Spatio-Temporal Sampling for Software Event Prediction
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Solution Overview
Problem
Existing machine learning models for predicting user behavior in software applications often suffer from inaccurate predictions due to randomly sampled data that over-represents certain aspects of usage patterns, leading to irrelevant actions and negatively impacting user experience.
Innovation Solution
The method involves generating a spatio-temporally sampled data set that balances spatial and temporal dimensions by sampling user activity data to create a predictive model that accurately forecasts user behavior, such as continued usage of a software application, by considering the amount of time spent and portions of the application visited.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If random sampling of usage pattern data is used to train machine learning models, then the training process is simple and fast, but the predictions become inaccurate due to over-representation of certain usage aspects
Solution Approach 1:
The usage pattern data is segmented into multiple dimensions (spatial dimensions representing different application features and temporal dimensions representing time-based usage patterns). This segmentation allows the system to systematically sample across different segments rather than using simple random sampling, thereby improving prediction accuracy while maintaining manageable complexity through structured organization of the data segments.
Solution Approach 2:
The patent introduces additional dimensions to the data sampling process by considering both spatial dimensions (different application features, screens, or modules) and temporal dimensions (time-based usage patterns). This multi-dimensional sampling approach transforms the single-dimensional random sampling into a more comprehensive spatio-temporal sampling framework, which captures more nuanced usage patterns and improves prediction accuracy without overwhelming complexity.
2Measurement precision
If comprehensive usage pattern data is collected to improve prediction accuracy, then more information is available for training, but the data processing time and computational resources increase
Solution Approach 1:
Instead of collecting and processing all available usage pattern data, the system applies partial sampling by selecting representative subsets across spatial and temporal dimensions. This partial action approach captures sufficient information for accurate predictions while avoiding the computational overhead of processing the entire dataset, thus reducing data processing time while maintaining prediction accuracy.
Solution Approach 2:
The system performs preliminary organization and structuring of usage pattern data into spatial and temporal dimensions before the actual training process. This preliminary action creates a pre-organized framework that enables efficient sampling and processing during training, reducing the computational burden and processing time during the actual model training while still utilizing comprehensive usage pattern information.
3Reliability
If spatio-temporally sampled data is used to train predictive models, then prediction accuracy improves, but the data generation and processing complexity increases
Solution Approach 1:
The complex task of generating spatio-temporally sampled data is broken down into manageable segments: spatial segmentation (dividing application usage into different features or screens) and temporal segmentation (dividing usage patterns into time-based segments). This segmentation reduces system complexity by creating modular, organized data structures that are easier to process while still capturing the comprehensive spatio-temporal characteristics needed for reliable predictions.
Solution Approach 2:
The patent systematically introduces spatial and temporal dimensions to organize usage pattern data, transforming unstructured comprehensive data into a structured multi-dimensional framework. This dimensional organization improves prediction reliability by capturing nuanced usage patterns while managing system complexity through structured data representation that enables efficient processing and analysis.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for training and using predictive models to predict the occurrence of an event within a software application. An example method generally generating a spatially sampled data set for a set of users of a software application. The spatially sampled data set includes, for each respective user of the set of users, an amount of time the user has spent, a number of discrete portions of the software application the user has visited, and an indication of whether the user has completed a defined task. A spatio-temporally sampled data set for users in the spatially sampled data set is generated, including, for each respective user in the spatially sampled data set, a plurality of candidate timestamps. A predictive model is trained based on the spatio-temporally sampled data set.