International business machines corporation (20240330646). REAL-TIME WORKFLOW INJECTION RECOMMENDATIONS simplified abstract
Contents
REAL-TIME WORKFLOW INJECTION RECOMMENDATIONS
Organization Name
international business machines corporation
Inventor(s)
Zachary A. Silverstein of Georgetown TX (US)
Melanie Dauber of Oceanside NY (US)
Jacob Ryan Jepperson of St. Paul MN (US)
Logan Bailey of Atlanta GA (US)
Jeremy R. Fox of Georgetown TX (US)
REAL-TIME WORKFLOW INJECTION RECOMMENDATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330646 titled 'REAL-TIME WORKFLOW INJECTION RECOMMENDATIONS
Simplified Explanation: The patent application describes systems and methods for generating workflow injection recommendations using machine learning predictive models and real-time software activity data.
- Training a machine learning predictive model with workflow event data from multiple remote computing devices
- Identifying actions of interest within software activity data during a workflow event based on user behavior
- Determining software recommendations for injecting tasks into the workflow based on user actions and recommendation profiles
- Sending recommendation notifications to users during workflow events
- Updating the predictive model based on user feedback
Key Features and Innovation: - Utilizes machine learning to generate software recommendations for completing tasks in workflow events - Real-time identification of user actions of interest to provide relevant software recommendations - Personalized recommendation profiles for users to enhance task completion efficiency - Continuous updating of the predictive model based on user feedback for improved recommendations
Potential Applications: - Task automation in various industries such as healthcare, finance, and manufacturing - Streamlining workflow processes in businesses to increase productivity and efficiency - Enhancing user experience in software applications by providing tailored recommendations for task completion
Problems Solved: - Lack of personalized software recommendations for users during workflow events - Inefficient task completion due to manual selection of software tools - Limited visibility into user actions and behaviors during workflow events
Benefits: - Improved task completion efficiency through personalized software recommendations - Enhanced user experience with real-time notifications and tailored suggestions - Increased productivity and workflow optimization in various industries
Commercial Applications: Optimizing Workflow Injection Recommendations with Machine Learning
Questions about Workflow Injection Recommendations: 1. How does machine learning improve the generation of software recommendations in workflow events? 2. What are the potential challenges in implementing real-time software activity data analysis for workflow injection recommendations?
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for workflow optimization and personalized software recommendations.
Original Abstract Submitted
systems and methods for generating workflow injection recommendations are provided. in embodiments, a method includes: training a machine learning (ml) predictive model with workflow event data received from multiple remote computing devices, thereby outputting a knowledge corpus of software recommendations to complete tasks in workflow events; identifying in real-time actions of interest within software activity data generated during a workflow event of a user based on a recommendation profile of the user; determining, from the knowledge corpus of software recommendations, one or more software recommendations for injecting one or more tasks into the workflow based on the actions of interest and the recommendation profile of the user; sending a recommendation notification to the user during the workflow event; and updating the ml predictive model based on user feedback responsive to the recommendation notification.
