A method of monitoring health status of a machine component on a real time basis is provided. The method includes generating a first signal indicative of an operational characteristic of the machine component by at least one sensor module at regular predetermined intervals. The method includes receiving the first signal by at least one Internet of Things (IoT) module. The method includes converting the first signal from analog format to digital format by the at least one IoT module to generate a second signal. The method includes transmitting the second signal wirelessly by the at least one IoT module. The method includes receiving the second signal by a mobile device. The method includes processing the second signal by the mobile device to determine a real time health status of the machine component and displaying the real time health status of the machine component on the mobile device.

Aspects of the invention are directed towards a system and a method for generating service actionable for a plurality of equipment. Embodiments of the invention describe the method comprises steps of behaviorally classifying an equipment into normalizing classification and behavior classification. The method further comprises steps of processing the normalizing and behavior classifications to generate one or more profiles corresponding to the equipment. The one or more profiles represent time-granular behavior patterns of the equipment. The method comprises steps of generating time-granular normalized characteristics for the equipment and normalizing variances of the time-granular normalized characteristics and the time-granular behavior patterns to generate possible service actionable (SACT) recommendations that are integrated into workflows to drive action and receive prediction confirmation.

Aspects of the invention are directed towards a system and a method for generating service actionable for a plurality of equipment. Embodiments of the invention describe the method comprises steps of behaviorally classifying an equipment into normalizing classification and behavior classification. The method further comprises steps of processing the normalizing and behavior classifications to generate one or more profiles corresponding to the equipment. The one or more profiles represent time-granular behavior patterns of the equipment. The method comprises steps of generating time-granular normalized characteristics for the equipment and normalizing variances of the time-granular normalized characteristics and the time-granular behavior patterns to generate possible service actionable (SACT) recommendations that are integrated into workflows to drive action and receive prediction confirmation.

An information processing apparatus includes processing circuitry configured to: acquire, from each of a plurality of electric power tools, position information of each of the plurality of electric power tools and actuation information indicating that each of the plurality of electric power tools is actuated; generate work information indicating a work status of each of the plurality of electric power tools based on the actuation information, the work status including a actuation history of each of the plurality of the electric power tools; receive designation of a region; extract, from among the plurality of electric power tools, an electric power tool of which the position information is included in the designated region; generate display data for causing a display device to display the work information of the extracted electric power tool; and output the display data.

A data management platform for Autonomous Vehicles (AVs) is provided. The data management platform can receive, from an AV at a first time, a first copy of a manifest including a creation history of a transformed object generated by the AV and a data integrity value corresponding to the transformed object. The data management platform can receive, from a second computing system at a second time, a second copy of the manifest. The data management platform can reconcile the first copy and the second copy. The data management platform can receive, from the second computing system at a third time, a request to upload the transformed object. The data management platform can validate the transformed object stored in storage of the first computing system based on the data integrity value included in the manifest.

A processor-implemented method is disclosed. The method includes: obtaining sensor readings from a plurality of sensors associated with a plurality of homes; receiving, from a computing device associated with a first one of the plurality of homes, a request for a live home score; determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes.

A processor-implemented method is disclosed. The method includes: obtaining sensor readings from a plurality of sensors associated with a plurality of homes; receiving, from a computing device associated with a first one of the plurality of homes, a request for a live home score; determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes.

A processor-implemented method is disclosed. The method includes: obtaining sensor readings from sensors associated with a first home and a plurality of second homes; determining a set of prioritized maintenance items for the first home based on comparing sensor readings from sensors associated with the plurality of second homes to identify and rank maintenance items; detecting one or more maintenance events associated with one or more of the set of the prioritized maintenance items for the first home; and outputting a live home score for the first home based on detecting the one or more maintenance events.

A processor-implemented method is disclosed. The method includes: obtaining sensor readings from sensors associated with a first home and a plurality of second homes; determining a set of prioritized maintenance items for the first home based on comparing sensor readings from sensors associated with the plurality of second homes to identify and rank maintenance items; detecting one or more maintenance events associated with one or more of the set of the prioritized maintenance items for the first home; and outputting a live home score for the first home based on detecting the one or more maintenance events.

Internet-of-Things (“IoT”) controllers built using hardened industrial technologies which improve functionality and reliability, such as a fixed-loop model in which a loop is repeated with configured time periodicity where sensors are queried, sensor responses are read, configured calculations are performed, and logic rules are evaluated resulting in decisions made and outputs activated. A variety of redundancy techniques are utilized to provide continuous non-stop operation of IoT controllers to compensate for possible hardware and software failures. Robust IoT controller redundancy also allows periodic maintenance, software updates and security patch installation without shutting down the IoT controllers.