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: 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: The invention provides a system configured to adjust a data rate of an image capturing device. The system comprises a computing device comprising a data processor, and a computer program product comprising a computer vision system for categorizing living beings having a pose that appear in a live video stream. The computer program product, when running on the data processor, receives a live video stream from the image capturing device at a first data rate, where the live video stream comprises a time slice with at least one frame comprising a living being having a pose; applies the computer vision system to the time slice for categorizing the living being, resulting in a category; and signals the image capturing device to set the live video stream at a second data rate, different from the first data rate and based upon the category.

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: 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: The invention provides a system configured to adjust a data rate of an image capturing device. The system comprises a computing device comprising a data processor, and a computer program product comprising a computer vision system for categorizing living beings having a pose that appear in a live video stream. The computer program product, when running on the data processor, receives a live video stream from the image capturing device at a first data rate, where the live video stream comprises a time slice with at least one frame comprising a living being having a pose; applies the computer vision system to the time slice for categorizing the living being, resulting in a category; and signals the image capturing device to set the live video stream at a second data rate, different from the first data rate and based upon the category.

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: 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 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 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: 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: 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: 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 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: An artificial intelligence (AI) system for determining to which social group a person belongs. The AI system includes a computer system and a computer program product which, when running on the computer system, is to: retrieve at least one image of the person belonging to a social group; labels the person, resulting in a labeled person; retrieve at least one image part showing appearance of the labeled person; subject said at least one image part to at least one trained machine learning model defined in the computer program product, the machine learning model including at least a part that is trained on a test set of annotated images which are annotated with respect to categories that correlate to social group, and determine from the at least one trained machine learning model a social group category to which the labeled person belongs.

Abstract: The invention provides a system configured to adjust a data rate of an image capturing device. The system comprises a computing device comprising a data processor, and a computer program product comprising a computer vision system for categorizing living beings having a pose that appear in a live video stream. The computer program product, when running on the data processor, receives a live video stream from the image capturing device at a first data rate, where the live video stream comprises a time slice with at least one frame comprising a living being having a pose; applies the computer vision system to the time slice for categorizing the living being, resulting in a category; and signals the image capturing device to set the live video stream at a second data rate, different from the first data rate and based upon the category.

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: 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: The invention provides an artificial intelligence (AI) system for categorizing events, said AI system comprising a first state and a second state, wherein: said AI system is in a first state for categorizing events in a first category type; upon categorizing of a first event in a predefined category of said first category type, said AI system is set to said second state, in said second state said AI system is set for categorizing subsequent events in a second category type.