Abstract: A computer-implemented method for automated product recommendations personalized to a user browsing a webpage of an e-commerce website or web application via a computer device. Image data of one or more product items displayed on the webpage is captured responsive to user input, and then sent to a server over a network. A software module that executes on the server analyzes the image data and segments it into one or more of attributes, constituent items, or objects of the one or more product items. The software module also receives media content feeds that contain images representative of style preferences of the user. The software module selects one or more specific items from a product catalogue of the e-commerce website or web application based, in part, on a visual similarity to the images representative of the style preferences of the user.

Abstract: The querying application of the present invention provides method and apparatus to find related queries having the greatest-valued and/or least-valued results. Elements (or inputs) of the related queries overlap with the elements of the user query. Preferably, the querying application of the present invention enables the user to trace other queries having the greatest-valued and/or least-valued results that overlap other elements of the user query. In accordance with another aspect of the present invention, the querying application of the present invention provides method and apparatus to find non-related queries having the greatest-valued and/or least-valued results.

Abstract: An automated training system and method for providing personalized instruction or advice to a plurality of users or students in a simple, easy-to-use manner to improve their performance in their respective domain, i.e., specific filed of human activity such as sports, stock trading, gardening, etc. The system analyzes the user's performance data to determine domain-specific performance metrics and generates advice/instruction based on the performance metrics.