US 12,217,298 B2
Systems and methods for integration of calendar applications with task facilitation services
Yoky Matsuoka, Los Altos Hills, CA (US); and Nitin Viswanathan, San Francisco, CA (US)
Assigned to Yohana LLC, Palo Alto, CA (US)
Filed by Yohana LLC, Palo Alto, CA (US)
Filed on Sep. 7, 2022, as Appl. No. 17/930,302.
Claims priority of provisional application 63/241,253, filed on Sep. 7, 2021.
Prior Publication US 2023/0077130 A1, Mar. 9, 2023
Int. Cl. G06Q 30/0601 (2023.01); G06F 9/48 (2006.01); G06F 9/54 (2006.01); G06Q 10/1093 (2023.01); H04L 67/306 (2022.01); H04L 67/50 (2022.01)
CPC G06Q 30/0631 (2013.01) [G06F 9/4831 (2013.01); G06F 9/54 (2013.01); G06Q 10/1097 (2013.01); H04L 67/306 (2013.01); H04L 67/535 (2022.05)] 21 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving calendar data for a particular user of a task facilitation service through an external application programming interface (API), wherein the calendar data is associated with a calendar of a calendar application;
accessing a user model corresponding to the particular user, wherein the user model is updated based on historic activity of the particular user;
processing the calendar data and the user model using a natural-language processing (NLP) model to generate a task recommendation, wherein the task recommendation indicates one or more recommended tasks for delegation by the particular user, wherein the NLP model was initially trained with a training dataset using unsupervised training and without user supervision, and wherein the training dataset includes training data associated with other users;
transmitting an indication corresponding to the task recommendation, wherein, when the indication is received by a computing device, the computing device is enabled to approve the task recommendation;
receiving an approval to proceed with performing the one or more recommended tasks;
accessing task-execution data associated with the one or more recommended tasks, wherein the task-execution data identifies performance statuses associated with the one or more recommended tasks; and
updating the NLP model based on the task-execution data, wherein updating includes adjusting one or more weights of the NLP model using the unsupervised training and without user supervision, and wherein the one or more weights of the NLP model are adjusted until a corresponding logarithmic loss exceeds a predetermined threshold.