Technology Transfer – from Research to Innovation

The research results of the Competence Center ML2R are directly used for practice-oriented applications and lead to new data-based products and services. Our range of transfer offers encompasses education and training programs as well as agile cooperation formats, designed for successful collaborations with companies. We hereby take the entire lifecycle of AI- and ML-based technologies into consideration: from strategy development, planning, implementation and integration to safeguarding, operation and optimization. Examples of successful cooperation projects with partners from business and industry are presented below.

Offers for Companies

Many companies have already recognized the potential of AI and ML applications, but they face key challenges: They need comprehensible, trustworthy technologies that can be integrated into corporate processes in a flexible manner. They lack specialists who can develop such technologies and implement them strategically within the company.

Here, we provide you with customized offers and work closely with you to set the course for sustainable success in international competition. Small and medium-sized enterprises (SMEs) in particular benefit from access to strategies, knowledge, experience and the latest findings from our research.

Learn more about Opportunities for Cooperation

Manager for Technology Transfer

Dr. Stefan Rüping

“At ML2R, we are convinced that true innovation comes from the interplay between application and research. That is why we strive to support companies and together ensure that excellent research lays the foundation for intelligent solutions.”

Successful Projects from Strategic Fields of Application

We accompany you on the way to implementing ML technologies and AI solutions in practice. We have selected some examples of successful projects and cooperation with companies for you. The focus lies on the following core application fields:

Industry 4.0

AI and ML technologies have the potential to shorten development cycles, optimize the use of resources and make industrial production more efficient overall. We are happy to support and offer guidance to medium-sized companies, especially with respect to optimizing their processes with AI and ML methods. Our offers for cooperation hereby cover the entire value chain: from feasibility analysis to strategy development, consultation on technology deployment to sustainable implementation and continuous improvement.

Experimental Designs using AI: Accelerated Development and Optimized Use of Resources

Regardless of whether the challenge is to find new products, materials and formulations or to optimize existing ones: Our AI-based methodology helps you in the planning and optimal execution of trials. In this way, you can use your resources efficiently and save time and money.

In order to successfully compete in the global market, manufacturing companies must develop and optimize their products, materials and formulations ever faster. Traditional Designs of Experiment (DoEs), often find it difficult to guarantee high levels of velocity while maintaining the highest quality standards and optimal use of resources.

Experiments are essential for the development of new formulations and materials. Their parameters often allow for a wide range of possible combinations. The testing of each of these possibilities requires a great deal of human and material resources and is therefore time-consuming and costly.

We have developed an Artificial Intelligence-based DoE methodology that optimizes experimental designs as well as the use of resources, thus saving time and reducing costs. With our DoE methodology, we help you reach your goal faster, more reliably and more efficiently than with conventional methods.

Depending on the initial state, AI-based technology can save up to 50 percent of trials compared to traditional concepts of test planning. In addition, problems previously considered too complex too solve can be resolved using AI-based methods. Based on Bayesian optimization, our DoE methodology generates iterative experiment plans that lead you to your goal as quickly as possible.

The technology we have developed is based on our research of hybrid methods that combine Machine Learning with expert knowledge. In this way, we also support you in anchoring and optimally using existing knowledge in your company in the long term.

Data Summaries: Smart Data Summaries for More Efficient Production

For an efficient production, machine and sensor data must be collected, combined and evaluated. In a cooperative project with the company Achenbach Buschhütten, ML2R researchers developed new methods by which data from rolling mills was condensed into data summaries that can be easily analyzed and processed.

The aim of interconnecting machines and components in the context of Industry 4.0 is primarily targeted at monitoring and predicting machine behavior. This makes it possible to avoid unplanned downtime and to increase the efficiency of production. The key hereby lies within the efficient communication of machines among each other and with the cloud. In many companies, the implementation of the respective data communication is still in its early stages, but at Achenbach Buschhütten it is already firmly integrated into day-to-day business. Achenbach Buschhütten is an independent family-owned company looking back on more than 565 years of history and 125 years of experience in rolling mill equipment. The company is a pioneer in the interconnection of industrial plants with their own cloud-based platform Achenbach OPTILINK®. Achenbach Buschhütten is hence an ideal research partner for the Competence Center ML2R.

