Experience Matrix Prediction for Menu Navigation
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Solution Overview
Problem
Users of electronic devices face challenges in navigating complex menu structures to activate or deactivate functionalities, leading to time-consuming interactions and difficulties in memorizing relevant menu locations, especially when multiple applications need to be managed.
Innovation Solution
A system utilizing an experience matrix that stores co-occurrence data as high-dimensional sparse vectors to predict user preferences and automate the activation or deactivation of functionalities based on query words derived from current, past, and future conditions, reducing the need for manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually navigate complex menu structures to activate or deactivate functionalities, then the device provides comprehensive control over multiple applications and functions, but the interaction time and effort increase significantly
Solution Approach 1:
The system pre-calculates and stores co-occurrence data in an experience matrix during idle periods, so that when a user queries about a functionality, the prediction is already prepared and can be immediately presented. This eliminates the need for real-time computation during user interaction, reducing response time and effort.
Solution Approach 2:
The system automatically learns from user behavior patterns and updates the experience matrix without requiring explicit user input or configuration. The system serves itself by autonomously improving its predictive capabilities through continuous observation of co-occurrence data, reducing the need for manual menu navigation.
2Adaptability or versatility
If the system provides comprehensive menu options for controlling multiple functionalities, then the device offers complete control, but the complexity of the user interface increases
Solution Approach 1:
Instead of presenting a uniform comprehensive menu for all functionalities, the system adapts the user interface to show only locally relevant options based on the current context and user preferences captured in the experience matrix. Each user session receives a customized simplified menu tailored to their specific needs and historical behavior patterns.
Solution Approach 2:
The system pre-processes and organizes menu options based on predicted user needs, so that when users interact with the device, they see only the most relevant functionalities already arranged in an optimal order, rather than navigating through complete but overwhelming menu structures.
3Measurement precision
If the system stores detailed co-occurrence data in an experience matrix, then the prediction accuracy improves, but the memory requirements and data processing complexity increase
Solution Approach 1:
The experience matrix is segmented into multiple smaller matrices or partitions, each storing co-occurrence data for specific contexts, applications, or time periods. This allows the system to manage large amounts of data more efficiently by only loading and processing relevant segments based on current user needs, reducing memory footprint while maintaining prediction accuracy.
Solution Approach 2:
The system stores and processes only the essential co-occurrence data needed for accurate predictions, rather than retaining all possible combinations. By focusing on the most significant patterns and discarding redundant information, the system achieves high prediction accuracy with reduced memory requirements.
Data Source
AI summary
Co-occurrence data representing e.g. preferences and facts observed in a plurality of situations may be stored in a matrix as combinations of high-dimensional sparse vectors. The matrix may be called e.g. as an experience matrix. The data stored in the experience matrix may be subsequently utilized e.g. for predicting a preference of a user in a new situation. A prediction may be determined by a method comprising providing a query comprising one or more query words, accessing the experience matrix containing co-occurrence data stored as vectors of the experience matrix, determining a first auxiliary vector by identifying a vector of the experience matrix associated with a first query word, forming a query vector by using the first auxiliary vector, and determining the prediction by comparing the query vector with the vectors of the experience matrix.