AI Subscription Access Management Across Platforms and Devices
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Solution Overview
Problem
Existing subscription management tools lack complexity in providing customers with actionable analytics and recommendations across multiple different providers or platforms, making it challenging for users to effectively manage their subscribed-to products and services.
Innovation Solution
A cross-platform system that tracks subscription usage data and utility metrics, generates actionable recommendations, and automatically processes changes to on-device applications or instructs remote subscription servers to modify subscriptions, using AI-powered assistance for optimizing subscription mixes based on budgeting rules and customer preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing subscription management tools are used, then basic subscription tracking is provided, but actionable analytics and recommendations across multiple providers are lacking
Solution Approach 1:
The patent merges data from multiple subscription providers and platforms into a single centralized management system. This consolidation enables comprehensive analytics and cross-platform recommendations by aggregating subscription information, usage data, and billing details in one location, resolving the information loss problem without requiring users to manage multiple separate tools
Solution Approach 2:
The management system is designed with multi-functionality to handle diverse subscription types across different providers (streaming services, software subscriptions, retail memberships, etc.). It provides universal analytics, reporting, and recommendation capabilities that work across all subscription categories, enabling actionable insights while maintaining a single unified interface rather than provider-specific tools
2Productivity
If manual subscription management is performed, then basic control is possible, but effectiveness and time efficiency are reduced
Solution Approach 1:
The system implements automated feedback mechanisms that continuously monitor subscription usage patterns, billing cycles, and service performance. This feedback enables the system to automatically generate recommendations for optimization, identify redundant subscriptions, and alert users to billing anomalies, significantly improving management efficiency while reducing the time users need to spend on manual tracking
Solution Approach 2:
The management system provides self-service capabilities including automated subscription tracking, usage analysis, and recommendation generation. The system autonomously collects data from multiple providers, processes information, and presents actionable insights without requiring manual intervention, thereby enhancing productivity while minimizing time investment from users
Data Source
AI summary
A computer-implemented method is disclosed. The method includes: obtaining, via a computing device, device usage data associated with a first service that is accessible on the computing device; querying at least one network node of a first network to obtain network resource usage data associated with the computing device; generating recommendation data comprising a plurality of data records corresponding to usage instances for the first service based on the device usage data and the network resource usage data; and causing to be modified at least one device setting of the computing device based on the generated recommendation data. The recommendation data may be generated by a recommendation engine that is implemented as an artificial intelligence (AI)-powered assistant.