Abstract: An air convection system configured for use in building structure having a suspended ceiling dividing the building structure to an upper plenum in airflow communication with an HVAC unit, and a lower area, said air convection system comprising; an air convection module having a plenum opening configured to face said plenum, a lower area opening configured to face said lower area; a controllable airflow facilitating unit configured to selectively facilitate airflow at least form said plenum opening to said lower area opening; and a control system configured to receive an operational signal indicative of at least one operational parameter of said HVAC unit, and operate said airflow facilitating unit in accordance with said operational signal.

Abstract: Controller apparatus operative in conjunction with a climate control system including at least first and second climate control elements, the controller apparatus comprising a hardware processor providing hysteresis coupling between the at least first and second climate control elements, including providing at least one first hysteresis plan controlling the first climate control element, and at least one second hysteresis plan controlling the second climate control element. The first and second hysteresis plans each have at least one parameter, and at least one parameter of the second hysteresis plan is/are coupled to at least one parameter of the first hysteresis plan by computing the at least one parameter/s of the second hysteresis plan as a function of at least one parameter/s of the first hysteresis plan.

Abstract: A smart home system comprising a data repository defining categories or groupings of smart home appliances or devices, and storing, for each individual appliance, an indication of a controller which governs the individual appliance; a user interface which may present a menu of the categories and which may enable a smart home end-user to enter a selection of a command and an individual category from among the categories to which the command is to be applied; and a hardware processor which, responsive to the selection, retrieves from the data repository the indication of the controller which governs each appliance in the individual category, to define a set of controllers to which the command is to be communicated, and communicates the command to all controllers in the set.

Abstract: A central hub and database for a smart home environment enable the learning of states associated with items within the smart home and the training one or more machine-learned models associated with the items. After training the machine-learned models, the central hub can modify a state of an item based on the machine-learned model associated with the item. For instance, a window can be opened or shut, a light can be dimmed or turned off, and a door can be locked. Each state of the object can be associated with a set of conditions that, when satisfied, cause the central hub to change the state of the object using the corresponding machine-learned model, for instance without receiving an explicit input from a user.

Abstract: A smart home control method comprising using at least one hardware processor to perform the following: generating an initial set of recommendations for, and/or operative limitations on, home control actions; measuring wellbeing indexes aka scores and using the scores as feedback to determine which home control actions improve the scores including introducing perturbations of device operation including applying at least one perturbation to at least one parameter of at least one home appliance; measuring the perturbations' effect on the scores, and further optimizing of house management to yield increased wellbeing by retaining post-perturbation values of at least one individual parameter of at least one individual home appliance, which yielded increased wellbeing relative to pre-perturbation values of the individual parameter of the individual home appliance.

Abstract: A method for generating data regarding individuals in an area of interest including operating a radar system which may be deployed in the area of interest, to provide a radar image including raw radar data; and/or using a hardware processor configured to store a trained model for analyzing the radar image, thereby to generate object recognition data, wherein the raw radar data generated by the radar system both undergoes signal processing, thereby to generate processed radar data which is used for said training, and is used directly, without signal processing, for training said model.

Abstract: A central hub and database for a smart home environment enable the learning of states associated with items within the smart home and the training one or more machine-learned models associated with the items. After training the machine-learned models, the central hub can modify a state of an item based on the machine-learned model associated with the item. For instance, a window can be opened or shut, a light can be dimmed or turned off, and a door can be locked. Each state of the object can be associated with a set of conditions that, when satisfied, cause the central hub to change the state of the object using the corresponding machine-learned model, for instance without receiving an explicit input from a user.

Abstract: An autonomous molecular computer that, when coupled to a molecular model of a disease, is capable of disease diagnosis. The computer preferably performs such diagnosis by detecting one or more disease markers. For example, optionally and preferably the molecular computer checks for the presence of over-expressed, under-expressed and mutated genes, applies programmed medical knowledge to this information to reach a diagnostic decision.