Predictive Entity Caching for Caller ID Data
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Solution Overview
Problem
Existing systems face inefficiencies in local storage and network access for caller identification data, leading to storage demands on user devices and unreliable network connections.
Innovation Solution
A method and system for dynamically and predictively caching data locally on user devices using a trained entity relevancy machine learning model, which analyzes user activity history to determine relevant entities and updates the cache accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If caller identification data is stored locally on user devices, then access reliability is improved, but storage demands on user devices increase
Solution Approach 1:
The system performs preliminary actions by predicting which caller identification data will be needed in the future and caching it locally in advance. The predictive caching mechanism analyzes historical activity patterns and pre-loads relevant entity data before it is actually requested, thus ensuring data availability while minimizing storage requirements.
Solution Approach 2:
The system dynamically changes the parameter of data relevance by using machine learning models to determine which entities are relevant to cache. The entity relevancy model continuously adjusts the selection criteria for cached data based on user behavior patterns, ensuring that only the most relevant data is stored locally, thereby optimizing the balance between storage usage and data accessibility.
2Productivity
If all entity data is cached locally, then network access frequency is reduced, but storage efficiency decreases
Solution Approach 1:
The system extracts only the essential and relevant portions of entity data for local caching, rather than caching complete datasets. The entity relevancy model identifies and extracts specific attributes and entities that are most likely to be needed, leaving less critical data on remote servers. This selective extraction approach reduces local storage requirements while maintaining network access efficiency.
Solution Approach 2:
The system applies partial action by caching only a subset of potentially needed data - specifically, the portion predicted to be most relevant based on historical patterns. Rather than caching all possible entity data (excessive action), the system caches just enough data to maintain high access reliability, optimizing the trade-off between storage usage and network access frequency.
3Productivity
If predictive caching using machine learning is implemented, then storage and network efficiencies are maximized, but system complexity increases
Solution Approach 1:
The system introduces an intermediary entity relevancy model that acts as a mediator between raw historical activity data and caching decisions. This machine learning model processes complex patterns in user behavior and translates them into simple caching instructions, effectively managing system complexity by isolating the computational complexity within a dedicated predictive component rather than分散 across the entire system.
Data Source
AI summary
Systems and methods of the present disclosure enable intelligent dynamic caching of data by accessing an activity history of historical electronic activity data entries associated with a user account, and utilizing a trained entity relevancy machine learning model to predict a degree of relevance of each entity associated with the historical electronic activity data entries in the activity history based at least in part on model parameters and activity attributes of each electronic activity data entry. A set of relevant entities are determined based at least in part on the degree of relevance of each entity. Pre-cached entities are identified based on pre-cached entity data records cached on the user device, and un-cached relevant entities from the set of relevant entities are identified based on the pre-cached entities. The cache on the user device is updated to cache the un-cached entity data records associated with the un-cached relevant entities.