US 11,669,353 B1
System and method for personalizing digital guidance content
Abhishek Sanghai, Bengalaru (IN); Gourav H. Dhelaria, Bangalore (IN); Raj Ganesh, Bengalaru (IN); Samvit Majumdar, Bengalaru (IN); and Maruthi Priya Kanyaka Vara Kumar Namburu, Bengalaru (IN)
Assigned to Whatfix Private Limited, Bangalore (IN)
Filed by Whatfix Private Limited, Bangalore (IN)
Filed on Dec. 10, 2021, as Appl. No. 17/643,683.
Int. Cl. H04L 67/564 (2022.01); G06F 9/451 (2018.01); H04L 67/50 (2022.01)
CPC G06F 9/453 (2018.02) [H04L 67/535 (2022.05); H04L 67/564 (2022.05)] 20 Claims
OG exemplary drawing
 
1. A method of personalizing digital guidance for use in an underlying computer application, the method comprising the steps of:
identifying an underlying application in which it is desired to provide personalized guidance content;
identifying different pages of the underlying application from which usage data will be gathered;
gathering usage data of the underlying application at a user level for n days;
creating a user behavior matrix from the gathered data with one axis of the matrix representing users of the underlying application and another axis of the matrix representing the different pages of the underlying application, wherein values in the matrix represent a predetermined measure of each of the users' behavior on the different pages;
using the behavior matrix, performing a user similarity calculation for each pair of the users to obtain a similarity number for each of the pairs of users;
tabulating a consumption count for each of the users and a particular piece of digital guidance content each user has consumed, each of the consumption counts reflecting a number of times a particular user has consumed the particular content;
using the user similarity numbers and the consumption counts, performing a series of score calculations for a recommendation user, wherein each of the score calculations is a product of one of the consumption counts and an associated one of the similarity numbers;
calculating an intermediate score for each of the pieces of content from the tabulating step, wherein each of the intermediate scores is calculated by summing the series of score calculations for each of the pieces of content;
counting a number of users who clicked on each of the pieces of content to obtain a click user count for each piece of content;
obtaining a final score for each of the pieces of content by dividing its intermediate score by its click user count;
deciding on a ranking order of the content for the recommendation user based on the final scores placed in descending order; and
recommending at least a highest ranked piece of content from the ranking step to the recommendation user.