Granted Patent B2

US 12,315,006 · App. 18/163,501 · Granted May 27, 2025

Inventors: Jill Liila (Chaska, MN); Nate Sukhtipyaroge (Eagan, MN); Margaret Anhalt (St. Paul, MN)

Assignee: MatrixCare, Inc.

G06Q40/02

Abstract

Claims (94)

1. A method, comprising:

receiving activity data describing a first activity for a financial account of a first resident in a residential care facility;

extracting, from the activity data, a first set of attributes corresponding to the first activity, comprising:

determining a first attribute of the first set of attributes by processing unstructured text associated with the first activity using one or more natural language processing techniques;

generating a first validity score by processing the first set of attributes using one or more machine learning models, wherein the first validity score indicates a probability that the first activity is valid;

in response to determining that the first validity score is below a defined threshold:

retrieving additional contextual data for the first activity; and

generating a contextual validity score based on processing the additional contextual data using one or more machine learning models; and

initiating one or more interventions for the first resident based on the first validity score and the contextual validity score.

2. The method of claim 1 , wherein determining the first attribute comprises:

identifying a field comprising natural language text describing a reason for the first activity; and

generating a vector representation of the natural language text.

3. The method of claim 2 , wherein determining the first attribute further comprises preprocessing the natural language text prior to generating the vector representation, comprising:

normalizing the natural language text; and

removing noise from the normalized natural language text.

4. The method of claim 1 , wherein the first set of attributes further comprise:

at least one attribute corresponding to a magnitude of the first activity;

at least one attribute corresponding to a recipient of the first activity;

at least one attribute corresponding to a time of the first activity;

at least one attribute corresponding to a form of the first activity; and

at least one attribute corresponding to characteristics of the first resident.

5. The method of claim 1 , wherein the one or more interventions comprise:

identifying a trusted caregiver of the first resident; and

outputting an alert to the trusted caregiver, wherein the alert comprises:

an indication of the first activity; and

a suggestion to question the first resident regarding the first activity.

6. The method of claim 1 , wherein the activity data further describes a plurality of activities for the financial account of the first resident, the method further comprising:

for each respective activity of the plurality of activities:

extracting, from the activity data, a respective set of attributes corresponding to the respective activity; and

generating a respective validity score by processing the respective set of attributes using the trained machine learning model.

7. The method of claim 1 , wherein the machine learning model was trained by:

receiving a pre-trained machine learning model previously trained based on historical data for a plurality of residents; and

generating the machine learning model by fine-tuning the pre-trained machine learning model using the activity data for the first resident.

8. The method of claim 1 , wherein the financial account of the first resident corresponds to funds managed by the residential care facility on behalf of the first resident.

9. A non-transitory computer-readable storage medium comprising computer-readable program code that, when executed using one or more computer processors, performs an operation comprising:

receiving activity data describing a first activity for a financial account of a first resident in a residential care facility;

extracting, from the activity data, a first set of attributes corresponding to the first activity, comprising:

determining a first attribute of the first set of attributes by processing unstructured text associated with the first activity using one or more natural language processing techniques;

generating a first validity score by processing the first set of attributes using a trained machine learning model, wherein the first validity score indicates a probability that the first activity is valid;

in response to determining that the first validity score is below a defined threshold, initiating one or more interventions for the first resident;

receiving second activity data describing a second activity for the financial account;

extracting, from the second activity data, a second set of attributes corresponding to the second activity; and

in response to determining that the second set of attributes fails to satisfy one or more rules for the first resident:

refraining from processing the second set of attributes using the trained machine learning model; and

initiating one or more additional interventions for the first resident.

10. The non-transitory computer-readable storage medium of claim 9 , wherein determining the first attribute comprises:

identifying a field comprising natural language text describing a reason for the first activity; and

generating a vector representation of the natural language text.

11. The non-transitory computer-readable storage medium of claim 10 , wherein determining the first attribute further comprises preprocessing the natural language text prior to generating the vector representation, comprising:

normalizing the natural language text; and

removing noise from the normalized natural language text.

12. The non-transitory computer-readable storage medium of claim 9 , wherein the first set of attributes further comprise:

at least one attribute corresponding to a magnitude of the first activity;

at least one attribute corresponding to a recipient of the first activity;

at least one attribute corresponding to a time of the first activity;

at least one attribute corresponding to a form of the first activity; and

at least one attribute corresponding to characteristics of the first resident.

13. The non-transitory computer-readable storage medium of claim 9 , wherein the one or more interventions comprise:

identifying a trusted caregiver of the first resident; and

outputting an alert to the trusted caregiver, wherein the alert comprises:

an indication of the first activity; and

a suggestion to question the first resident regarding the first activity.

14. The non-transitory computer-readable storage medium of claim 9 , wherein the activity data further describes a plurality of activities for the financial account of the first resident, the operation further comprising:

for each respective activity of the plurality of activities:

extracting, from the activity data, a respective set of attributes corresponding to the respective activity; and

generating a respective validity score by processing the respective set of attributes using the trained machine learning model.

15. The non-transitory computer-readable storage medium of claim 9 , wherein the machine learning model was trained by:

receiving a pre-trained machine learning model previously trained based on historical data for a plurality of residents; and

generating the machine learning model by fine-tuning the pre-trained machine learning model using the activity data for the first resident.

16. A method, comprising:

receiving historical data describing activity for a financial account of a first resident in a residential care facility;

extracting, from the historical data, a set of attributes corresponding to an activity, comprising:

determining a first attribute of the set of attributes by processing unstructured text associated with the activity using one or more natural language processing techniques;

training a machine learning model to generate validity scores based on the set of attributes, wherein the validity scores indicate probability that financial account activity is valid;

generating a first validity score by processing a new set of attributes using the trained machine learning model, wherein the first validity score indicates a probability that a first activity is valid;

in response to determining that the first validity score is below a defined threshold:

retrieving additional contextual data for the first activity; and

generating a contextual validity score based on processing the additional contextual data using a second machine learning model; and

initiating one or more interventions based on the first validity score and the contextual validity score.

17. The method of claim 16 , wherein determining the first attribute comprises:

identifying a field comprising natural language text describing a reason for the activity; and

generating a vector representation of the natural language text.

18. The method of claim 17 , wherein determining the first attribute further comprises preprocessing the natural language text prior to generating the vector representation, comprising:

normalizing the natural language text; and

removing noise from the normalized natural language text.

19. The method of claim 16 , wherein the set of attributes further comprise:

at least one attribute corresponding to a magnitude of the activity;

at least one attribute corresponding to a recipient of the activity;

at least one attribute corresponding to a time of the activity;

at least one attribute corresponding to a form of the activity; and

at least one attribute corresponding to characteristics of the first resident.

20. The method of claim 16 , wherein training the machine learning model comprises:

receiving a pre-trained machine learning model previously trained based on historical data for a plurality of residents; and

generating the machine learning model by fine-tuning the pre-trained machine learning model using the historical data for the first resident.

Assignments (1)

Continuity (2)

References Cited (21)