ChatOps Form Generation From Historical Conversation Patterns
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Solution Overview
Problem
Current mechanisms lack the ability to automatically generate electronic forms within ChatOps environments for efficiently gathering information during task execution, especially in workflows involving multiple entities with varying user preferences and task frequencies.
Innovation Solution
An improved computing tool that analyzes historical asynchronous communication logs to identify frequently occurring normalized utterances, generates electronic forms based on these utterances, and adapts to user preferences by automatically presenting forms during runtime interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If electronic forms are manually created for each communication sequence in ChatOps, then information gathering capability is improved, but time consumption and effort increase significantly
Solution Approach 1:
The system performs preliminary analysis of historical conversation logs to pre-identify frequently occurring normalized utterances and their corresponding attributes. This preliminary action enables the automatic generation of electronic forms before they are actually needed, eliminating the manual creation process and significantly reducing time consumption while maintaining high information gathering efficiency
Solution Approach 2:
The system enables self-service by automatically generating electronic forms based on analyzed communication patterns without requiring manual intervention. The forms are created autonomously by the system using machine learning models that process historical data and identify recurring information requirements, allowing the ChatOps environment to serve itself rather than relying on external form creation
2Adaptability or versatility
If a standardized approach is used for all user interactions, then system simplicity is maintained, but user preference adaptation is limited
Solution Approach 1:
The system applies local quality by customizing electronic form presentation based on individual user preferences while maintaining a standardized core structure. Each user receives forms tailored to their specific communication patterns and preferences identified through analysis of their historical interactions, allowing personalization without requiring complete system redesign for each user
Solution Approach 2:
The system implements dynamics by making form presentation adaptive and changeable based on user behavior patterns. The electronic forms dynamically adjust their presentation style, level of detail, and interaction mode according to each user's preferences learned from historical data, enabling the system to evolve and adapt to user needs without increasing fundamental complexity
3Productivity
If forms are presented to all users regardless of preference, then information gathering is maximized, but user experience and satisfaction decrease
Solution Approach 1:
The system uses feedback mechanisms by analyzing user responses and interactions with electronic forms to continuously improve form presentation. User preferences are captured through their interaction patterns and fed back into the system to refine future form presentations, ensuring that productivity gains are maintained while user experience continuously improves based on actual usage data
Solution Approach 2:
The system applies partial action by selectively presenting electronic forms only to users who have demonstrated preference for this interaction mode. Rather than forcing forms on all users, the system identifies and targets specific user segments who will benefit from form-based interactions, achieving sufficient information gathering efficiency while preserving natural conversation flow for users who prefer it
Data Source
AI summary
Mechanisms are provided for automated generation of an electronic form for an electronic messaging subsystem. Historical conversation logs are obtained from the electronic messaging subsystem which comprise a plurality of communication sequences. Communication sequences within the historical conversation logs are clustered according to similarity of features. For each cluster the following operations are performed: identifying, within the cluster, sequences of normalized utterances that are repeated across communication sequences of the cluster; categorizing each sequence, in a set of the sequences of normalized utterances, as to whether the sequence can be represented as one or more electronic forms; and extracting, for each communication sequence in the cluster, attributes and corresponding attribute values. One or more electronic form data structures are generated based on the attributes and corresponding attribute values extracted for each communication sequence in the cluster.