Abstract: An appliance (210) to heat a liquid, the appliance (21) including: a vessel (212) to receive the liquid to be heated, the vessel (212) having a generally central upright longitudinal axis (218), a bottom housing (220), and a side wall (222) extending upwardly from the bottom housing (220), with a portion of the bottom housing (220) and side wall (222) at least partly enclosing a chamber (234) within which the liquid is heated; and a heating element (238) in thermal communication with the chamber (234) so that heat generated by the element (238) can be delivered to the liquid via convection, wherein the housing (210) includes a deformable sealing member (268) sealingly associated with the element (238) to sealingly connect the element (238) and bottom housing (220), with the member (268) inhibiting transmission of sound from the chamber (234).

Abstract: There is disclosed herein a toaster (10) including: a housing (12); a pair of opposing food guards (24a, 24b) located within the housing (12) and defining a toasting cavity (25a, 25b) for receiving foodstuff to be toasted; a pair of heating elements (28) located within the housing (12), each heating element (28) being located on a respective side of the pair of food guards (24a, 24b), wherein each food guard (24a, 24b) is spaced from the corresponding heating element (28) by a clearance (81); and wherein each food guard (24a, 24b) has an upwardly-extending portion (75c, 75d) that extends upwardly to a portion (75a, 75b) that extends transversely from the upwardly-extending portion so as to extend above the corresponding heating element (28), the transversely-extending portion (75a, 75b) of each food guard (24a, 24b) projecting away from the opposing food guard (24a, 24b).

Abstract: Techniques for generating a machine learning model to detect event instances from physical sensor data, including applying a first machine learning model to first sensor data from a first physical sensor at a location to detect an event instance, determining that a performance metric for use of the first machine learning model is not within an expected parameter, obtaining second sensor data from a second physical sensor during a period of time at the same location as the first physical sensor, obtaining third sensor data from the first physical sensor during the period of time, generating location-specific training data by selecting portions of the third sensor data based on training event instances detected using the second sensor data, training a second ML model using the location-specific training data, and applying the second ML model instead of the first ML model for detecting event instances.

Abstract: An appliance (210) to heat a liquid, the appliance (21) including: a vessel (212) to receive the liquid to be heated, the vessel (212) having a generally central upright longitudinal axis (218), a bottom housing (220), and a side wall (222) extending upwardly from the bottom housing (220), with a portion of the bottom housing (220) and side wall (222) at least partly enclosing a chamber (234) within which the liquid is heated; and a heating element (238) in thermal communication with the chamber (234) so that heat generated by the element (238) can be delivered to the liquid via convection, wherein the housing (210) includes a deformable sealing member (268) sealingly associated with the element (238) to sealingly connect the element (238) and bottom housing (220), with the member (268) inhibiting transmission of sound from the chamber (234).

Abstract: A computer system is provided that includes a server configured to store a plurality of location accounts, each location account being associated with a physical space at a recorded geospatial location. The plurality of location accounts utilize shared data definitions of a physical space parameter. Each physical space is equipped with a corresponding on-premise sensor configured to detect measured values for the physical space parameter over time and send to the server a data stream indicating the measured values. The computer system further includes a network portal, via which an authorized user for a location account can selectively choose whether to share the measured values or a summary thereof with other location accounts via the network portal.

Abstract: There is disclosed herein a toaster (10) including: a housing (12); a pair of opposing food guards (24a, 24b) located within the housing (12) and defining a toasting cavity (25a, 25b) for receiving foodstuff to be toasted; a pair of heating elements (28) located within the housing (12), each heating element (28) being located on a respective side of the pair of food guards (24a, 24b), wherein each food guard (24a, 24b) is spaced from the corresponding heating element (28) by a clearance (81); and wherein each food guard (24a, 24b) has an upwardly-extending portion (75c, 75d) that extends upwardly to a portion (75a, 75b) that extends transversely from the upwardly-extending portion so as to extend above the corresponding heating element (28), the transversely-extending portion (75a, 75b) of each food guard (24a, 24b) projecting away from the opposing food guard (24a, 24b).

Abstract: A computer system is provided that includes a server configured to store a plurality of location accounts, each location account being associated with a physical space at a recorded geospatial location. The plurality of location accounts utilize shared data definitions of a physical space parameter. Each physical space is equipped with a corresponding on-premise sensor configured to detect measured values for the physical space parameter over time and send to the server a data stream indicating the measured values. The computer system further includes a network portal, via which an authorized user for a location account can selectively choose whether to share the measured values or a summary thereof with other location accounts via the network portal.

Abstract: Techniques for generating a machine learning model to detect event instances from physical sensor data, including applying a first machine learning model to first sensor data from a first physical sensor at a location to detect an event instance, determining that a performance metric for use of the first machine learning model is not within an expected parameter, obtaining second sensor data from a second physical sensor during a period of time at the same location as the first physical sensor, obtaining third sensor data from the first physical sensor during the period of time, generating location-specific training data by selecting portions of the third sensor data based on training event instances detected using the second sensor data, training a second ML model using the location-specific training data, and applying the second ML model instead of the first ML model for detecting event instances.