Abstract: A method for multiple sensor calibration and tracking, the method including identifying a non-permanent object in a first field of detection (FOD) of a sensor, wherein the sensor is one of a plurality of sensors positioned in various locations at a site, each sensor has a corresponding FOD; tracking the identified object by single-sensor tracking in the first FOD; predicting appearance of the object, and based on the predictions initiating single-sensor tracking in at least one other FOD where it is predicted that the object will appear; and outputting data captured from the tracked FODs.

Abstract: A method for multiple sensor calibration and tracking, the method including identifying a non-permanent object in a first field of detection (FOD) of a sensor, wherein the sensor is one of a plurality of sensors positioned in various locations at a site, each sensor has a corresponding FOD; tracking the identified object by single-sensor tracking in the first FOD; predicting appearance of the object, and based on the predictions initiating single-sensor tracking in at least one other FOD where it is predicted that the object will appear; and outputting data captured from the tracked FODs.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules: and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A method for AI training at an end device placed in an end-user premises, the method comprising: receiving by a processor of the end device sensor data captured by a sensor bundle of the end device; deciding if the sensor data included a data unit that requires external feedback, and in case external feedback is required obtaining external feedback about classification of the data unit; updating a decision module controlled by the processor of the end device based on the obtained feedback; and making a decision about the received sensor data, by the decision module. The external feedback is obtained from an end user or from another device. The method includes obtaining instructions about the type of decision to make, from a user or by pre-configuration. External feedback about the decision is obtained and the decision module is updated based on the feedback.

Abstract: A system for dependent monitoring and caregiving, the system comprising: an image sensor for capturing image data of a scene in which a dependent is situated; a controller configured to control various modules in the environment of the dependent; and a processor configured to tun a decision module configured to detect a state of the dependent based on image data and to execute code instructions for: receiving image data from the image sensor; analyzing the received image data by the decision module and detect a state of the dependent based on the analysis; instruct the controller to control various modules in the environment of the dependent in response to the detected state; and generate and output a report about the dependent state.

Abstract: A robotic system (“new robot”) operative for performing at least one task in an environment, the system comprising: learn-from-predecessor functionality governed by a data exchange protocol, which controls short-range wireless knowledge transfer from a short-range wireless transmitter in a predecessor robot system (“old robot”) to a short-range wireless receiver in said robotic system, said knowledge comprising at least one environment-specific datum previously stored by the predecessor robot.

Abstract: A robotic system (“new robot”) operative for performing at least one task in an environment, the system comprising: learn-from-predecessor functionality governed by a data exchange protocol, which controls short-range wireless knowledge transfer from a short-range wireless transmitter in a predecessor robot system (“old robot”) to a short-range wireless receiver in said robotic system, said knowledge comprising at least one environment-specific datum previously stored by the predecessor robot.

Abstract: A raw-data analysis system and method, the system including an image sensor configured to capture and transmit raw image data, a decision module configured to receive from the image sensor raw image data unprocessed by an image signal processor, and decide whether the raw image data implies an activity of interest, and at least one controller configured to control what subset of the raw image data the image sensor transmits to the decision module. Additionally, an image data analysis system including a photo-detector, a sensor module configured to detect a state of interest, an image sensor configured to capture and transmit raw image data, and a controller configured to receive an illumination level value from the photo-detector and to control the image sensor to operate with settings that match the received illumination level value once the sensor module detects a state of interest.

Abstract: Disclosed are systems and devices for facilitating audiovisual communication. According to embodiments, there may be provided a communication device including or otherwise functionally associated with a video camera to acquire visual information from a scene and to convert the visual information into a video stream. A video processing circuit may (a) receive the video stream, (b) extract user related features from the video stream; and (c) detect and characterize one or more user actions. A controller to receive the video processing circuit output and responsive to an indication of one or more detected events within the video stream to trigger a communication session between said communication device and an addressee device, wherein different addressee devices may be associated with different detected actions.

Abstract: A system and method for training a learner based on private data, the method including receiving by a processor private unannotated data from a private unannotated dataset, the private unannotated data is inaccessible for annotation from outside the processor or by a user interface of the processor and training a learner engine based on the received private data.

Abstract: A system and method for accent invariant speech recognition comprising: maintaining a database scoring a set of language units in a given language, and for each of the language units, scoring audio samples of pronunciation variations of the language unit pronounced by a plurality of speakers; extracting and storing m the database a feature vector for locating each of the audio samples in a feature space; identifying pronunciation variation distances, which are distances between locations of audio samples of the same language unit in the feature space, and inter-unit distances, which are distances between locations of audio samples of different language units in the feature space; calculating a transformation applicable on the feature space to reduce the pronunciation variation distances relative to the inter-unit distances; and based on the calculated transformation, training a processor to classify as a same language unit pronunciation variations of the same language unit.

Abstract: A method of controlling output of a neural network, the method including receiving or training the neural network; wherein the neural network is an application executed on a computer that receives input from sensors and provides an output comprising predictions and/or decisions based on the input, identifying a region of the neural network that contains information of interest, finding within the identified region a specific node or group of nodes that contains specific information of interest; and applying a manipulation application external to the neural network to operate on and alter the output of the specific node or group of nodes within the neural network; wherein the altered output of the specific node affects the output of the neural network without altering the input of the neural network.

Abstract: This application discloses systems for generating a searchable real-scene database that includes records indicating entities and physical events occurring within the real-scene. The system includes (i) a camera feed interface to receive a camera feed from the cameras observing the real-scene; (ii) an image processing circuitry including a static scene analysis unit to: (a) extract features of entities appearing in a camera feed, (b) recognize entity types of entities appearing in the camera feeds, (c) assign an entity designator to a specific entity appearing in the camera feeds, (d) generate an entity designator record for a uniquely identifiable entity, and generate an attribute record for a uniquely identifiable entity; and (iii) a scene dynamics analysis unit to: (a) detect entity movements, (b) detect entity speech, (c) characterize entity actions based on detected entity movement or entity speech, and (d) generate a record indicting an action taken by an entity.

Abstract: This application discloses a system for generating a searchable real-scene database which includes records indicating entities and physical events occurring within the real-scene. The system includes: (i) a camera feed interface to receive a camera feed from each of one or more cameras observing the real-scene; (ii) an image processing circuitry that includes: (1) a static scene analysis unit (2) a scene dynamics analysis unit; (iii) a scene data query engine to search and retrieve records from said digital data storage in accordance with a user query received through a public user interface; (iv) a real-time scene query interface to receive a user scene query to be resolved using real-time camera feeds; and (v) a real-time scene query controller to in invoke and control one or more scene camera(s) while assessing at least one output from said scene analysis units in accordance with a user scene query.

Abstract: This application discloses to methods, circuits, devices, assemblies and systems for generating a searchable real-scene database including records indicating entities and physical events occurring within the real-scene. The said system may include a camera feed interface to receive a camera feed from each of one or more cameras observing the real-scene. It may also include image processing circuitry including a static scene analysis unit to: (a) extract features of entities appearing in a camera feed, (b) recognize entity types of entities appearing in the one or more camera feeds, (c) assign an entity designator to a specific entity appearing in the camera feeds, (d) generate an entity designator record for a uniquely identifiable entity, and generate an attribute record for a uniquely identifiable entity.