Granted Patent B1

US 12,243,638 · App. 18/615,623 · Granted Mar 4, 2025

Inventors: Madison Cole Miner (Bellingham, WA); Eric Andrew Kolve (Seattle, WA); Jonathan Peter Manion (Bellingham, WA); Robert Imre Gara, Jr. (Seattle, WA)

Assignee: DexCare, Inc.

G16H40/20G16H10/60G16H50/70

Abstract

Claims (141)

1. A method for generating service offerings in a computing environment using one or more processors to execute instructions that are configured to cause actions, comprising:

determining content from a content panel based on one or more of a markup language, an encoding, or a format associated with the content panel;

determining one or more subjects associated with the content based on information included in the content and one or more evaluations of the content by a subject model;

determining a service category associated with the one or more subjects based on one or more services provided by a healthcare organization;

employing an offering model to generate an offering panel based on the service category and an availability of the one or more services, wherein the offering panel displays information associated with an available service, wherein the offering model includes a machine learning model;

evaluating the offering model based on monitoring one or more physical interactions between one or more of users and the offering panel;

employing one or more results of the evaluation to perform further actions, including:

designating the offering model for retraining based on the one or more performance metrics falling below a threshold value;

retraining the designated offering model based on one or more other metrics associated with one or more other offering models and a training model, wherein the training model includes one or more of a machine learning model or a large language model; and

employing the retrained offering model to generate one or more other offering panels for display to the one or more users; and

employing a deficiency model to compare the one or more physical interactions of the one or more users with the one or more offering panels to one or more previous physical interactions of the one or more users with one or more deficient offering panels; and

employing the comparison to determine one or more potential sources that improve relevance of the one or more offering panels for the one or more users and identify one or more causes of relevance deficiency for the one or more users.

2. The method of claim 1 , further comprising:

modifying the content in the content panel to include one or more links or references to the offering panel, wherein activating the link or reference displays the offering panel separate from the content panel.

3. The method of claim 1 , wherein generating the offering panel, further comprises:

determining a portion of the one or more users that are patients of the healthcare organization based on authenticating the portion of the one or more users with the healthcare organization;

determining patient information for the authenticated portion of the one or more users from the healthcare organization, wherein the patient information includes one or more of demographic information, a date of a previous visit, insurance information, a provider visited during a previous visit, a service category associated with a previous visit, or a patient profile; and

providing the patient information to the offering model.

4. The method of claim 1 , wherein determining the one or more subjects associated with the content, further comprises:

generating a prompt that includes one or more portions of the content for evaluation by the subject model that includes one or more large language models; and

determining the one or more subjects based on a response provided by the subject model.

5. The method of claim 1 , wherein determining the service category associated with the one or more subjects, further comprises:

determining a plurality of service categories based on a plurality of services offered by the healthcare organization; and

employing a subject-category model to determine the plurality of service categories based on one or more matches of the one or more subjects with the one or more service categories.

6. The method of claim 1 , wherein generating the offering panel further comprises:

determining one or more available services associated with the service category;

prioritizing the one or more available services based on one or more a type of visit, an availability of providers, a utilization of providers, or a location of a clinic, wherein the type of visit includes one or more of a remote visit or an in-person visit; and

generating one or more user interface controls that enables the user to request a visit associated with the one or more the available services, wherein the one or more available services are sorted or redacted based on the prioritization of the one or more available services.

7. The method of claim 1 , further comprising:

determining one or more non-deficient offering panels based on one or more physical interaction metrics that exceed one or more defined threshold values;

determining input data and one or more offering profiles associated with the one or more non-deficient offering panels;

updating training data to include the determined input data and determined one or more offering profiles; and

employing the updated training data to retrain the offering model that is designated for retraining.

8. A network computer for generating service offerings, comprising:

a memory that stores at least instructions; and

one or more processors that execute instructions that are configured to cause actions, including:

determining content from a content panel based on one or more of a markup language, an encoding, or a format associated with the content panel;

determining one or more subjects associated with the content based on information included in the content and one or more evaluations of the content by a subject model;

determining a service category associated with the one or more subjects based on one or more services provided by a healthcare organization;

employing an offering model to generate an offering panel based on the service category and an availability of the one or more services, wherein the offering panel displays information associated with an available service, wherein the offering model includes a machine learning model;

evaluating the offering model based on monitoring one or more physical interactions between one or more of users and the offering panel;

employing one or more results of the evaluation to perform further actions, including:

designating the offering model for retraining based on the one or more performance metrics falling below a threshold value;

retraining the designated offering model based on one or more other metrics associated with one or more other offering models and a training model, wherein the training model includes one or more of a machine learning model or a large language model; and

employing the retrained offering model to generate one or more other offering panels for display to the one or more users; and

employing a deficiency model to compare the one or more physical interactions of the one or more users with the one or more offering panels to one or more previous physical interactions of the one or more users with one or more deficient offering panels; and

employing the comparison to determine one or more potential sources that improve relevance of the one or more offering panels for the one or more users and identify one or more causes of relevance deficiency for the one or more users.

