Embedded Service Personalization Through Interaction-Based Module Selection
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Solution Overview
Problem
Current embedded services fail to provide a tailored experience by leveraging user interactions within applications, often activating without regard to the user's inputs, leading to irrelevant services.
Innovation Solution
A system utilizing machine learning models trained via federated learning to analyze user interactions, selecting relevant embedded modules based on these interactions, and employing modules for abnormality detection, contract analysis, impersonation detection, and reward generation to enhance user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If embedded services are activated based on preset time or generic rules, then service activation is simple and fast, but the services are not relevant to user interactions and provide poor personalization
Solution Approach 1:
The system continuously monitors user interactions with the primary application and uses this feedback to dynamically determine which embedded services to activate. The service activation is based on real-time analysis of user behavior patterns, ensuring services are relevant to current user needs rather than following preset time-based or generic rules.
Solution Approach 2:
The patent replaces traditional mechanical rule-based service activation (preset time triggers, generic user profiles) with an AI-driven system that uses machine learning models to analyze user interactions and automatically determine appropriate embedded services. This substitution enables sophisticated personalization without requiring complex manual configuration of activation rules.
2Ease of manufacture
If AI models are trained using centralized data collection, then model training is straightforward, but user data privacy is compromised
Solution Approach 1:
The patent segments the centralized data collection process into distributed federated learning nodes. Each user device trains local AI models using its own data without sharing the data itself, and only model parameters or gradients are exchanged with the central server. This segmentation enables model training while preserving user data privacy.
Solution Approach 2:
The system introduces an intermediary federated learning framework that mediates between user data and central model training. The intermediary enables collaborative model improvement across multiple users without direct data sharing, using cryptographic techniques and secure multi-party computation to ensure privacy while facilitating straightforward model training.
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.