Abstract: The invention relates to a method for training a machine learning model to identify a subject having at least one machine readable identifier providing a subject ID, said method comprising: providing a computer vision system with an image capturing system comprising at least one image capturing device, and a reader system comprising at least one reader for reading said at least one machine readable identifier; defining said machine learning model in said computer vision system; capturing a first image using said image capturing system, said first image showing said subject; reading said subject ID using said reader system when capturing said first image, and linking said subject ID with said first image, said linking providing said first image with a linked subject ID, providing a first annotated image; capturing at least one further image showing said subject, linking said linked subject ID to said at least one further image providing at least one further annotated image, and subjecting said first annotat

Abstract: There is provided a system configured to trigger system configured to trigger a function, said system comprising: at least one actuator that is associated with said function; an image capturing sensor; a computing device comprising a data processor, and a computer program product comprising a machine learning model trained for detecting an intent of a person to operate said at least one actuator, wherein said computer program product when running on said data processor: receives at least one image from said image capturing sensor; analyzes said at least one image, the analyzing comprises: subjecting said at least one image to said machine learning model; detecting presence of said intent to operate said at least one actuator in said at least one image, and triggers said function upon detecting said intent.

Abstract: A turbine stator assembly including a guide vane assembly and a sealing ring bearing an abradable element of a dynamic sealing ring. The assembly includes pins secured to the sealing ring and cooperating with respective oblong openings formed in the guide vane assembly so as to allow a radial movement of the ring with respect to the guide vane assembly in order to compensate for differential thermal expansions. A seal is arranged so as to compensate for circumferential spaces between ring sectors forming the guide vane assembly.

Abstract: A catheter includes a hollow body which extends longitudinally and has at least one first side window and a probe emitting a wave beam, the first side window allowing the radiation of the beam in a region next to the catheter for generating imaging, the catheter also including a longitudinal guiding device allowing a transmission of a movement to an intervention element, the intervention element moving in at least an area of the beam, the intervention angle between the first intervention element and the axis of the catheter being controlled by a remote control system.

Abstract: A body language system for determining a body language message of a living being in a context comprising an artificial intelligence system, said AI system running a computer program that: retrieves an image of said living being showing body language; labels said living being in said image, resulting in a labeled living being; determines said context from said image using a trained machine learning model; determines a baseline body language of said labeled living being from said image using a trained machine learning model; adapts a trained machine learning model of said AI system using said baseline body language and said context; applies the adapted trained machine learning model of said AI system to the one image for categorizing said body language resulting in a category, and applying said category for determining said body language message.

Abstract: A device configured to transcribe an appearance of a human being, including a common housing holding an image capturing sensor, a computing device having a data processor, and a computer program product including a first machine learning model trained for detecting and labeling human beings, a second machine learning model trained for detecting appearances of human beings and a transcription module to transcribe the detected appearances of human beings to text.

Abstract: A method for identifying a scene, comprising a computing device receiving a plurality of data points corresponding to a scene; the computing device determining one or more subsets of data points from the plurality of data points that are indicative of at least one sub-scene in said scene, said at least one sub-scene displayed on a display device that is part of said scene, wherein said at least one sub-scene does not represent said scene; the computing device categorizing said scene, disregarding said at least one sub-scene, wherein the categorizing includes interpreting said scene by a computer vision system such that said at least one sub-scene is not taken into account in the categorizing of said scene.

Abstract: There is provided a device configured to transcribe an appearance of a human being, said device comprising a common housing holding an image capturing sensor, a computing device comprising a data processor, and a computer program product comprising a first machine learning model trained for detecting and labeling human beings, a second machine learning model trained for detecting appearances of human beings and a transcription module to transcribe the detected appearances of human beings to text.

Abstract: A method for identifying a scene, comprising a computing device receiving a plurality of data points corresponding to a scene; the computing device determining one or more subsets of data points from the plurality of data points that are indicative of at least one sub-scene in said scene, said at least one sub-scene displayed on a display device that is part of said scene, wherein said at least one sub-scene does not represent said scene; the computing device categorizing said scene, disregarding said at least one sub-scene, wherein the categorizing includes interpreting said scene by a computer vision system such that said at least one sub-scene is not taken into account in the categorizing of said scene.

Abstract: Methods, systems, and computer storage media for providing a marketing budget allocation service associated with an optimization and simulation engine in a data analytics system. The marketing budget allocation service is a marketing expenditure optimization service that provides marketing budget allocation intelligence and visualizations based on data analysis and business information (e.g., prior hypothesis input data or scenario input data). The marketing budget allocation service is based on a service pipeline that includes a set of allocation intelligence data processors and operations that are used to generate output that is managed (e.g., visualized, presented, and revised) using a data analytics user interface. In operation, based on analyzing marketing budget allocation data—via an allocation service analytics computation model, marketing budget allocation results data are generated.