9. The network computer of claim 8 , wherein the one or more processors execute instructions that are configured to cause actions, further comprising:

modifying the content in the content panel to include one or more links or references to the offering panel, wherein activating the link or reference displays the offering panel separate from the content panel.

10. The network computer of claim 8 , wherein generating the offering panel, further comprises:

determining a portion of the one or more users that are patients of the healthcare organization based on authenticating the portion of the one or more users with the healthcare organization;

determining patient information for the authenticated portion of the one or more users from the healthcare organization, wherein the patient information includes one or more of demographic information, a date of a previous visit, insurance information, a provider visited during a previous visit, a service category associated with a previous visit, or a patient profile; and

providing the patient information to the offering model.

11. The network computer of claim 8 , wherein determining the one or more subjects associated with the content, further comprises:

generating a prompt that includes one or more portions of the content for evaluation by the subject model that includes one or more large language models; and

determining the one or more subjects based on a response provided by the subject model.

12. The network computer of claim 8 , wherein determining the service category associated with the one or more subjects, further comprises:

determining a plurality of service categories based on a plurality of services offered by the healthcare organization; and

employing a subject-category model to determine the plurality of service categories based on one or more matches of the one or more subjects with the one or more service categories.

13. The network computer of claim 8 , wherein generating the offering panel further comprises:

determining one or more available services associated with the service category;

prioritizing the one or more available services based on one or more a type of visit, an availability of providers, a utilization of providers, or a location of a clinic, wherein the type of visit includes one or more of a remote visit or an in-person visit; and

generating one or more user interface controls that enables the user to request a visit associated with the one or more the available services, wherein the one or more available services are sorted or redacted based on the prioritization of the one or more available services.

14. The network computer of claim 8 , wherein the one or more processors execute instructions that are configured to cause actions, further comprising:

determining one or more non-deficient offering panels based on one or more physical interaction metrics that exceed one or more defined threshold values;

determining input data and one or more offering profiles associated with the one or more non-deficient offering panels;

updating training data to include the determined input data and determined one or more offering profiles; and

employing the updated training data to retrain the offering model that is designated for retraining.

15. A processor readable non-transitory media that includes instructions configured for generating service offerings, wherein execution of the instructions by one or more processors on one or more network computers performs actions, comprising:

determining content from a content panel based on one or more of a markup language, an encoding, or a format associated with the content panel;

determining one or more subjects associated with the content based on information included in the content and one or more evaluations of the content by a subject model;

determining a service category associated with the one or more subjects based on one or more services provided by a healthcare organization;

employing an offering model to generate an offering panel based on the service category and an availability of the one or more services, wherein the offering panel displays information associated with an available service, wherein the offering model includes a machine learning model;

evaluating the offering model based on monitoring one or more physical interactions between one or more of users and the offering panel;

employing one or more results of the evaluation to perform further actions, including:

designating the offering model for retraining based on the one or more performance metrics falling below a threshold value;

retraining the designated offering model based on one or more other metrics associated with one or more other offering models and a training model, wherein the training model includes one or more of a machine learning model or a large language model; and

employing the retrained offering model to generate one or more other offering panels for display to the one or more users; and

employing a deficiency model to compare the one or more physical interactions of the one or more users with the one or more offering panels to one or more previous physical interactions of the one or more users with one or more deficient offering panels; and

employing the comparison to determine one or more potential sources that improve relevance of the one or more offering panels for the one or more users and identify one or more causes of relevance deficiency for the one or more users.

16. The media of claim 15 , further comprising:

modifying the content in the content panel to include one or more links or references to the offering panel, wherein activating the link or reference displays the offering panel separate from the content panel.

17. The media of claim 15 , wherein generating the offering panel, further comprises:

determining a portion of the one or more users that are patients of the healthcare organization based on authenticating the portion of the one or more users with the healthcare organization;

determining patient information for the authenticated portion of the one or more users from the healthcare organization, wherein the patient information includes one or more of demographic information, a date of a previous visit, insurance information, a provider visited during a previous visit, a service category associated with a previous visit, or a patient profile; and

providing the patient information to the offering model.

18. The media of claim 15 , wherein determining the one or more subjects associated with the content, further comprises:

generating a prompt that includes one or more portions of the content for evaluation by the subject model that includes one or more large language models; and

determining the one or more subjects based on a response provided by the subject model.

