Interaction Prediction Model for Selective App Prompts From Messages
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for interacting with applications based on electronic communications are inflexible, inefficient, and resource-intensive due to reliance on predefined rules, failing to trigger relevant interactions and causing unnecessary computational overhead.
Innovation Solution
A tailored interaction prediction model, such as a neural network, processes features of electronic communications to generate predicted interaction values, determining when and how to interact with applications based on user behavior, and optionally stores locally for security and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined rules are used to trigger application interactions, then the triggering conditions are clear and deterministic, but the system lacks flexibility and fails to trigger relevant interactions for communications that do not conform to strict rules
Solution Approach 1:
The patent transitions from rigid rule-based parameters (exact term matching, positional requirements) to flexible machine learning model parameters that can adapt to various communication patterns. The model learns from training data to predict interactions, allowing it to handle diverse scenarios without strict predefined conditions.
Solution Approach 2:
The system evolves from static predefined rules to dynamic machine learning models that continuously adapt based on user interactions and feedback. The model can be retrained and adjusted to improve its predictions over time, making the system more responsive to changing user behaviors and communication patterns.
2Measurement precision
If machine learning models are trained centrally on user data, then model accuracy can be improved, but user privacy and data security are compromised
Solution Approach 1:
The patent implements local training where each user device trains its own interaction prediction model using local user data. This distributed approach allows each device to maintain high prediction accuracy tailored to individual user preferences while keeping sensitive data localized and secure, eliminating the need to centralize user data.
Solution Approach 2:
The system uses local models as intermediaries that process user data on-device without requiring centralized data collection. These local models serve as mediators between user data and prediction functionality, enabling accurate predictions while maintaining data privacy through federated learning techniques.
3Reliability
If interaction prompts are provided for all electronic communications, then no relevant interactions are missed, but computational resources are wasted on unnecessary prompts
Solution Approach 1:
The system applies partial action by providing interaction prompts selectively rather than for all communications. The trained model predicts which communications are likely to benefit from interaction prompts, applying the intervention only where needed based on predicted user behavior patterns.
Solution Approach 2:
The system implements feedback loops where user responses to interaction prompts (or lack thereof) are fed back into the training data. This continuous feedback allows the model to learn from actual user behavior and refine its predictions, improving its ability to identify which communications warrant interaction prompts.
Data Source
AI summary
Training and/or utilizing an interaction prediction model to generate a predicted interaction value that indicates a likelihood of interaction with a corresponding application on the basis of an electronic communication. The application can be in addition to any electronic communication application that is utilized in formulating the electronic communication and/or that is utilized in rendering the electronic communication. The predicted interaction value can be generated based on processing, utilizing the interaction prediction model, of features of the electronic communication and/or of other features. The predicted interaction value can be utilized to determine whether to perform further action(s) that interact with, and/or enable efficient interaction with, the application on the basis of the electronic communication.