AI-Generated Electronic Message Summaries for Priority Review
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Solution Overview
Problem
Existing electronic messaging systems are time-intensive and resource-consuming due to users reading and interacting with numerous messages to determine relevance, leading to productivity loss and excessive computing resource usage.
Innovation Solution
An electronic messaging system utilizes a generative AI model to categorize and prioritize incoming activity items, generating digest, importance, and content summaries, allowing users to navigate through summarized activity items based on priority, reducing the need for individual message retrieval and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users read and interact with numerous messages individually to determine relevance, then users can ensure they do not miss important information, but productivity is reduced and computing resources are excessively consumed
Solution Approach 1:
The patent extracts the most important information from messages by generating summaries that capture key points, allowing users to quickly assess relevance without reading entire messages. This extraction process maintains information completeness for important items while reducing the time and effort required to evaluate message relevance.
Solution Approach 2:
The system performs preliminary analysis of messages by generating summaries and assigning importance indicators before users review them. This preliminary action allows users to prioritize their review based on pre-processed information, significantly improving productivity while ensuring important messages are not missed.
2Reliability
If users read and interact with numerous messages individually to determine relevance, then users can ensure they do not miss important information, but excessive computing resources are consumed
Solution Approach 1:
The system extracts only the most relevant information from messages through summary generation, avoiding the need to process and transmit entire message contents. This extraction approach maintains information completeness for important items while significantly reducing computing resource consumption during message evaluation.
Solution Approach 2:
The patent changes the parameter of information representation from complete message text to condensed summaries with importance indicators. This parameter change reduces the volume of data that needs to be processed, transmitted, and stored, thereby reducing computing resource consumption while preserving essential information.
3Loss of information
If the system provides all new messages to users for review, then users have access to complete information, but network bandwidth is excessively consumed
Solution Approach 1:
The system extracts essential information from messages and transmits only these summaries to users, rather than transmitting complete message contents. This extraction approach ensures that important information remains available to users while significantly reducing network bandwidth consumption during message delivery.
Solution Approach 2:
The patent changes the parameter of transmitted data from complete message text to condensed summaries with metadata. This parameter change reduces the size of data transmitted over the network, thereby reducing bandwidth consumption while preserving the essential information users need to assess message relevance.
4Ease of operation
If the system generates comprehensive summaries for all activity items, then users can quickly assess message relevance, but device complexity increases
Solution Approach 1:
The patent applies different levels of summary generation and processing to different messages based on their characteristics and importance. Rather than uniformly processing all messages with the same complexity, the system adapts the summarization approach to each message's needs, reducing overall system complexity while maintaining ease of operation for message assessment.
Solution Approach 2:
The system performs comprehensive analysis and summary generation only for messages that are likely to be important, rather than applying the same processing to all messages. This partial action approach reduces device complexity by avoiding unnecessary processing of less important messages while still providing comprehensive summaries when needed.
Data Source
AI summary
An electronic message computing system tracks new activity items that reflect activities that have not yet been seen by the user. A. generative artificial intelligence (AI) model generates a digest summary that is provided to the user the next time the user accesses the electronic message system. The digest summary summarizes new activity. The generative AI model also generates importance summaries that summarize the importance of a particular activity to the user, and content summaries that summarize the content of an activity item (such as an electronic mail message). The electronic messaging system also assigns a priority to each new activity item and provides the summaries, along with a priority, to a client computing system. The client computing system conducts a user experience, navigating the user through the new activity items, based upon the priority assigned by the electronic message computing system.