Human Readable Representation of Data

Software often generates lengthy and difficult-to-recall alphanumeric identifiers, e.g., b23fc5a0a2bd3c964910c47f6a3252bffa146. Examples of such identifiers include links to documents or web-resources, entries in databases, digital signatures, etc. The length and the seemingly arbitrary construction of the identifier makes it difficult for a human user, e.g., a programmer, to identify, memorize, or compare. This disclosure describes techniques that map lengthy alphanumeric strings to easy-to-recall icons and/or colors. The map can be a deterministic, many-to-one function, e.g., a hashing function

Automatic Personalization of User Interfaces based on User Interaction Analytics

The default user interface (UI) of software applications is the same for all users, even though users differ in terms of their needs and preferences for using the software. UI customization is typically limited to the most advanced and/or highly active users. As a result, a significant proportion of users of a software do not reap the benefits of having a UI that is personalized to them. This disclosure describes techniques to determine and present a personalized UI to each user or an application, with the user’s permission. UI personalization is performed based on analytics of user-permitted data of user interaction and other relevant information. The analysis can be performed by a suitably trained machine learning model which outputs the optimal personalized UI for each user. Model training and execution is performed on the user device, and if the user permits, on a server that trains the model based on aggregated, non-identifiable user data