User Activity Classification Using Dimensionality Reduction
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Solution Overview
Problem
Existing solutions for classifying user activity using simple heuristics lead to biased and inaccurate results due to the robustness of multi-dimensional activity data, especially in educational platforms, where variance in user activity across applications and services complicates real-time predictions and data insight generation.
Innovation Solution
Applying AI processing with dimensionality reduction techniques, such as PCA, to transform user activity data into a low-dimensional representation, enabling accurate binary classifications of user activity levels, which are then contextualized and rendered for educational platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple heuristics (e.g., majority vote) are used for classifying user activity, then the classification process is computationally simple and fast, but the accuracy and reliability of the classification deteriorates due to over-counting correlated events and inability to contextualize different activity scales
Solution Approach 1:
The patent transforms the classification approach by changing parameters from simple event counting to normalized activity scores that account for different activity scales and correlations. This involves computing activity scores that are normalized by expected activity levels, thereby adjusting the parameter representation to improve classification accuracy without requiring excessive computational resources.
Solution Approach 2:
The patent introduces a new dimension of analysis by computing activity scores that combine multiple event types into a unified metric. This dimensional transformation allows the system to contextualize events across different activity scales and applications, resolving the contradiction between simple processing and accurate classification by operating in an enhanced feature space.
2Loss of information
If robust multi-dimensional activity data is collected across multiple applications and services, then the comprehensiveness and information content of the data improves, but the complexity of processing and analyzing the data increases, leading to computational resource demands and latency issues
Solution Approach 1:
The patent extracts only the essential features needed for classification by computing activity scores that aggregate multiple event types into a single normalized metric per application/service. This extraction process removes redundant information while preserving the critical signals needed for accurate classification, thereby reducing processing complexity without losing important data insights.
Solution Approach 2:
The patent segments the complex multi-dimensional data into manageable components by computing activity scores for each application/service separately, then combining these scores for overall classification. This segmentation approach allows the system to handle robust multi-dimensional data in a modular fashion, reducing computational complexity while maintaining information completeness.
3Adaptability or versatility
If activity data is aggregated across multiple applications and services with varying content and tasks, then the versatility and applicability of the system improves, but the difficulty of generating accurate real-time predictions increases due to variance in user activity patterns
Solution Approach 1:
The patent creates a universal activity scoring mechanism that can be applied across multiple applications and services with different content and tasks. The activity score computation is designed to be application-agnostic, normalizing events from diverse sources into a common metric that enables accurate predictions regardless of the specific application or service context.
Solution Approach 2:
The patent addresses variance in activity patterns by changing the parameter representation to include normalized activity scores that account for expected activity levels in different contexts. This parameter transformation allows the system to adapt to varying content and tasks across applications while maintaining prediction accuracy through context-aware normalization.
4Loss of information
If voluminous weakly correlated activity data is processed to generate meaningful data insights, then the comprehensiveness of the analysis improves, but the computational resources required and the processing time increase significantly
Solution Approach 1:
The patent applies partial action by computing activity scores that focus on the most relevant events and applications for each user context. Rather than processing all voluminous data equally, the system computes scores that capture the essential signals needed for meaningful insights, thereby reducing processing time while maintaining insight quality through selective aggregation of the most informative data.
Data Source
AI summary
Non-limiting examples of the present disclosure relate to application of artificial intelligence (AI) processing to generate classifications of user activity for a group of users. For example, a classification prediction is generated indicating whether students in an educational class are predicted, over a predetermined time period, to have a high or low activity level based on contextual analysis of multiple types of user-driven events. As user activity data is typically quite robust, the present disclosure applies dimensionality reduction processing to efficiently manage user activity data and further improve accuracy in generating downstream binary classifications. A dimensionality reduction transformation of user activity data results in a low-dimensional representation of input feature data that is contextually relevant for generating a binary classification. Derived classifications are then utilized to generate data insights pertaining to user activity levels of one or more users. Data insights are can then be rendered for presentation.