Abstract: An automated seed treatment system is adapted for on-site operation at a retail seed distributor. A sealed seed-treater vessel is configured to apply a plurality of chemical treatments to a batch of seed based on a seed treatment recipe. A programmable system controller is electrically coupled to a pump controller of each of a plurality of pump-stations. The programmable system controller is configured to receive a material transfer indication from each of the plurality of pump-stations and issue commands to the pump controller of each pump-station in response to the seed treatment recipe.

Abstract: A body language system for determining a body language message of a living being in a context comprising an artificial intelligence system, said AI system running a computer program that: retrieves an image of said living being showing body language; labels said living being in said image, resulting in a labeled living being; determines said context from said image using a trained machine learning model; determines a baseline body language of said labeled living being from said image using a trained machine learning model; adapts a trained machine learning model of said AI system using said baseline body language and said context; applies the adapted trained machine learning model of said AI system to the one image for categorizing said body language resulting in a category, and applying said category for determining said body language message.

Abstract: The invention provides a method for marking a machine learning model, said method comprising providing a training dataset, adding at least one anomaly to said training dataset, and training said machine learning model using said training dataset. This can for instance help to protect property rights on trained neural networks.

Abstract: A device configured to monitor a vibrating object, the device comprising a common housing holding an accelerometer for sampling vibration signatures of the vibrating object, resulting in vibration samples, and a computing device comprising a data processor and a memory having stored thereon a computer program product for monitoring the vibrating object, the computer program product comprising an input module to receive the vibration samples, an analysis module to analyze the vibration samples to derive asset health scores, a machine learning model to determine asset operating ranges, and an output module to output messages, wherein the computer program product when running on the data processor causes the computing device to receive during a time interval, having an end time t1, the vibration samples from the accelerometer, resulting in a time series vector array, and to analyze the vibration samples comprising deriving from the time series vector array a baseline asset health score and deriving from the time

Abstract: The invention provides a method for marking a machine learning model, said method comprising providing a training dataset, adding at least one anomaly to said training dataset, and training said machine learning model using said training dataset. This can for instance help to protect property rights on trained neural networks.

Abstract: A body language system for determining a body language message of a living being in a context comprising an artificial intelligence system, said AI system running a computer program that: retrieves an image of said living being showing body language; labels said living being in said image, resulting in a labeled living being; determines said context from said image using a trained machine learning model; determines a baseline body language of said labeled living being from said image using a trained machine learning model; adapts a trained machine learning model of said AI system using said baseline body language and said context; applies the adapted trained machine learning model of said AI system to the one image for categorizing said body language resulting in a category, and applying said category for determining said body language message.

Abstract: The invention relates to a method for training a machine learning model to identify a subject having at least one machine readable identifier providing a subject ID, said method comprising: providing a computer vision system with an image capturing system comprising at least one image capturing device, and a reader system comprising at least one reader for reading said at least one machine readable identifier; defining said machine learning model in said computer vision system; capturing a first image using said image capturing system, said first image showing said subject; reading said subject ID using said reader system when capturing said first image, and linking said subject ID with said first image, said linking providing said first image with a linked subject ID, providing a first annotated image; capturing at least one further image showing said subject, linking said linked subject ID to said at least one further image providing at least one further annotated image, and subjecting said first annotat

Abstract: The invention relates to a method for training a machine learning model to identify a subject having at least one machine readable identifier providing a subject ID, said method comprising: providing a computer vision system with an image capturing system comprising at least one image capturing device, and a reader system comprising at least one reader for reading said at least one machine readable identifier; defining said machine learning model in said computer vision system; capturing a first image using said image capturing system, said first image showing said subject; reading said subject ID using said reader system when capturing said first image, and linking said subject ID with said first image, said linking providing said first image with a linked subject ID, providing a first annotated image; capturing at least one further image showing said subject, linking said linked subject ID to said at least one further image providing at least one further annotated image, and subjecting said first annotat

Abstract: A catheter includes a hollow body which extends longitudinally and has at least one first side window and a probe emitting a wave beam, the first side window allowing the radiation of the beam in a region next to the catheter for generating imaging, the catheter also including a longitudinal guiding device allowing a transmission of a movement to an intervention element, the intervention element moving in at least an area of the beam, the intervention angle between the first intervention element and the axis of the catheter being controlled by a remote control system.