Contact Center Storage Lifecycle Optimization
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Solution Overview
Problem
Contact centers using CCaaS solutions face inefficiencies and increased costs due to outdated lifecycle management rules for interactions-related objects, as current systems lack the ability to assess and optimize storage strategies based on specific usage patterns and metadata analysis.
Innovation Solution
A computerized system with a Retention Effectiveness Calculation (REC) module that analyzes metadata from interactions-related objects, calculates Rule Effectiveness Scores and Object Retention Scores, and provides recommendations for optimizing lifecycle management by transitioning objects between storage classes based on access patterns and metadata criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If generic AWS S3 intelligent tiering is used to manage storage classes, then automated storage optimization is provided, but it does not consider specific contact center business requirements and metadata
Solution Approach 1:
The patent segments the storage management system into two distinct components: a generic automated tiering engine (AWS S3 intelligent tiering) and a contact center-specific lifecycle management layer. This layering allows the system to maintain automated optimization while adding domain-specific customization through contact center-aware lifecycle rules that consider metadata like call type, agent skill, and routing information.
Solution Approach 2:
The patent introduces a contact center-specific lifecycle management intermediary layer between the storage system and the contact center applications. This intermediary translates generic storage optimization into contact center-specific policies by analyzing metadata and applying business rules, thereby bridging the gap between automated tiering and domain-specific requirements.
2Extent of automation
If lifecycle rules are configured based on fixed time periods (30, 90, 180 days), then simple automation is achieved, but it does not adapt to changing usage patterns and leads to inefficiencies
Solution Approach 1:
The patent transforms static, fixed-time lifecycle rules into dynamic rules that adapt to changing usage patterns. The system continuously monitors access patterns and metadata, automatically adjusting lifecycle rules to reflect current contact center needs. This allows the system to respond to seasonal variations, changing call volumes, and evolving business requirements without manual intervention.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors actual usage patterns of stored objects and uses this information to optimize lifecycle rules. By analyzing access frequency, metadata correlations, and storage cost implications, the system provides feedback loops that automatically refine retention policies to improve storage efficiency while maintaining necessary accessibility.
3Productivity
If detailed metadata analysis and correlation are performed to optimize lifecycle rules, then storage efficiency is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal metadata analysis framework that handles multiple types of contact center metadata (call type, agent information, routing data, customer information) through a single correlation engine. This multi-functional approach consolidates what would otherwise require separate analysis systems, reducing overall complexity while enabling comprehensive metadata-driven lifecycle optimization across different object types and storage classes.
Data Source
AI summary
A computerized-method for determining and utilizing an effectiveness of lifecycle-management for storage of interactions-related objects, is provided herein. In a computerized system that is communicating with a multi-tier storage in a cloud-environment having a lifecycle-rules data-storage to store one or more lifecycle-rules, operating a Retention Effectiveness Calculation (REC) module. The operating of the REC module includes: (i) retrieving all lifecycle-rules from the lifecycle-rules data-storage; (ii) for each lifecycle-rule in the lifecycle rules data-storage calculating a Rule Effectiveness Score (RES); (iii) grouping all the calculated RES by media type; (iv) for each media type, calculating an Object Retention Score (ORS) for the media type; (v) dividing an aggregation of the ORS of all media types by a total number of media types to yield a total ORS for a contact-center; and (vi) displaying via a display unit the total ORS of the contact-center.