Issue Tracking Recommendation Panel for Faster Issue Resolution
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Solution Overview
Problem
Existing issue tracking systems face challenges in efficiently accessing and compiling information from disparate sources, making it difficult to manage large quantities of issues and provide effective user interfaces for resolving technical problems.
Innovation Solution
A generative answer interface that utilizes a graphical user interface with a recommendation panel, including selectable link objects, subject matter expert references, and suggested action narratives, generated using a generative output engine to provide relevant content for issue resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional issue tracking interfaces are used to manage large quantities of issues, then basic tracking functionality is maintained, but information accessibility and compilation efficiency deteriorate
Solution Approach 1:
The patent introduces a generative AI intermediary that acts as a mediator between users and the complex issue tracking system. This AI component processes natural language queries, automatically searches and compiles information from disparate issue sources, and presents synthesized results, thereby eliminating the need for users to navigate complex interfaces directly while maintaining full access to the underlying system data
Solution Approach 2:
The patent replaces manual mechanical operations (users manually searching, filtering, and compiling issue information through complex interface interactions) with an automated intelligent system. The generative AI model processes information requests by transforming them into structured queries, automatically retrieving and synthesizing data from multiple sources, and generating compiled results without requiring users to perform manual navigation or data aggregation tasks
2Loss of information
If manual compilation of issue information from disparate locations is performed, then comprehensive information gathering is achieved, but time consumption and operational efficiency worsen
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing issue data from disparate sources into a structured format that the generative AI can efficiently query. The AI model is pre-trained on domain-specific terminology and issue patterns, enabling it to quickly understand and retrieve relevant information without performing time-consuming manual compilation during actual user operations
Solution Approach 2:
The patent replaces the manual mechanical process of information compilation with an automated intelligent system. The generative AI model automatically transforms user queries into structured search operations, retrieves information from multiple disparate sources simultaneously, and synthesizes comprehensive results in seconds, replacing what would otherwise require manual data gathering and compilation across multiple systems and locations
3Measurement precision
If detailed issue data is displayed to improve resolution accuracy, then information completeness improves, but user interface complexity and cognitive load worsen
Solution Approach 1:
The patent segments the comprehensive issue information into hierarchical levels of detail. The generative AI analyzes the specific context of each issue and selectively presents only the most relevant information segments at the appropriate level of detail, organizing data into logical groups such as key issue characteristics, related historical issues, and recommended actions, thereby providing diagnostic precision without overwhelming users with unnecessary details
Solution Approach 2:
The system applies local quality by tailoring the information presentation to the specific local context of each issue and user. The generative AI adjusts the level of detail, format, and focus of information based on the particular issue type, user role, and query context, providing highly relevant customized information for each situation rather than displaying uniform detailed data for all cases, thereby improving both diagnostic accuracy and usability
Data Source
AI summary
Embodiments described herein relate to systems and methods for providing a recommendation panel for a graphical user interface of an issue tracking platform. The system and methods can include causing display of the recommendation panel in the issue-view graphical user interface. The recommendation panel can include a first section including a set of one or more selectable link objects, where each selectable link object associated with a respective content item identified for the request type; a second section including a link to a user profile of a subject matter expert user, where the subject matter expert user selected based on a subject matter determined using the issue data; and a third section including suggested action narrative. The suggested action narrative can be determined using a generative response received from the generative output engine in response to the prompt.