Granted Patent B2

US 12,333,463 · App. 18/807,694 · Granted Jun 17, 2025

Inventors: Yoky Matsuoka (Los Altos Hills, CA); Nitin Viswanathan (San Francisco, CA); Gwendolyn W. van der Linden (Redwood City, CA); Amy Y. Seng (El Cerrito, CA); Lingyun Liu (Sunnyvale, CA); Benjamin Deming (Campbell, CA); Sean Paterson (Mountain View, CA)

Assignee: Panasonic Well LLC

G06Q10/063112G06Q10/0639

Abstract

Claims (80)

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.

2. The computer-implemented method of claim 1 , wherein the trained machine learning algorithm further:

generates a ranking of the set of available experiences, wherein the ranking is generated based on the set of experience preferences; and

selects the set of experience recommendations according to the ranking.

3. The computer-implemented method of claim 1 , further comprising:

generating a template corresponding to the experience recommendation, wherein the template indicates information required for the performance of the one or more tasks; and

providing the template, wherein when the template is received, the template is automatically populated based on the member profile.

4. The computer-implemented method of claim 1 , further comprising:

detecting rejection of another experience recommendation from the one or more experience recommendations; and

updating the member profile based on the rejection, wherein when the member profile is updated, a likelihood of other experience recommendations similar to the other experience recommendation being selected for the member is reduced.

5. The computer-implemented method of claim 1 , further comprising:

processing the updated member profile through the trained machine learning algorithm to generate a new set of experience recommendations, wherein the updated member profile is processed without representative interaction.

6. The computer-implemented method of claim 1 , further comprising:

automatically communicating with the member through the communications session to obtain additional information required for the one or more tasks; and

generating the one or more tasks based on the experience and the additional information.

7. The computer-implemented method of claim 1 , wherein receiving the feedback corresponding to the performance of the one or more tasks further comprises:

automatically soliciting the member through the communications session for the feedback.

8. A system, comprising:

one or more processors; and

memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:

automatically detect 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;

process a member profile associated with the member and the one or more family members to identify a set of experience preferences;

automatically query 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;

process 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;

provide the set of experience recommendations through the communications session between the member and the representative;

evaluate 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;

monitor 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;

receive 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

update 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.

9. The system of claim 8 , wherein the trained machine learning algorithm further:

generates a ranking of the set of available experiences, wherein the ranking is generated based on the set of experience preferences; and

selects the set of experience recommendations according to the ranking.

10. The system of claim 8 , wherein the instructions further cause the system to:

generate a template corresponding to the experience recommendation, wherein the template indicates information required for the performance of the one or more tasks; and

provide the template, wherein when the template is received, the template is automatically populated based on the member profile.

11. The system of claim 8 , wherein the instructions further cause the system to:

detect rejection of another experience recommendation from the one or more experience recommendations; and

update the member profile in based on the rejection, wherein when the member profile is updated, a likelihood of other experience recommendations similar to the other experience recommendation being selected for the member is reduced.

12. The system of claim 8 , wherein the instructions further cause the system to:

process the updated member profile through the trained machine learning algorithm to generate a new set of experience recommendations, wherein the updated member profile is processed without representative interaction.

13. The system of claim 8 , wherein the instructions further cause the system to:

automatically communicate with the member through the communications session to obtain additional information required for the one or more tasks; and

generate the one or more tasks based on the experience and the additional information.

14. The system of claim 8 , wherein the instructions that cause the system to receive the feedback corresponding to the performance of the one or more tasks further cause the system to:

automatically solicit the member through the communications session for the feedback.

15. A non-transitory computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:

automatically detect 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;

process a member profile associated with the member and the one or more family members to identify a set of experience preferences;

automatically query 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;

process 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;

provide the set of experience recommendations through the communications session between the member and the representative;

evaluate 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;

monitor 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;

receive 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

update 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.

16. The non-transitory computer-readable storage medium of claim 15 , wherein the trained machine learning algorithm further:

generates a ranking of the set of available experiences, wherein the ranking is generated based on the set of experience preferences; and

selects the set of experience recommendations according to the ranking.

17. The non-transitory computer-readable storage medium of claim 15 , wherein the executable instructions further cause the computer system to:

generate a template corresponding to the experience recommendation, wherein the template indicates information required for the performance of the one or more tasks; and

provide the template, wherein when the template is received, the template is automatically populated based on the member profile.

18. The non-transitory computer-readable storage medium of claim 15 , wherein the executable instructions further cause the computer system to:

detect rejection of another experience recommendation from the one or more experience recommendations; and

update the member profile based on the rejection, wherein when the member profile is updated, a likelihood of other experience recommendations similar to the other experience recommendation being selected for the member is reduced.

19. The non-transitory computer-readable storage medium of claim 15 , wherein the executable instructions further cause the computer system to:

process the updated member profile through the trained machine learning algorithm to generate a new set of experience recommendations, wherein the updated member profile is processed without representative interaction.

20. The non-transitory computer-readable storage medium of claim 15 , wherein the executable instructions further cause the computer system to:

automatically communicate with the member through the communications session to obtain additional information required for the one or more tasks; and

generate the one or more tasks based on the experience and the additional information.

21. The non-transitory computer-readable storage medium of claim 15 , wherein the executable instructions that cause the computer system to receive the feedback corresponding to the performance of the one or more tasks further cause the computer system to:

automatically solicit the member through the communications session for the feedback.

Assignments (4)

Continuity (3)

References Cited (21)