User Interface for Input With Switches Using Machine Learned Huffman Codes

Users with special circumstances, such as limited mobility or physical strength, are often unable to utilize the normal keyboard of a device. To overcome these difficulties, these users utilize alternative mechanisms for typed input, such as a mouse, trackpad, switches, buttons, etc. These mechanisms operate by mapping the full set of possible inputs onto a limited number of buttons, which makes their use cumbersome and slow. This disclosure utilizes Huffman coding to optimize the encoding of a large set of symbols into a set of codewords based on the probability of use of each symbol, calculated via a trained machine learning model. Given a reasonably accurate machine-learned prediction model, the techniques of this disclosure ensure that generating the desired typed input can be accomplished with minimal number of switch selections

Machine-Learned Caching of Datasets

Generally, the present disclosure is directed to creating and/or modifying a pre-cache for a client device connected to a remote server containing a dataset. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict the likelihood a particular piece of data will be used (e.g. opened, edited, saved, etc.) within a time frame based on information about the data, the user’s interaction with the data, and/or the user’s schedule

Machine-Learning for Optimization of Software Parameters

Generally, the present disclosure is directed to optimizing tuning parameters in a computing system and/or software application using machine learning. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an optimal value for tuning parameters based on metrics provided by a developer. As examples, such metrics may be related to an amount of user engagement, latency associated with the application, or efficiency of executing the software application