If machine data is available in large quantities through an interlinkage, it should be analyzed profitably. One challenge is that the large amounts of data can hardly be stored and analyzed in a timely manner. In addition, it is clear that a profitable analysis must always be carried out in close cooperation with the plant engineers.

Data summaries are an ideal approach to reducing the amount of data and making it comprehensible for users, such as plant engineers. A data summary is a small, well-chosen excerpt from the overall data that presents as much information and special features of the whole data set as possible. Formally, summaries can be represented using submodular functions, such as the “Informative Vector Machine”. Submodular functions provide a theoretically sound framework for the analysis of data summarization techniques while formalizing the notion of summarization in a mathematically precise way. This can then be used to select small and expressive summaries in linear time. On the other hand, the quality of the summaries and the selection process can be objectified and compared with the subjective impressions of the operating engineers.

At TU Dortmund University, methods for the summarization of data from astrophysics have been developed, which allow physicists to gain a quick overview of large measurement series. These techniques were further developed at ML2R in cooperation with Achenbach Buschhütten and adapted to the use on sensor data from rolling mills, which are available as time series. Further research on methods for data summarization is a vital part of the cooperation between Achenbach Buschhütten and the Competence Center ML2R.

Cognitive Process

 

Automation

In any company, understanding, summarizing and categorizing texts is an essential part of many work processes. We support you in automating cognitive processes: Benefit from Natural Language Understanding to understand and interpret texts using AI. This saves you time and money and makes your workflows more efficient. Our solutions have also proven themselves to be well-suited for specialist and industry-specific texts, such as court rulings, medical documentation or maintenance reports.

Insight Financials: Generating Financial and Economic Analyses swiftly and by automation

With our tool designed for financial and business professionals, you’ll have information at your fingertips quickly in order to create automated analyses. Our search and analysis engine allows you to automatically find company reports published on the web and extract data. You are hereby able to save time when researching, analyzing and preparing financial and economic data. You are able to use this data for example for market analyses, benchmarking and your strategic corporate planning.

Thousands of company reports, such as stock exchange prospectuses and annual financial statements, are published every year. These reports are based on different legal requirements. The financial figures they contain are available in an unstructured form, are complex and can be defined differently. Therefore, manual information research for benchmarking and market analysis based on company reports is very costly and time-consuming.

Insight Financials is an intelligent, AI-based, cross-industry and cross-company tool that facilitates the work of analysts, consultants, corporate strategists and investors. Based on methods of Artificial Intelligence and Machine Learning, our tool allows for the automated sighting of financial reports from thousands of companies using autonomous web crawlers.

All information essential to you as a user is extracted and provided in real-time through an intuitive web application. The tool offers a granular search process, various filtering options and a benchmarking tool. It is also able to detect outliers and make predictions based on the extracted, temporary data. You can hence make a direct comparison between competitors, which poses valuable information for a company’s management as well as for auditors and investors.

The neural network-based analysis, or so-called parsing, of automatically downloaded company reports was developed by researchers at the Competence Center ML2R. In addition, the ML2R focuses on resource-efficient ML technologies: The language and extraction models, used for the automatic extraction of the financial figures, do not require any specialized, cost-intensive infrastructure.

RICO: Artificial Intelligence facilitates Coding and Billing of Hospital Services

The “RightCoding” (RICO) software simplifies and automates billing processes. Hospital employees use RICO to code diagnoses, seamlessly record treatments and bill health insurance providers for services. The respective processes are hence conducted in a manner that is simpler, more secure and in compliance with data protection regulations.

In order for hospitals to be able to bill health insurance providers for their services in the treatment of patients, the conducted services must be coded. This process is essential for the securing of hospital revenues. However, it demands a high degree of personnel resources and is time-consuming as well as at times prone to errors.

Together with GSG Consulting GmbH and with the participation of ML2R scientists, Fraunhofer IAIS has developed the AI-backed software RICO, which supports hospital staff in the coding process. RICO stands for “RightCoding”: It helps the hospital team to include all services when billing and checks whether all relevant supporting documents are available. If supporting documents are missing, the software automatically searches for them in the medical records.

