Methods for improving a work process

A machine learning system with generative AI and RAG pipeline optimizes work processes by categorizing and modifying steps to align with target goals, addressing efficiency and sustainability challenges in work processes.

DE102024135950A1Pending Publication Date: 2026-06-03TEUTLOFF TECH ACAD GGMBH
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Patent Information

Application Number
DE102024135950
Authority / Receiving Office
DE · DE
Patent Type
Applications
Current Assignee / Owner
Filing Date
2024-12-03
Publication Date
2026-06-03

AI Technical Summary

Technical Problem

Existing work processes face challenges in optimizing multiple parameters such as efficiency and environmental sustainability, especially when conventional methods have been exhausted, and there is a need for a method that can improve these processes independently of the specific parameter being optimized.

Method used

A method utilizing a machine learning system with generative AI and a RAG pipeline to categorize and optimize work steps by determining key performance indicators, simulating modified workflows, and training on target work steps to improve efficiency and sustainability.

Benefits of technology

The method effectively identifies and modifies work steps to enhance key performance indicators, reducing deviations and optimizing processes by integrating machine learning and AI to align actual steps with target steps, thereby improving productivity and reducing waste.

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Abstract

A method for improving a work process, wherein the method comprises the following steps: a) Providing information about the actual work steps by which the work process is carried out, b) Categorizing the current work steps using a machine learning system that includes a generative AI, for example an LLM, and a RAG pipeline trained with information about the work process, c) Determining a key performance indicator to be improved based on the categorization performed, d) Providing information about changed test workflows, e) Repeat steps b) to d)
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