Clustering Platform Sessions for Targeted Content Delivery
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Solution Overview
Problem
Current systems for tracking and analyzing user behavior during platform sessions lack efficient methods for clustering user interactions, leading to suboptimal resource allocation and user interface customization.
Innovation Solution
A system and method that cluster platform sessions by transforming session records into a three-dimensional space using normalization factors, identifying clusters based on user engagement metrics, and associating user accounts with these clusters to tailor interactive promotional interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user behavior data is tracked and stored in detail during platform sessions, then user analysis precision is improved, but data processing complexity and infrastructure requirements increase
Solution Approach 1:
The patent segments user behavior data into distinct session records with specific metrics (engagement counts, query counts, output status). Each session record is independently structured and stored, allowing for precise tracking while enabling modular processing and analysis of individual session components rather than handling all data as a single complex entity.
Solution Approach 2:
The patent transforms raw session data into normalized parameters by applying normalization factors to engagement counts and query counts. This parameter transformation standardizes the data structure, reducing processing complexity while preserving the precision needed for accurate user behavior analysis through consistent scaling and comparison across sessions.
2Adaptability or versatility
If platform sessions are clustered and user accounts are associated with clusters, then user interface customization is improved, but computational processing load increases
Solution Approach 1:
The patent segments the user base into distinct clusters based on session behavior patterns. Each user account is associated with a specific cluster identifier, dividing the homogeneous user population into heterogeneous groups. This segmentation enables targeted interface customization for each cluster while reducing the computational burden of customizing interfaces for every individual user separately.
Solution Approach 2:
The patent merges users with similar behavior patterns into the same cluster, combining multiple individual processing requirements into a single group-based processing unit. This merging reduces the overall processing load by allowing the system to apply the same interface configuration to multiple users simultaneously rather than processing each user's customization requirements individually.
3Measurement precision
If session records include multiple engagement counts and normalization factors, then user behavior measurement precision is improved, but data storage requirements increase
Solution Approach 1:
The patent applies parameter changes by normalizing engagement counts and query counts using normalization factors stored within each session record. This transformation converts raw counts into standardized metrics that enable precise cross-session comparison while maintaining a compact data structure. The normalization factors are integrated into the session record itself, avoiding the need for separate large-scale storage structures.
Solution Approach 2:
The patent performs preliminary normalization of engagement data during the session recording process itself, rather than requiring post-processing of raw data. By calculating and storing normalized engagement counts and query counts along with their normalization factors at the time of session creation, the system establishes measurement precision upfront, reducing the need for additional data processing and storage overhead later.
Data Source
AI summary
Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products that provide for an improved and more efficient system for transmitting content to client devices. The embodiments disclose an apparatus that clusters user behavior based on platform session records. The platform session records comprise stored data associated with a plurality of user inputs received by the system for the duration of a platform session. The apparatus and methods transform the platform session records into data comprising a form that is usable for identifying clusters of platform sessions. As a result of the clustering methodologies, the apparatus is configured to deliver content specifically optimized for users of client devices. The present disclosure thus provides for an improved networked system that reduces the amount of data transferred via the network and allows for a more efficient system that has reduced infrastructure requirements and improved performance.