Adaptive Communication Prioritization via Context-Aware Action Extraction
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Solution Overview
Problem
Current communication tools and machine learning techniques fail to effectively prioritize and organize actionable statements in electronic communications due to their inability to adapt to various contexts, languages, and complexities, leading to missed action items and economic losses.
Innovation Solution
A computer-implemented method and system that identifies the importance and urgency of communications by evaluating features such as party importance, content, and context, using adaptive models to prioritize communications and conversations based on these factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual organization techniques (flagging, priority indication) are used to help users organize communications, then users can organize action items in their communication backlog, but users have to process and manually organize all communications themselves, adding more tasks for users to perform
Solution Approach 1:
The system automatically processes and organizes communications without requiring user intervention. The server autonomously parses communication content, identifies actionable statements, extracts entities and context, and generates organized representations, allowing the system to serve itself rather than requiring users to manually organize each communication.
Solution Approach 2:
The system performs preliminary processing of communications before users need to review them. By pre-parsing, pre-identifying action items, and pre-organizing communications in the backlog, the system reduces the cognitive load and time users would otherwise spend on organization tasks.
2Productivity
If automated processing algorithms using statistical machine learning approaches are used to parse communications, then users are relieved from manual processing, but the algorithms cannot adapt to the wide range of contexts necessary for effective automation
Solution Approach 1:
The system dynamically adapts its processing approach based on the specific context of each communication. Rather than using a static statistical model, the system adjusts its parsing and entity recognition strategies according to the detected communication type, domain, and contextual cues, enabling effective automation across diverse scenarios.
Solution Approach 2:
The system changes its processing parameters and strategies based on the detected context. By identifying communication patterns, domains, and contextual features, the system adjusts its entity recognition and action item extraction parameters to match the specific situation, thereby maintaining both efficiency and adaptability.
3Reliability
If comprehensive and generic patterns and rules are manually encoded to cover all possible actions and contexts, then all actionable statements can be captured, but finding and training such comprehensive patterns is an exceedingly difficult and non-scalable task
Solution Approach 1:
The system replaces manual mechanical encoding of patterns with an automated learning system. Instead of manually creating and maintaining comprehensive rule sets, the system uses machine learning algorithms to automatically learn patterns from data, substituting the mechanical process of manual rule creation with an automated computational process that scales more effectively.
Solution Approach 2:
The system introduces an intermediary learning layer between the raw communications and the pattern recognition. This intermediary component automatically extracts and generalizes patterns from training data, serving as a mediator that translates diverse communication formats into structured representations without requiring manual encoding of every possible pattern.
4Productivity
If the volume of communications increases with increased frequency and speed of exchange, then more information can be communicated, but the volume becomes impossible or impractical to process effectively within traditional work schedules
Solution Approach 1:
The system performs continuous processing of communications as they arrive, rather than batching them for later review. By continuously parsing, identifying, and organizing action items in real-time, the system maintains a constantly updated backlog without requiring dedicated processing time, thereby eliminating the trade-off between communication volume and processing time.
Data Source
AI summary
In one embodiment, a computer-implemented method for action-aware communication and conversation prioritization includes: identifying an importance of one or more parties to a communication or conversation; evaluating content of the communication or conversation to identify one or more features of the communication or conversation; assessing an importance of the content included in the communication or conversation based on one or more of the identified features; determining an urgency of the communication or conversation based on one or more of the identified features; and prioritizing the communication or conversation based at least in part on the importance of one or more of the parties, the importance of the content, and the urgency of the communication or conversation. The method is adaptive in a continuous manner based on user actions responsive to the communication or conversation. Related systems and computer program products are also disclosed.