Dynamic Embedded Service Selection via Federated Learning
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Solution Overview
Problem
Current embedded services fail to provide a tailored experience for users by not leveraging their dynamic interactions with the underlying application, resulting in static and irrelevant service offerings.
Innovation Solution
A system that maintains a central collection of embedded modules, analyzes user interactions, and selects relevant modules using machine learning models trained on previous user interactions, incorporating federated learning to preserve user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If embedded services are provided as static, generalized offerings, then implementation complexity is reduced, but service relevance to users deteriorates
Solution Approach 1:
The patent transforms static embedded services into dynamic ones by continuously monitoring user interactions with the primary application and adapting service offerings in real-time. The system observes user behavior patterns, interaction depth, and engagement metrics to dynamically select and customize which embedded services are presented to each user, ensuring services evolve with user needs rather than remaining fixed.
Solution Approach 2:
The system changes key parameters of embedded service delivery including timing (when services are offered), selection (which services are offered), and customization (how services are tailored). By adjusting these parameters based on real-time analysis of user interaction data, the system optimizes service relevance without requiring complete redesign of the service architecture.
2Adaptability or versatility
If user interaction data is collected and analyzed to personalize services, then service relevance improves, but user privacy concerns increase
Solution Approach 1:
The patent introduces an intermediary layer that processes user interaction data without exposing raw personal information. The system analyzes behavioral patterns and interaction metadata at an aggregated, anonymized level to infer user intent and preferences. This intermediary processing mechanism enables personalization while maintaining a privacy buffer between raw user data and service delivery logic.
Solution Approach 2:
The system enables users to indirectly control their privacy exposure through self-service mechanisms where users can adjust their interaction patterns or opt-out of certain tracking. The personalized service experience emerges from user choices about what interactions to engage in, rather than from explicit data collection, giving users agency over their privacy boundaries.
3Measurement precision
If real-time analysis of user interactions is performed, then service selection accuracy improves, but computational resources required increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and categorizing user interaction data as it is generated, creating structured features and patterns before they need to be analyzed for service selection. The system establishes baseline user profiles and interaction patterns in advance, so that real-time decision-making requires only comparing current interactions against pre-computed models rather than analyzing raw data from scratch.
Solution Approach 2:
The analysis system is segmented into multiple independent components that process different aspects of user interactions in parallel. Rather than performing one comprehensive analysis, the system divides evaluation into separate modules that assess different interaction dimensions (e.g., engagement depth, interaction frequency, content type), each consuming minimal computational resources while collectively achieving high selection accuracy.
Data Source
AI summary
A method according to the present disclosure may include providing an embedded service in an application, in response to receiving an input via the embedded service, determining an applicable module of a plurality of modules based on a characteristic of at least one of the input or of the embedded service, processing the input via the applicable module, and controlling the application based on the processed input. Another method according to the present disclosure may include providing an embedded service in an application, training a monitoring model via federated learning based on data derived locally from the application, monitoring, via the trained monitoring model, a plurality of interactions with the embedded service, determining, by the trained monitoring model, that at least one of the plurality of interactions comprises an anomalous interaction, and in response to the determination, restricting further usage of the application.