Multi-Modal Recommendation Engine for Cross-Platform Cloud Notifications
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Solution Overview
Problem
Existing recommendation services are unable to provide instructions across all device and interface types due to the varying computing requirements of different interfaces, limiting their ability to manage notifications seamlessly across multiple platforms.
Innovation Solution
A system that implements a notification component for centralized management of actions, utilizing a recommendation component to determine and transmit computer-readable instructions for various devices and interfaces, leveraging a machine learned model to generate contextually relevant actions for notifications, thereby enabling proactive and efficient handling of notifications across diverse platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If typical recommendation services provide recommendations for specific device types, then the recommendations are optimized for that particular interface, but the service cannot provide instructions across all device and interface types
Solution Approach 1:
The patent implements a universal recommendation service architecture that can deliver recommendations across multiple device types and interface modalities. The system uses a centralized recommendation engine that generates platform-agnostic recommendations, which are then adapted to specific devices through client applications. This allows the service to maintain versatility across smartphones, tablets, wearables, and desktop devices without requiring separate recommendation systems for each platform.
Solution Approach 2:
The patent segments the recommendation system into distinct components: a centralized recommendation service that generates core recommendations, and device-specific client applications that adapt and present these recommendations locally. This segmentation allows the core recommendation logic to remain universal while accommodating interface-specific requirements through the client layer, resolving the contradiction between cross-platform capability and interface optimization.
2Adaptability or versatility
If the system transmits recommendation data to multiple devices, then notification management coverage is improved, but network bandwidth consumption increases
Solution Approach 1:
The patent implements proactive push notifications that deliver recommendation data to devices before users actively request them. The system pre-generates and transmits recommendation payloads to multiple devices based on predicted user needs and device usage patterns, reducing the need for repeated data exchanges and minimizing overall network bandwidth consumption while maintaining comprehensive multi-device coverage.
Solution Approach 2:
The patent implements device-specific adaptation where the centralized service sends core recommendation data, and local client applications customize and cache these recommendations for each device type. This allows the system to maintain multi-device notification management while optimizing network bandwidth by transmitting only essential data centrally and performing local customization, rather than sending fully customized recommendations to each device.
3Speed
If the system processes notifications in real-time, then response speed is improved, but computational resources are consumed
Solution Approach 1:
The patent implements pre-computed recommendation templates and device profile data that are prepared in advance and stored in the centralized service. When a notification arrives, the system quickly matches the notification against pre-computed templates and retrieves relevant device profiles, dramatically reducing real-time computational requirements while maintaining fast response speeds across multiple devices.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on device characteristics and notification priority levels. High-priority notifications receive full real-time processing, while lower-priority notifications use simplified matching algorithms and cached data. This parameter adjustment allows the system to maintain real-time processing capability for critical notifications while reducing overall computational resource consumption through adaptive processing intensity.
Data Source
AI summary
Techniques for a system to automatically recommend supported actions to a notification associated with a resource in a cloud computing environment are discussed herein. A system can includes a recommendation engine and a registry to provide centralized management of actions for responding to a notification instead of each service managing actions separately. A machine learned model can generate recommendations for resolving the notification based on a template stored in the registry that associates actions with instructions for presenting the recommendation in a respective interface modality (e.g., Chatbot, mobile application, console, etc.). In some examples, the system can implement a machine learned model, a graph model, and/or a heuristic model, to determine potential actions as recommendation data.