Real-Time Loyalty Credit Calculation Using IMS OSAS and OLS
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Solution Overview
Problem
Existing methods for administering loyalty programs in IP-based multimedia subsystems, such as IMS, are limited by the inability to perform real-time analysis and credit calculations based on user call behavior, relying on auxiliary functions and offline data analysis with significant delays.
Innovation Solution
A method and system for real-time credit calculation of loyalty programs using the Online Statistics Advertisement Server (OSAS) to record and store statistical data, which is then matched with loyalty profiles to trigger bonuses via the Online Loyalty Server (OLS) using Service Oriented Architecture, enabling immediate crediting and notification of customers through SMS or MMS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If offline CDR analysis is used for loyalty program administration, then data collection is simple, but the processing delay is several hours
Solution Approach 1:
The patent applies preliminary action by pre-configuring loyalty profiles with all necessary crediting criteria, rules, and parameters in the LPF database before runtime. This allows the system to immediately match runtime statistics against pre-defined profiles without requiring complex real-time analysis logic, thereby reducing processing delay while maintaining system efficiency
Solution Approach 2:
The patent introduces an intermediary OSAS component that collects and stores runtime statistics in a standardized format, acting as a mediator between data collection and loyalty profile matching. This intermediary layer simplifies the overall system architecture by centralizing data collection functions and providing ready-to-use statistics for profile matching, reducing both processing delay and system complexity
2Productivity
If auxiliary functions are used to analyze runtime data and compare with loyalty profiles, then data collection is centralized, but the analysis capability is insufficient
Solution Approach 1:
The patent applies universality by designing the LPF (Loyalty Profile Function) to store comprehensive loyalty program definitions that can handle multiple crediting criteria simultaneously. The single LPF component provides universal profile management capabilities that work across different loyalty programs and crediting scenarios, enhancing analysis capability without requiring separate specialized components for each program type
Solution Approach 2:
The system applies self-service by enabling the LPF to automatically perform profile matching and crediting decisions based on pre-configured rules. The loyalty profiles contain all necessary logic to self-evaluate runtime statistics and determine crediting eligibility without requiring external auxiliary analysis functions, thereby improving productivity while maintaining manageable system complexity
3Reliability
If real-time statistics collection is implemented, then customer retention improves, but the system complexity increases
Solution Approach 1:
The patent applies merging by integrating loyalty program management directly into the existing IMS architecture. The LPF, HSS, and OSAS components leverage existing infrastructure and signaling paths, combining loyalty functionality with telecom operations rather than creating separate standalone systems. This approach improves customer retention through real-time capabilities while minimizing the increase in system complexity by reusing existing architectural elements
Data Source
AI summary
The invention relates to a method for real time credit calculation of differing loyalty programs based on telephone usage of a user and use of IP-based multimedia sub system (IMS) services, wherein statistical data for the user is recorded and stored in a databank by means of an OSAS (Online Statistic Advertisement Server) service. The invention is characterized by storage of loyalty profiles in the LPF, registration of the customer in the IMS, start up of a service by the customer/user, contacting the OSAS in real time via the HSS profile, recording the statistics in real time for the service such as, for example, type, time spans and similar, triggering the OLS based on the HSS profile, startup of the interfaces by the OLS, use of operator systems through the OLS and provision of the service after contacting the OLS.