Patents Assigned to Data Science Evangelists, Inc.
  • Automated assessment and grading of computerized algorithms
    Patent number: 10140886
    Abstract: A grading system and method for grading a user solution to a computing assignment are presented. The method includes receiving a program code submitted by a user, wherein the received program code is the user solution to the computing assignment; activating at least one code processing engine, wherein each code processing engine is a secured isolated execution environment; executing the program code in the at least one activated code processing engine to produce an answer; determining a grade for the answer based on an expected answer and an approximate grading function, wherein the approximate grading function is determined based on a type of the computing assignment; and returning the grade to the user.
    Type: Grant
    Filed: March 23, 2016
    Date of Patent: November 27, 2018
    Assignee: DATA SCIENCE EVANGELISTS, INC.
    Inventors: Alan Du, Ariel Mndange-Pfupfu, Christian Moscardi, Tianhui Li
  • AUTOMATED ASSESSMENT AND GRADING OF COMPUTERIZED ALGORITHMS
    Publication number: 20170278421
    Abstract: A grading system and method for grading a user solution to a computing assignment are presented. The method includes receiving a program code submitted by a user, wherein the received program code is the user solution to the computing assignment; activating at least one code processing engine, wherein each code processing engine is a secured isolated execution environment; executing the program code in the at least one activated code processing engine to produce an answer; determining a grade for the answer based on an expected answer and an approximate grading function, wherein the approximate grading function is determined based on a type of the computing assignment; and returning the grade to the user.
    Type: Application
    Filed: March 23, 2016
    Publication date: September 28, 2017
    Applicant: Data Science Evangelists, Inc.
    Inventors: Alan DU, Ariel MNDANGE-PFUPFU, Christian MOSCARDI, Tianhui LI