RICO, the Artificial Intelligence-based coding support software, greatly simplifies the coding and billing process and implements it in a way that withstands medical service audits. RICO was developed in accordance with the highest data protection standards, is immediately operational without additional measures necessary, and is already in use in multiple hospitals.

ML2R conducts research on hybrid ML methods that combine expert knowledge with Machine Learning. This involves, for example, linking contextual text understanding with medical knowledge as sets of rules such as treatment and disease codes or the German drug directory “Rote Liste”. The RICO software for AI-based automation of billing processes in hospitals is one result of this research.

Industry 4.0

AI and ML technologies have the potential to shorten development cycles, optimize the use of resources and make industrial production more efficient overall. We are happy to support and offer guidance to medium-sized companies, especially with respect to optimizing their processes with AI and ML methods. Our offers for cooperation hereby cover the entire value chain: from feasibility analysis to strategy development, consultation on technology deployment to sustainable implementation and continuous improvement.

Experimental Designs using AI: Accelerated Development and Optimized Use of Resources

Regardless of whether the challenge is to find new products, materials and formulations or to optimize existing ones: Our AI-based methodology helps you in the planning and optimal execution of trials. In this way, you can use your resources efficiently and save time and money.

In order to successfully compete in the global market, manufacturing companies must develop and optimize their products, materials and formulations ever faster. Traditional Designs of Experiment (DoEs), often find it difficult to guarantee high levels of velocity while maintaining the highest quality standards and optimal use of resources.

Experiments are essential for the development of new formulations and materials. Their parameters often allow for a wide range of possible combinations. The testing of each of these possibilities requires a great deal of human and material resources and is therefore time-consuming and costly.

We have developed an Artificial Intelligence-based DoE methodology that optimizes experimental designs as well as the use of resources, thus saving time and reducing costs. With our DoE methodology, we help you reach your goal faster, more reliably and more efficiently than with conventional methods.

Depending on the initial state, AI-based technology can save up to 50 percent of trials compared to traditional concepts of test planning. In addition, problems previously considered too complex too solve can be resolved using AI-based methods. Based on Bayesian optimization, our DoE methodology generates iterative experiment plans that lead you to your goal as quickly as possible.

The technology we have developed is based on our research of hybrid methods that combine Machine Learning with expert knowledge. In this way, we also support you in anchoring and optimally using existing knowledge in your company in the long term.

Data Summaries: Smart Data Summaries for More Efficient Production

For an efficient production, machine and sensor data must be collected, combined and evaluated. In a cooperative project with the company Achenbach Buschhütten, ML2R researchers developed new methods by which data from rolling mills was condensed into data summaries that can be easily analyzed and processed.

The aim of interconnecting machines and components in the context of Industry 4.0 is primarily targeted at monitoring and predicting machine behavior. This makes it possible to avoid unplanned downtime and to increase the efficiency of production. The key hereby lies within the efficient communication of machines among each other and with the cloud. In many companies, the implementation of the respective data communication is still in its early stages, but at Achenbach Buschhütten it is already firmly integrated into day-to-day business. Achenbach Buschhütten is an independent family-owned company looking back on more than 565 years of history and 125 years of experience in rolling mill equipment. The company is a pioneer in the interconnection of industrial plants with their own cloud-based platform Achenbach OPTILINK®. Achenbach Buschhütten is hence an ideal research partner for the Competence Center ML2R.

If machine data is available in large quantities through an interlinkage, it should be analyzed profitably. One challenge is that the large amounts of data can hardly be stored and analyzed in a timely manner. In addition, it is clear that a profitable analysis must always be carried out in close cooperation with the plant engineers.

Data summaries are an ideal approach to reducing the amount of data and making it comprehensible for users, such as plant engineers. A data summary is a small, well-chosen excerpt from the overall data that presents as much information and special features of the whole data set as possible. Formally, summaries can be represented using submodular functions, such as the “Informative Vector Machine”. Submodular functions provide a theoretically sound framework for the analysis of data summarization techniques while formalizing the notion of summarization in a mathematically precise way. This can then be used to select small and expressive summaries in linear time. On the other hand, the quality of the summaries and the selection process can be objectified and compared with the subjective impressions of the operating engineers.

