Context Engine for Action Recommendations in Incoming Communications
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Solution Overview
Problem
Current communication technologies lack context-based information in notifications, leading users to respond inadequately to incoming communications without specific or customized context, potentially missing relevant actions.
Innovation Solution
A computing device collects user data to generate context information, identifies characteristics of incoming communications, and provides action recommendations based on this context, using modules like context engine, recommendation generation, and notification modules to suggest specific actions through audible or visual notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If contextual information is added to notifications to improve user response quality, then the relevance and significance of communications is enhanced, but the device complexity and information processing requirements increase
Solution Approach 1:
The system performs preliminary actions by collecting user data, generating context information, and identifying communication characteristics before the user needs to respond. The context engine proactively builds user profiles and monitors communications in advance, so that when a notification arrives, the relevant context is already prepared and ready for immediate presentation to the user, eliminating the need for complex real-time analysis.
Solution Approach 2:
The patent introduces a context engine as an intermediary component that sits between the communication system and the user. This mediator collects data from multiple sources, processes it to generate context information, and presents relevant context to the user alongside notifications. The context engine acts as a buffer that simplifies the overall system architecture by centralizing context management rather than requiring direct integration between all system components.
2Measurement precision
If multiple data sources are monitored to generate comprehensive context information, then the accuracy of action recommendations is improved, but the use of energy and data processing requirements increase
Solution Approach 1:
The system applies local quality by monitoring and processing data from multiple sources selectively rather than uniformly. The context engine identifies which data sources are most relevant to the current communication context and focuses processing efforts on those specific sources. For example, if a notification is from a banking application, the system prioritizes collecting financial context from banking-related data sources while potentially reducing monitoring of unrelated sources, thereby reducing overall energy consumption while maintaining context accuracy.
3Ease of operation
If action recommendations are provided based on contextual analysis, then user decision-making is improved, but the time required to process and present information increases
Solution Approach 1:
The system performs preliminary analysis and generates action recommendations in advance, before the user needs to respond to a communication. The context engine continuously monitors communications and prepares relevant context information and potential actions ahead of time, so that when a notification arrives, the system can immediately present pre-analyzed recommendations to the user without requiring time-consuming real-time processing.
Solution Approach 2:
The system implements feedback mechanisms where user responses to action recommendations are tracked and used to refine future recommendations. The context engine learns from user behavior patterns and adjusts the generation of action recommendations based on what actions the user actually takes, improving the quality and relevance of recommendations over time while reducing the need for extensive real-time analysis.
Data Source
AI summary
Various implementations provide context-based action recommendations based on an incoming communication. To provide the context-based action recommendation, a computing device collects information associated with a user that can be used to generate context information. Upon receiving an incoming communication, the computing device identifies various characteristics associated with the incoming communication. In turn, the computing device analyzes the context information, using the identified characteristics, to identify context information relevant to the incoming communication. Some implementations generate an action recommendation based on the identified context information such as a purchase transaction correlated with a monetary deposit and/or determining a biometric, or vital sign of the user, and notify the user of the action recommendation. In one or more implementations, when an audio control mode is enabled, the computing device notifies the user of the action recommendation using an audible notification.