Abstract: A method, apparatus, and system for efficient machine learning with query-based knowledge assistance includes determining a state of data captured by a sensor in communication with a first edge device to determine if the captured data includes data that is out of distribution based on a trained inference model of the first edge device, if it is identified that an amount of out of distribution data in the captured data is preventing the trained inference model from making an accurate prediction, communicating a request for resources to a second edge device or a server to elicit a response from the second edge device or the server including resources required to update the trained inference model, receiving the requested resources, updating the trained inference model using the received resources, and making a prediction for the received captured data using the updated, trained inference model.

Abstract: The use of heat is elemental in household applications, particularly in cooking apparatus, but they are not smart and automated, which often causes fires and other safety hazards. In fact, 42% of household fires are caused by cooking devices. In our modern household, all cooking devices require some kind of user input, such as time, temperature, food type etc. to determine how long to cook, often resulting in under/overcooking due to human error. This invention demonstrates advanced thermopile assisted remote temperature detection technology in conjunction with controlled feedback systems to effectively cook food. Extending this with the use of a computing device and “Internet Of Things,” a revolutionary cooking apparatus has been devised that can automatically calculate & control the desired temperature of any food type & amount placed in the device without need of any user input. A working prototype has been created that includes all these features.

Abstract: A system may include an image clustering engine and a cluster provision engine. The image clustering image may be configured to access a set of circuit images and cluster the circuit images into different groups via an unsupervised learning process, wherein clustering by the unsupervised learning process is invariant to each invariant property of an invariant property set. A given invariant property in the invariant property set may correspond to a given image transformation, the invariant properties in the invariant property set may be discrete, and the total number of invariant properties in the invariant property set may be finite. The cluster provision engine may be configured to provide the clustered circuit images for further processing or analysis by an electronic design automation (EDA) application.