19. The media of claim 15 , wherein determining the service category associated with the one or more subjects, further comprises:

determining a plurality of service categories based on a plurality of services offered by the healthcare organization; and

employing a subject-category model to determine the plurality of service categories based on one or more matches of the one or more subjects with the one or more service categories.

20. The media of claim 15 , wherein generating the offering panel further comprises:

determining one or more available services associated with the service category;

prioritizing the one or more available services based on one or more a type of visit, an availability of providers, a utilization of providers, or a location of a clinic, wherein the type of visit includes one or more of a remote visit or an in-person visit; and

generating one or more user interface controls that enables the user to request a visit associated with the one or more the available services, wherein the one or more available services are sorted or redacted based on the prioritization of the one or more available services.

21. The media of claim 15 , further comprising:

determining one or more non-deficient offering panels based on one or more physical interaction metrics that exceed one or more defined threshold values;

determining input data and one or more offering profiles associated with the one or more non-deficient offering panels;

updating training data to include the determined input data and determined one or more offering profiles; and

employing the updated training data to retrain the offering model that is designated for retraining.

22. A system for generating service offerings in a computing environment:

one or more network computers, comprising:

a memory that stores at least instructions; and

one or more processors that execute instructions that are configured to cause actions, including:

determining content from a content panel based on one or more of a markup language, an encoding, or a format associated with the content panel;

determining one or more subjects associated with the content based on information included in the content and one or more evaluations of the content by a subject model;

determining a service category associated with the one or more subjects based on one or more services provided by a healthcare organization;

employing an offering model to generate an offering panel based on the service category and an availability of the one or more services, wherein the offering panel displays information associated with an available service, wherein the offering model includes a machine learning model;

evaluating the offering model based on monitoring one or more physical interactions between one or more of users and the offering panel;

employing one or more results of the evaluation to perform further actions, including:

designating the offering model for retraining based on the one or more performance metrics falling below a threshold value;

retraining the designated offering model based on one or more other metrics associated with one or more other offering models and a training model, wherein the training model includes one or more of a machine learning model or a large language model; and

employing the retrained offering model to generate one or more other offering panels for display to the one or more users; and

employing a deficiency model to compare the one or more physical interactions of the one or more users with the one or more offering panels to one or more previous physical interactions of the one or more users with one or more deficient offering panels; and

employing the comparison to determine one or more potential sources that improve relevance of the one or more offering panels for the one or more users and identify one or more causes of relevance deficiency for the one or more users; and

one or more client computers, comprising:

a memory that stores at least instructions; and

one or more processors that execute instructions that are configured to cause actions, including:

providing the content panel.

23. The system of claim 22 , wherein the one or more processors of the one or more network computers execute instructions that are configured to cause actions, further comprising:

modifying the content in the content panel to include one or more links or references to the offering panel, wherein activating the link or reference displays the offering panel separate from the content panel.

24. The system of claim 22 , wherein generating the offering panel, further comprises:

determining a portion of the one or more users that are patients of the healthcare organization based on authenticating the portion of the one or more users with the healthcare organization;

determining patient information for the authenticated portion of the one or more users from the healthcare organization, wherein the patient information includes one or more of demographic information, a date of a previous visit, insurance information, a provider visited during a previous visit, a service category associated with a previous visit, or a patient profile; and

providing the patient information to the offering model.

25. The system of claim 22 , wherein determining the one or more subjects associated with the content, further comprises:

generating a prompt that includes one or more portions of the content for evaluation by the subject model that includes one or more large language models; and

determining the one or more subjects based on a response provided by the subject model.

26. The system of claim 22 , wherein determining the service category associated with the one or more subjects, further comprises:

determining a plurality of service categories based on a plurality of services offered by the healthcare organization; and

employing a subject-category model to determine the plurality of service categories based on one or more matches of the one or more subjects with the one or more service categories.

27. The system of claim 22 , wherein generating the offering panel further comprises:

determining one or more available services associated with the service category;

prioritizing the one or more available services based on one or more a type of visit, an availability of providers, a utilization of providers, or a location of a clinic, wherein the type of visit includes one or more of a remote visit or an in-person visit; and

generating one or more user interface controls that enables the user to request a visit associated with the one or more the available services, wherein the one or more available services are sorted or redacted based on the prioritization of the one or more available services.

28. The system of claim 22 , wherein the one or more processors of the one or more network computers execute instructions that are configured to cause actions, further comprising:

determining one or more non-deficient offering panels based on one or more physical interaction metrics that exceed one or more defined threshold values;

determining input data and one or more offering profiles associated with the one or more non-deficient offering panels;

updating training data to include the determined input data and determined one or more offering profiles; and

employing the updated training data to retrain the offering model that is designated for retraining.

Assignments (1)

References Cited (64)

Cited By (1)

US 12,430,518