Auto Consumption Filter for Context-Aware Asset Delivery
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Solution Overview
Problem
Existing computing systems require constant user interaction to manage and deliver recommended assets, lacking the ability to automatically filter and control consumption based on specific criteria such as location, device, and privacy considerations.
Innovation Solution
Implementing an auto consumption filter that evaluates recommendations against predefined or learned filter rules to determine when and how recommended assets should be automatically delivered, allowing for automatic consumption or filtering based on context-specific criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If constant user interaction is required to manage and deliver recommended assets, then delivery control and precision are improved, but user burden and time consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-defining filter rules and consumption preferences before asset delivery is needed. Users configure their preferences in advance, and the system automatically applies these rules when recommendations are generated, eliminating the need for constant user interaction during delivery while maintaining precise control over what assets are delivered.
Solution Approach 2:
The system enables self-service by allowing users to configure their own filter rules and consumption preferences once, and then the system automatically manages asset delivery based on these self-defined parameters. This eliminates the need for continuous user intervention while maintaining delivery precision through the self-configured rules.
2Productivity
If automatic consumption is implemented without filter rules, then system efficiency and productivity are improved, but compliance with location, device, and privacy constraints deteriorates
Solution Approach 1:
The system segments the automatic consumption process by introducing filter rules that divide asset delivery into compliant and non-compliant categories. The filter rules act as a segmentation mechanism that automatically separates assets meeting location, device, and privacy constraints from those that don't, allowing efficient automatic delivery of only compliant assets.
Solution Approach 2:
The filter rules serve as an intermediary between the automatic consumption system and the constraint requirements. This intermediary layer automatically evaluates assets against location, device, and privacy constraints before delivery, ensuring reliability and constraint compliance while maintaining the overall efficiency of automatic consumption.
3Reliability
If filter rules are manually configured for each asset, then constraint compliance is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The system applies universality by creating reusable filter rules that can be applied across multiple assets and delivery scenarios. Instead of manually configuring separate rules for each asset, users define universal filter rules that automatically apply to all assets, reducing device complexity and operational difficulty while maintaining constraint compliance.
Solution Approach 2:
The system simplifies filter rule management by allowing users to define filter rules in terms of high-level parameters (location types, device categories, privacy settings) rather than detailed asset-specific criteria. This parameter-based approach reduces the complexity of filter rule configuration while ensuring comprehensive constraint compliance.
Data Source
AI summary
A method includes acts for filtering auto consumption recommendations and auto consumption actions. The method includes receiving from a recommendation system, a recommendation of an asset for consumption. The asset for consumption is evaluated in the context of one or more filter rules regarding auto consumption. The filter rules are configured to filter recommended assets from being consumed when certain criteria are met or to permit recommended assets to be consumed when certain criteria are met. As a result, the method includes identifying one or more constraints on how recommended asset should be consumed. The method further includes filtering consumption of the recommended asset based on the one or more constraints.