Granted Patent B2

US 12,248,756 · App. 17/758,276 · Granted Mar 11, 2025

Inventors: Howard Hugh Hamilton (Atlanta, GA); Terry Woodford (Kennesaw, GA)

Assignee: Equifax Inc.

G06F40/40G06F40/284G06F40/295

Abstract

Claims (81)

1. A computer-implemented method in which one or more processing devices performs operations comprising:

accessing unstructured data;

determining a natural language processing (NLP) configuration;

generating a first set of NLP procedures based on the natural language processing configuration;

generating a plurality of predictor variables by applying the first set of NLP procedures on the unstructured data;

generating an attribute pool that comprises the generated plurality of predictor variables;

executing a machine-learning prediction model on at least the plurality of predictor variables in the attribute pool to generate a prediction result;

evaluating a predictive power of each of the plurality of predictor variables based on the prediction result;

updating the NLP configuration;

generating one or more additional NLP procedures not present in the first set of NLP procedures;

generating one or more additional predictor variables by applying the one or more additional NLP procedures;

adding to the attribute pool at least one of the generated one or more additional predictor variables containing a predictive power higher than the predictive power of at least one of the plurality of predictor variables;

retaining at least one predictor variable from the predictor variables within the attribute pool that is predictive as an input predictor variable for the machine-learning prediction model;

training the machine-learning prediction model trained using predictor variables comprising the at least one predictor variable; and

transmitting, to a remote computing device, a risk indicator for a target entity generated by the trained machine-learning prediction model, wherein the risk indicator is usable for controlling access to one or more interactive computing environments by the target entity.

2. The computer-implemented method of claim 1 , further comprising, prior to applying any NLP procedure on the unstructured data, processing the unstructured data by applying one or more of:

a context-independent normalization process;

a context-dependent normalization process;

a tokenization process;

a stop word filtering process; or

a stemming and lemmatization process.

3. The computer-implemented method of claim 1 , wherein the one or more NLP procedures used to generate any predictor variable comprises at least one of a word embedding procedure, a bag-of-word procedure, a named entity recognition procedure, or an information extraction procedure.

4. The computer-implemented method of claim 1 , wherein the operations further comprise:

executing the machine-learning prediction model on the additional predictor variables in the attribute pool to generate additional prediction results.

5. The computer-implemented method of claim 1 , wherein a predictor variable is predictive if the predictive power of the predictor variable satisfies a criterion for predictiveness and is otherwise not predictive.

6. The computer-implemented method of claim 1 , wherein the predictive power of a predictor variable is determined by calculating statistics based on prediction results, the statistics comprising one or more of statistical significance, Kolmogorov-Smirnov (KS) statistics, or Gini statistics.

7. The computer-implemented method of claim 6 , wherein a predictor variable is predictive if the calculated statistics of the predictor variable satisfies a criterion for predictiveness determined by a threshold value of the statistics.

8. A system for generating predictor variables from unstructured data, the system comprising:

one or more processing device; and

one or more non-transitory computer-readable medium communicatively coupled to the one or more processing device, wherein the one or more processing devices are configured to execute program code stored in the non-transitory computer-readable medium and thereby perform operations comprising:

processing the unstructured data to generate processed data;

determining a natural language processing (NLP) configuration;

generating a first set of NLP procedures based on the natural language processing configuration;

generating a plurality of predictor variables by applying the first set of NLP procedures on the unstructured data;

generating an attribute pool that comprises the generated plurality of predictor variables;

executing a prediction model on at least the plurality of predictor variables in the attribute pool to generate a prediction result;

evaluating a predictive power of each of the plurality of predictor variables based on the prediction result;

updating the NLP configuration;

generating one or more additional NLP procedures not present in the first set of NLP procedures;

generating one or more additional predictor variables by applying the one or more additional NLP procedures;

adding to the attribute pool at least one of the generated one or more additional predictor variables; and

retaining at least one predictor variable from the predictor variables within the attribute pool as an input predictor variable for the prediction model.

