Abstract: The present invention relates to cleaning compositions comprising polypeptides having peptidoglycan degradation activity, as well as use of the cleaning compositions for cleaning of an item such as a textile or a surface.

Abstract: Artificial intelligence (AI) layer-based process extraction for robotic process automation (RPA) is disclosed. Data collected by RPA robots and/or other sources may be analyzed to identify patterns that can be used to suggest or automatically generate RPA workflows. These AI layers may be used to recognize patterns of user or business system processes contained therein. Each AI layer may “sense” different characteristics in the data and be used individually or in concert with other AI layers to suggest RPA workflows.

Abstract: Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).

Abstract: A method for visualizing a process map is executed by a process map server. The method includes receiving a flowchart and a step-by-step recording related to a process. Generating a process map by combining the flowchart and the step-by-step recording and displaying the process map. The process map displays a task, step, and action related to the process. A detail window shows information associated with the process, and portions of the process, in response to user input. The action is based on information from the step-by-step recording.

Abstract: The present invention relates to polypeptide variants having DNase activity, as well as detergent compositions comprising the variants, use of the variants for cleaning, and methods for obtaining the variants.

Abstract: Disclosed herein is a computing system. The computing system includes a memory and a processor. The memory stores processor executable instructions for a workflow recommendation assistant engine. The processor is coupled to the memory. The processor executes the workflow recommendation assistant engine to cause the computing device to analyze images of a user interface corresponding to user activity, execute a pattern matching of the images with respect to existing automations, and provide a prompt indicating that an existing automation matches the user activity.

Abstract: A method for visualizing a process map is executed by a process map server. The method includes receiving a flowchart and a step-by-step recording related to a process. Generating a process map by combining the flowchart and the step-by-step recording and displaying the process map. The process map displays a task, step, and action related to the process. A detail window shows information associated with the process, and portions of the process, in response to user input. The action is based on information from the step-by-step recording.

Abstract: Artificial intelligence (AI) layer-based process extraction for robotic process automation (RPA) is disclosed. Data collected by RPA robots and/or other sources may be analyzed to identify patterns that can be used to suggest or automatically generate RPA workflows. These AI layers may be used to recognize patterns of user or business system processes contained therein. Each AI layer may “sense” different characteristics in the data and be used individually or in concert with other AI layers to suggest RPA workflows.

Abstract: Method for operating a group 1 of pressure sensors which are arranged in such a manner that they can measure the pressure in a common measurement volume 2, wherein the group of pressure sensors comprises at least a first pressure sensor 1? with a first pressure measurement range and a second pressure sensor 1? with a second pressure measurement range, wherein the first and second pressure measurement ranges overlap in an overlap pressure measurement range, wherein the first and second pressure sensors are each based on an indirect pressure measurement principle and are configured to output a measurement signal calibrated to a reference gas, and wherein the method comprises the steps of: a) providing calibration data specific to the type of gas for the first measurement signal and for the second measurement signal, which calibration data describe a dependence of the first and second measurement signals on the effective pressure and on a list of types of gas, respectively; b) recording a first and a second meas

Abstract: A method for operating a group of pressure sensors is provided. First and second pressure sensors respectively have first and second pressure measurement ranges, and are arranged to measure the pressure in a common measurement volume, and have measurement ranges that overlap in a range. The method comprises: aa) reading out first and second measurement signals respectively from the first and second pressure sensors substantially simultaneously while the pressure in the common measurement volume is in the overlapping range; bb) stipulating the first measurement signal which has been read out as the adjustment point for the second pressure sensor; cc) determining at least one calibration parameter, in particular a gas-dependent calibration parameter, for the second pressure sensor as a function of the first measurement signal, as a function of the adjustment point for the second pressure sensor, as stipulated in bb), and as a function of the second measurement signal.

Abstract: Disclosed herein is a computing system. The computing system includes a memory and a processor. The memory stores processor executable instructions for a workflow recommendation assistant engine. The processor is coupled to the memory. The processor executes the workflow recommendation assistant engine to cause the computing device to analyze images of a user interface corresponding to user activity, execute a pattern matching of the images with respect to existing automations, and provide a prompt indicating that an existing automation matches the user activity.

Abstract: The present invention relates to polypeptides having hexosaminidase activity, and polynucleotides encoding the polypeptides. The invention also relates to nucleic acid constructs, vectors, and host cells comprising the polynucleotides as well as methods of producing and using the polypeptides.

Abstract: Artificial intelligence (AI)-based process identification, extraction, and automation for robotic process automation (RPA) is disclosed. Listeners may be deployed to user computing systems to collect data pertaining to user actions. The data collected by the listeners may then be sent to one or more servers and be stored in a database. This data may be analyzed by AI layers to recognize patterns of user behavioral processes therein. These recognized processes may then be distilled into respective RPA workflows and deployed to automate the processes.

Abstract: The present invention relates to cleaning compositions comprising at least one Glyco_hydro_114 glycosyl hydrolase and one or more anionic surfactants, a method for cleaning an item and use of the cleaning composition according to the invention.

Abstract: The present invention relates to polypeptides having hexosaminidase activity, and polynucleotides encoding the polypeptides. The invention also relates to nucleic acid constructs, vectors, and host cells comprising the polynucleotides as well as methods of producing and using the polypeptides.

Abstract: Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).

Abstract: Artificial intelligence (AI)-based process identification, extraction, and automation for robotic process automation (RPA) is disclosed. Listeners may be deployed to user computing systems to collect data pertaining to user actions. The data collected by the listeners may then be sent to one or more servers and be stored in a database. This data may be analyzed by AI layers to recognize patterns of user behavioral processes therein. These recognized processes may then be distilled into respective RPA workflows and deployed to automate the processes.

Abstract: Artificial intelligence (AI) layer-based process extraction for robotic process automation (RPA) is disclosed. Data collected by RPA robots and/or other sources may be analyzed to identify patterns that can be used to suggest or automatically generate RPA workflows. These AI layers may be used to recognize patterns of user or business system processes contained therein. Each AI layer may “sense” different characteristics in the data and be used individually or in concert with other AI layers to suggest RPA workflows.

Abstract: Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).

Abstract: Disclosed herein is a computing system. The computing system includes a memory and a processor. The memory stores processor executable instructions for a workflow recommendation assistant engine. The processor is coupled to the memory. The processor executes the workflow recommendation assistant engine to cause the computing device to analyze images of a user interface corresponding to user activity, execute a pattern matching of the images with respect to existing automations, and provide a prompt indicating that an existing automation matches the user activity.