At TU Dortmund University, methods for the summarization of data from astrophysics have been developed, which allow physicists to gain a quick overview of large measurement series. These techniques were further developed at ML2R in cooperation with Achenbach Buschhütten and adapted to the use on sensor data from rolling mills, which are available as time series. Further research on methods for data summarization is a vital part of the cooperation between Achenbach Buschhütten and the Competence Center ML2R.

Cognitive Process Automation

In any company, understanding, summarizing and categorizing texts is an essential part of many work processes. We support you in automating cognitive processes: Benefit from Natural Language Understanding to understand and interpret texts using AI. This saves you time and money and makes your workflows more efficient. Our solutions have also proven themselves to be well-suited for specialist and industry-specific texts, such as court rulings, medical documentation or maintenance reports.

Insight Financials: Generating Financial and Economic Analyses swiftly and by automation

With our tool designed for financial and business professionals, you’ll have information at your fingertips quickly in order to create automated analyses. Our search and analysis engine allows you to automatically find company reports published on the web and extract data. You are hereby able to save time when researching, analyzing and preparing financial and economic data. You are able to use this data for example for market analyses, benchmarking and your strategic corporate planning.

Thousands of company reports, such as stock exchange prospectuses and annual financial statements, are published every year. These reports are based on different legal requirements. The financial figures they contain are available in an unstructured form, are complex and can be defined differently. Therefore, manual information research for benchmarking and market analysis based on company reports is very costly and time-consuming.

Insight Financials is an intelligent, AI-based, cross-industry and cross-company tool that facilitates the work of analysts, consultants, corporate strategists and investors. Based on methods of Artificial Intelligence and Machine Learning, our tool allows for the automated sighting of financial reports from thousands of companies using autonomous web crawlers.

All information essential to you as a user is extracted and provided in real-time through an intuitive web application. The tool offers a granular search process, various filtering options and a benchmarking tool. It is also able to detect outliers and make predictions based on the extracted, temporary data. You can hence make a direct comparison between competitors, which poses valuable information for a company’s management as well as for auditors and investors.

The neural network-based analysis, or so-called parsing, of automatically downloaded company reports was developed by researchers at the Competence Center ML2R. In addition, the ML2R focuses on resource-efficient ML technologies: The language and extraction models, used for the automatic extraction of the financial figures, do not require any specialized, cost-intensive infrastructure.

RICO: Artificial Intelligence facilitates Coding and Billing of Hospital Services

The “RightCoding” (RICO) software simplifies and automates billing processes. Hospital employees use RICO to code diagnoses, seamlessly record treatments and bill health insurance providers for services. The respective processes are hence conducted in a manner that is simpler, more secure and in compliance with data protection regulations.

In order for hospitals to be able to bill health insurance providers for their services in the treatment of patients, the conducted services must be coded. This process is essential for the securing of hospital revenues. However, it demands a high degree of personnel resources and is time-consuming as well as at times prone to errors.

Together with GSG Consulting GmbH and with the participation of ML2R scientists, Fraunhofer IAIS has developed the AI-backed software RICO, which supports hospital staff in the coding process. RICO stands for “RightCoding”: It helps the hospital team to include all services when billing and checks whether all relevant supporting documents are available. If supporting documents are missing, the software automatically searches for them in the medical records.

RICO, the Artificial Intelligence-based coding support software, greatly simplifies the coding and billing process and implements it in a way that withstands medical service audits. RICO was developed in accordance with the highest data protection standards, is immediately operational without additional measures necessary, and is already in use in multiple hospitals.

ML2R conducts research on hybrid ML methods that combine expert knowledge with Machine Learning. This involves, for example, linking contextual text understanding with medical knowledge as sets of rules such as treatment and disease codes or the German drug directory “Rote Liste”. The RICO software for AI-based automation of billing processes in hospitals is one result of this research.