9. The system of claim 8 , wherein processing the unstructured data comprises applying one or more of:

a context-independent normalization process;

a context-dependent normalization process;

a tokenization process;

a stop word filtering process; or

a stemming and lemmatization process.

10. The system of claim 8 , wherein the one or more NLP procedures used to generate any predictor variable comprises at least one of a word embedding procedure, a bag-of-word procedure, a named entity recognition procedure, or an information extraction procedure.

11. The system of claim 8 , wherein the operations further comprise:

training a machine-learning prediction model trained using predictor variables comprising the at least one predictor variable; and

transmitting, to a remote computing device, a risk indicator for a target entity generated by the trained machine-learning prediction model, wherein the risk indicator is usable for controlling access to one or more interactive computing environments by the target entity.

12. The system of claim 11 , wherein a predictor variable is predictive if the predictive power of the predictor variable satisfies a criterion for predictiveness and is otherwise not predictive.

13. The system of claim 8 , wherein the predictive power of a predictor variable is determined by calculating statistics based on prediction results, the statistics comprising one or more of statistical significance, Kolmogorov-Smirnov (KS) statistics, or Gini statistics.

14. A non-transitory computer-readable medium having instructions stored thereon that are executable by a processor to causing a computing device to perform operations, the operations comprising:

accessing unstructured data;

processing the unstructured data to generate processed data;

determining a natural language processing (NLP) configuration;

generating a first set of NLP procedures based on the natural language processing configuration;

generating a plurality of predictor variables by applying the first set of NLP procedures on the unstructured data;

generating an attribute pool that comprises the generated plurality of predictor variables;

executing a prediction model on at least the plurality of predictor variables in the attribute pool to generate a prediction result;

evaluating a predictive power of each of the plurality of predictor variables based on the prediction result;

updating the NLP configuration;

generating one or more additional NLP procedures not present in the first set of NLP procedures;

generating one or more additional predictor variables by applying the one or more additional NLP procedures;

adding to the attribute pool at least one of the generated one or more additional predictor variables containing a predictive power higher than the predictive power of at least one of the plurality of predictor variables; and

retaining at least one predictor variable from the predictor variables within the attribute pool that is predictive as an input predictor variable for the prediction model.

15. The non-transitory computer-readable medium of claim 14 , wherein processing the unstructured data comprises applying one or more of:

a context-independent normalization process;

a context-dependent normalization process;

a tokenization process;

a stop word filtering process; or

a stemming and lemmatization process.

16. The non-transitory computer-readable medium of claim 14 , wherein the one or more NLP procedures used to generate the plurality of predictor variables comprises at least one of a word embedding procedure, a bag-of-word procedure, a named entity recognition procedure, or an information extraction procedure.

17. The non-transitory computer-readable medium of claim 16 , wherein the operations further comprise:

training a machine-learning prediction model trained using predictor variables comprising the at least one predictor variable; and

transmitting, to a remote computing device, a risk indicator for a target entity generated by the trained machine-learning prediction model, wherein the risk indicator is usable for controlling access to one or more interactive computing environments by the target entity.

18. The non-transitory computer-readable medium of claim 17 , wherein a predictor variable is predictive if the predictive power of the predictor variable satisfies a criterion for predictiveness and is otherwise not predictive.

19. The non-transitory computer-readable medium of claim 14 , wherein the predictive power of a predictor variable is determined by calculating statistics based on prediction results, the statistics comprising one or more of statistical significance, Kolmogorov-Smirnov (KS) statistics, or Gini statistics.

20. The non-transitory computer-readable medium of claim 19 , wherein a predictor variable is predictive if the calculated statistics of the predictor variable satisfies a criterion for predictiveness determined by a threshold value of the statistics.

Assignments (1)

Continuity (2)

References Cited (14)