Not all parameters are processed with equal depth at all times - the system adjusts the level of analysis based on what is currently needed for accurate prediction, reducing unnecessary processing energy.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting the monitoring and processing intensity of different parameters based on the situation. For example, location data may be monitored continuously for navigation contexts but less frequently for other activities, optimizing the balance between prediction accuracy and energy consumption.
Data Source
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AI summary
An apparatus for predicting a next user action includes a processor configured to detect a user action, detect at least one of a time and a location of the user action, translate the user action and at least one of the time of the user action and the location of the user action into a set of words, calculate a semantic similarity between the set of words and at least one previously generated set of words and determine one or more predicted actions of the user based on a closest semantic similarity between the set of words and the at least one previously generated set of words.