Predictive Touch Input Correction Using Markov Chains
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Solution Overview
Problem
Touch-sensitive screens often lack accuracy in interpreting user input, leading to unintended selections and user frustration due to their small size and inability to accurately detect touch inputs.
Innovation Solution
The system predicts user input by aggregating data on prior behavior to determine the most likely intended interaction, using techniques such as Markov chains and user-specific patterns to correct mistouches and improve selection accuracy on touch screens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Weight of moving object
If touch-sensitive screens are made small to portability, then device portability is improved, but touch input accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediary system consisting of a processor and predictive algorithms that mediate between the touch screen's physical limitations and the user's input intentions. The system uses Markov chains and user behavior patterns as computational intermediaries to translate imprecise touch locations into accurate intended selections, resolving the contradiction between small screen size and input accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously learns from user behavior patterns and adjusts its predictions accordingly. By analyzing historical user interactions and providing adaptive feedback, the system compensates for the inherent imprecision of small touch screens, improving accuracy over time without requiring larger display surfaces.
2Weight of moving object
If touch screen size is reduced for portability, then device portability is improved, but user input reliability deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing user behavior patterns and Markov chain models before actual input occurs. The system prepares predictive models based on historical data, allowing it to make accurate predictions about user intentions in advance, thereby improving reliability despite the small screen size and limited precision of individual touch inputs.
3Weight of moving object
If touch screen is made small for portability, then device portability is improved, but measurement precision of touch location deteriorates
Solution Approach 1:
The patent transitions from two-dimensional spatial measurement (direct touch location) to a multi-dimensional predictive model that incorporates temporal patterns, user behavior history, and contextual information. By adding these additional dimensions of analysis, the system achieves high measurement precision for intended input locations despite the physical limitations of small screen real estate.
Data Source
AI summary
Techniques for determining user input on a touch screen of a user device are disclosed. In some situations, the techniques include: receiving information about a user input provided to a touch screen of a user device, the touch screen displaying two or more selectable objects, wherein each of the selectable objects, if selected, initiates a response corresponding to the selection of the object, determining a selectable object among the selectable objects that has a highest likelihood of being an object that a user intended to select with the user input, and providing a response corresponding to a selection of the determined selectable object. In one situation, a selectable object is a button or a hyperlink included in a Web page displayed on the screen of the device, and the response includes content associated with the selected button or hyperlink.