UiPath had two patents in big data during Q2 2024. UiPath Inc’s patents in Q2 2024 focus on process evolution and micro-optimization for robotic process automation (RPA). The company utilizes artificial intelligence (AI) for scientific planning of RPA implementations, embedded analytics for measuring and aligning RPA operations with business outcomes, and deploying AI skills through an AI fabric for seamless application and management of AI in RPA workflows. The cycle of planning, measuring, and reporting is iteratively improved with the help of AI, and RPA implementations are chosen based on estimated return on investment (ROI). Additionally, a method is provided for automatically augmenting features into target applications hosted by browsers without modifying the source code, executed by an augmentation engine. GlobalData’s report on UiPath gives a 360-degree view of the company including its patenting strategy. Buy the report here.
UiPath had no grants in big data as a theme in Q2 2024.
Recent Patents
Application: Process evolution for robotic process automation and workflow micro-optimization (Patent ID: US20240208049A1)
The patent filed by UiPath Inc. focuses on process evolution for robotic process automation (RPA) and RPA workflow micro-optimization. The implementation involves scientifically planning RPA with the potential use of artificial intelligence (AI) and embedded analytics to measure, report, and align RPA operations with strategic business outcomes. The RPA is then deployed using AI skills, such as machine learning (ML) models, through an AI fabric that scales and manages AI for RPA workflows. The cycle of planning, measuring, and reporting is iteratively improved with the help of AI, enhancing the effectiveness of RPA for businesses. Additionally, RPA implementations are identified and implemented based on their estimated return on investment (ROI).
The patent claims detail the computer-readable media storing programs that receive RPA implementation plans, perform analytics on associated data, generate RPA workflows based on analytics, and control RPA robots to interact with computing systems. The data analyzed includes employee interactions, financial information, and workflow performance metrics. The method involves iteratively repeating the analytics process, generating RPA workflows, and deploying them via RPA robots to enhance RPA effectiveness. Furthermore, a computing system is described that analyzes and prioritizes activities in RPA workflows based on performance criteria, allowing for modifications to improve automation efficiency. The system can generate, run, and test RPA robots implementing modified workflows, ensuring goal achievement and performance improvement before deployment.
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