AI Messaging Engine for Mobile App Communication Condensation
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Solution Overview
Problem
Users in business settings receive a high volume of electronic communications without effective summarization or response mechanisms, leading to information overload and reduced engagement with notifications and alerts.
Innovation Solution
A method utilizing a neural network trained on user communications and historical selections to condense and contextualize information, providing personalized next steps and interactive options on mobile devices, thereby reducing the load and increasing user response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users receive all electronic communications without filtering, then complete information is provided to users, but information overload occurs and user engagement decreases
Solution Approach 1:
The patent segments electronic communications by grouping messages into topic-based collections rather than presenting them as individual items. Communications are organized into distinct groups representing different topics or contexts, allowing users to process information in manageable segments rather than overwhelming individual messages sequentially
Solution Approach 2:
The system extracts and removes duplicative or redundant communications from the legacy communications before presentation. By identifying and eliminating duplicate messages, the system retains essential information while reducing overall volume and preventing information overload
2Loss of information
If all legacy communications are presented to users, then comprehensive information is available, but the time required to review and respond increases
Solution Approach 1:
Communications are segmented into topic-based groups with summary representations, allowing users to quickly assess each group's content and relevance without reviewing every individual message. This hierarchical organization enables efficient navigation and faster decision-making about which communications require detailed attention
Solution Approach 2:
The system performs preliminary processing of communications by identifying duplicative messages and generating topic-based summaries before user interaction. This pre-processing eliminates redundant information and prepares condensed representations, reducing the time users need to spend on review and response
3Device complexity
If generic response options are provided to users, then system complexity is minimized, but user response rates remain low due to lack of personalization
Solution Approach 1:
The system applies local quality by providing personalized response options tailored to each user's historical behavior patterns, preferences, and communication context. Rather than uniform generic responses, the system adapts suggested actions to match individual user characteristics and specific communication scenarios, increasing relevance and response rates
Data Source
AI summary
A system for condensing user communications relating to a topic is provided. The system may include a processor and a non-transitory memory. The processor may: designate a topic of user interest; retrieve legacy communications; and remove duplicative communications. The processor may form a topic-centric training set for a neural network. The topic-centric training set may be based on the legacy communications, legacy intelligence, and the plurality of outcomes and may be delimited by an analysis of the database. The processor may synthesize the neural network using the topic-centric training set in order to assign individual weights to each of a plurality of nodes in the neural network. In response to a selection of the topic of user interest, the processor may generate a plurality of user options based on the neural network. The system may include a display in order to prompt the user to select one of the options.