US 12,333,463 B2
Automated recommendation and curation of tasks for experiences
Yoky Matsuoka, Los Altos Hills, CA (US); Nitin Viswanathan, San Francisco, CA (US); Gwendolyn W. van der Linden, Redwood City, CA (US); Amy Y. Seng, El Cerrito, CA (US); Lingyun Liu, Sunnyvale, CA (US); Benjamin Deming, Campbell, CA (US); and Sean Paterson, Mountain View, CA (US)
Assigned to Panasonic Well LLC, Palo Alto, CA (US)
Filed by Yohana LLC, Palo Alto, CA (US)
Filed on Aug. 16, 2024, as Appl. No. 18/807,694.
Application 18/807,694 is a continuation of application No. 17/741,549, filed on May 11, 2022.
Claims priority of provisional application 63/188,396, filed on May 13, 2021.
Prior Publication US 2024/0412127 A1, Dec. 12, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 10/0631 (2023.01); G06Q 10/0639 (2023.01)
CPC G06Q 10/063112 (2013.01) [G06Q 10/0639 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
automatically detecting a request for one or more experience recommendations for a member and one or more family members associated with the member, wherein the request is detected by using natural language processing to evaluate different communications exchanged over a communications session between the member and a representative;
processing a member profile associated with the member and the one or more family members to identify a set of experience preferences;
automatically querying in real-time a resource library to identify a set of available experiences, wherein the set of available experiences is implemented to generate experience recommendations for reducing levels of stress associated with different members;
processing the set of available experiences and the set of experience preferences through a trained machine learning algorithm to generate a set of experience recommendations, wherein the trained machine learning algorithm is trained using a dataset of sample experience recommendations and sample member profiles, and wherein the set of experience recommendations corresponds to one or more available experiences selected according to the set of experience preferences;
providing the set of experience recommendations through the communications session between the member and the representative;
evaluating in real-time new communications exchanged over the communications session to detect selection of an experience recommendation from the set of experience recommendations, wherein the selection is an indication of a request to curate a corresponding experience for the member and the one or more family members;
monitoring performance of one or more tasks corresponding to the experience, wherein the one or more tasks are performed on behalf of the member and the one or more family members;
receiving feedback corresponding to the performance of the one or more tasks, wherein the feedback is received through the communications session, and wherein the feedback includes an indication of whether the experience resulted in a positive outcome for the member and the one or more family members; and
updating the member profile and the trained machine learning algorithm according to the feedback, wherein the trained machine learning algorithm is updated to generate new experience recommendations that have a higher likelihood of being selected by different members.