AI Recommendation Links for Faster Program Action Execution
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Solution Overview
Problem
Program managers face challenges in quickly grasping the overall status of complex programs and identifying projects requiring immediate attention, leading to increased risks of errors and missed critical details.
Innovation Solution
A system that generates custom links based on generative AI model recommendations, allowing users to take actionable steps by analyzing and augmenting user prompts, and presenting targeted functionality through embedded or separate links.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If program managers manually review complex program data to grasp overall status and identify projects requiring attention, then they can make informed decisions, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent introduces an intermediary system comprising a generative AI model and a link generation module that mediates between the complex program data and the program manager. The AI model analyzes program data, identifies projects requiring attention, and generates actionable links, thereby eliminating the need for manual review while maintaining high accuracy in program status assessment.
2Reliability
If program managers dedicate more time to reviewing program details, then they can identify critical projects more accurately, but the risk of errors and missing critical details increases due to the demanding nature of the process
Solution Approach 1:
The system enables self-service by allowing the AI model to autonomously analyze program data, identify critical projects, and generate actionable recommendations without human intervention in the analysis process. The system serves itself by automatically processing complex financial metrics and program data, thereby improving reliability while reducing the complexity burden on program managers.
3Measurement precision
If the system provides detailed AI recommendations, then users can take accurate actions, but users may struggle to determine how to initiate the recommended actions
Solution Approach 1:
The system performs preliminary actions by pre-generating executable links that are embedded within the AI recommendations. These links are prepared in advance and contain all necessary information to initiate the recommended actions. When users interact with the links, the actions are automatically initiated, eliminating the confusion about how to execute recommendations while maintaining high precision.
Data Source
AI summary
Techniques for generating actionable links to Artificial Intelligence (AI)-generated content are disclosed. A system generates a prompt to a generative AI model to generate content, including a recommended action based on a set of analyzed data. The prompt further includes instructions to identify a resource used to generate the recommended action. The generative AI model identifies resources used to generate the recommended action based on a set of resources included in the prompt or based on fine-tuning the generative AI model with a dataset that includes system tools available in a system. A system analyzes content output from the generative AI model to identify a recommended action. The system matches the functionality of a resource used to generate the recommendation with the recommended action. The system generates software code to link the AI-generated content to the resource with functionality to perform the recommended action.