Dynamic Banner Suggestions for Context-Aware Service Actions
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Solution Overview
Problem
User devices waste resources trying to identify new operations due to a lack of immediate useful actions, such as searching for services or deciding between multiple options, leading to inefficient use of power, memory, and processing resources.
Innovation Solution
A system and method that generates real-time suggestions based on dynamic notification and banner data, utilizing a machine learning algorithm to analyze context data, location information, inventory availability, and user preferences to provide proactive and personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the user device actively searches for and evaluates multiple services to identify useful operations, then the user can find relevant services, but device resources (power, memory, processing) are wasted
Solution Approach 1:
The system performs preliminary actions by proactively analyzing context data (location, time, user preferences) and generating service suggestions before the user needs to search. The server pre-evaluates which services are relevant based on current context, so when suggestions are presented to the user, the device does not need to actively search and evaluate multiple services, thereby conserving device resources while maintaining ease of operation
Solution Approach 2:
A server acts as an intermediary between the user device and multiple services. The server receives context data from the device, performs the complex analysis and service evaluation centrally, and returns filtered suggestions to the device. This shifts the computational burden from the resource-constrained device to the more powerful server, reducing device resource consumption while preserving the ability to identify useful operations
2Adaptability or versatility
If the system provides comprehensive service options to users, then users have more choices, but processing time and resources increase
Solution Approach 1:
The system applies local quality by providing different levels of service options based on specific contexts and user needs. Rather than uniformly presenting all available services, the system analyzes context data (location, time, user profile) and selectively presents only the most relevant service options for each situation. This maintains adaptability and versatility where needed while reducing processing time by filtering out irrelevant options in other contexts
3Ease of operation
If the user manually searches for services and operations, then the user maintains control over the search process, but the process becomes inefficient and resource-intensive
Solution Approach 1:
The system implements self-service by enabling users to define their preferences, context parameters, and service criteria once, after which the system automatically performs searches and evaluations based on these settings. The user maintains control over the search parameters and can modify them as needed, but the actual search process is automated, significantly improving productivity while preserving user control
Solution Approach 2:
The system uses feedback mechanisms where user responses to suggested operations are analyzed and used to refine future suggestions. The system learns from user interactions with suggested services, adjusting the search and recommendation process based on what the user accepts or rejects. This maintains user control while improving efficiency by progressively refining the search process based on actual user needs rather than generic assumptions
Data Source
AI summary
An apparatus comprises a memory and a processor communicatively coupled to one another. The memory may be configured to store existing configuration commands instructing execution of one or more operations. The processor may be configured to collect dynamic banner data from one or more interfaces. The dynamic banner data may be representative of multiple existing operations performed by the one or more interfaces. Further, the processor may be configured to generate a plurality of dynamic configuration commands based at least in part upon the dynamic banner data. The dynamic configuration commands may be updates to the existing configuration commands. The processor may be configured to generate multiple suggestions to perform one or more suggested operations based on the dynamic configuration commands, and present the suggestions in a dynamic banner via the one or more interfaces.