Granted Patent B2

US 12,190,384 · App. 18/616,149 · Granted Jan 7, 2025

Inventors: Damien Patton (Plano, TX); Christian Gratton (Eaton Rapids, MI)

Assignee: TRETE Inc.

G06Q40/04G06F16/215G06F16/2358G06F16/2379G06Q20/363G06Q20/3674G06Q20/389G06Q20/401G06Q20/42G06Q30/0613G06Q40/06G06Q2220/00

Abstract

Claims (67)

1. A computer-implemented method for onboarding asset data into a digital platform, the method comprising:

electronically collecting, via an artificial intelligence module, a pushed first data set into a database, the first data set purportedly, by a user, defining characteristics of an asset;

accessing unique identifiers corresponding to each of a plurality of relevant items within the first data set;

automatically and subsequent to accessing the unique identifiers, utilizing the artificial intelligence module performing an approval/rejection process on the first data set checking the validity of the plurality of relevant items from additional data sources, including:

electronically collecting a pulled second data set into the database using the unique identifiers, the second data set considered as corresponding to the asset based on the unique identifiers;

cross-referencing the first data set with the second data set;

generating first data set validation findings based on results of the cross-referencing;

electronically notifying an administrative entity of the first data set validation findings; and

accessing a first data set rejection/approval decision of the administrative entity; and

automatically, as part of a constant training cycle, training the artificial intelligence module based on the generated first data set validation findings and the accessed first data set rejection/approval decision improving data searching performance and data validating performance of the artificial intelligence module.

2. The method of claim 1 , wherein collecting the first data set comprises collecting the first data set from the user and wherein collecting the second data set comprises collecting the second data set from at least one of: a public internet or a non-public internet.

3. The method of claim 2 , further comprising:

assigning a weight to the second data set; and

determining a confidence score for the first data set validation findings based on the assigned weight.

4. The method of claim 1 , further comprising verifying quality of the first data set and the second data set prior to cross-referencing the first data set with the second data set.

5. The method of claim 1 , wherein generating first data set validation findings comprises generating a reason to reject the first data set and wherein electronically notifying an administrative entity of the first data set validation findings comprises notifying the administrative entity of the reason to reject the first data set.

6. The method of claim 1 , wherein accessing unique identifiers corresponding to each of a plurality of relevant items within the first data set comprises creating unique identifiers for a plural subset of items within the first data subset.

7. The method of claim 1 , further comprising performing a security check on the first data set prior to utilizing the artificial intelligence module performing an approval/rejection process; and

wherein utilizing the artificial intelligence module performing an approval/rejection process comprises utilizing the artificial intelligence module subsequent to the security check determining the first data is safe for further analysis.

8. The method of claim 1 , wherein electronically collecting the pushed first data set into the database comprises electronically collecting the pushed first data set purportedly, by the user, defining characteristics of a real estate property asset.

9. The method of claim 1 , wherein electronically collecting the pushed first data set into the database comprises electronically collecting an image purported, by the user, to be an image of the asset;

wherein accessing unique identifiers corresponding to each of a plurality of relevant items comprises creating an image fingerprint from the image;

wherein electronically collecting the pulled second data set comprises collecting a second image using the image fingerprint;

wherein cross-referencing the first data set with the second data set comprises comparing the image to the second image determining the similarity between the image and the second image; and

wherein generating the first data set validation findings comprising generating the first data set validating findings based on the determined similarity.

10. The method of claim 1 , wherein electronically collecting the pushed first data set into the database comprises electronically collecting an image purported, by the user, to be an image of the asset;

further comprising identifying an image descriptor within the image;

wherein accessing unique identifiers corresponding to each of a plurality of relevant items comprises formulating a search tag from the image descriptor; and

wherein electronically collecting the pulled second data set comprises collecting the second data set using the search tag.

11. The method of claim 1 , further comprising electronically collecting a pulled third data set into the database, the third data set pulled from a different data source than the second data set; and

wherein cross-referencing the first data set with the second data set comprises cross-referencing the first data set with the second data set and the third data set.

12. The method of claim 1 , wherein accessing the first data set rejection/approval decision comprises accessing a first data set approval; and

further comprising generating a report the includes the first data set and the second data set responsive to accessing the first data set approval decision.

13. The method of claim 1 , wherein electronically collecting, via an artificial intelligence module, the pushed first data set into the database comprises electronically collecting asset type data indicating an asset type of the asset into the database; and

further comprising:

electronically collecting, via an artificial intelligence module, a pushed third data set into the database, the third data set purportedly defining characteristics of another asset of the asset type;

accessing other unique identifiers corresponding to each of a plurality of other relevant items within the third data set;

automatically and subsequent to accessing the other unique identifiers, utilizing the artificial intelligence module including the improved data searching performance and data validating performance, performing an approval/rejection process on the third data set checking the validity of the plurality of other relevant items from further data sources, including:

electronically collecting a pulled fourth data set into the database using the unique identifiers;

cross-referencing the third data set with the fourth data set;

generating additional data set validation findings based on results of the cross-referencing;

electronically notifying an administrative entity of the additional data set validation findings; and

accessing an additional data set rejection/approval decision of the administrative entity corresponding to the additional data set validation findings; and

automatically, as part of a constant training cycle, further training the artificial intelligence module based on the generated additional data set validation findings and the accessed additional data set rejection/approval decision further improving the data searching performance and the data validating performance of the artificial intelligence module.

14. The method of claim 1 , wherein accessing unique identifiers corresponding to each of a plurality of relevant items within the first data set comprises finding a plurality of unique identifiers within the first data set, each unique identifier in the plurality of unique identifiers corresponding to a relevant item within the plurality of relevant items.

15. The method of claim 1 , further comprising electronically informing the user of the accuracy of the first data set at a user interface.

16. A system comprising:

one or more memories comprising instructions; and

one or more processors configured to execute the instructions, which, when executed, cause the one or more processors to onboard asset data into a digital platform, including:

electronically collect, via an artificial intelligence module, a pushed first data set into a database, the first data set purportedly, by a user, defining characteristics of the asset;

access unique identifiers corresponding to each of a plurality of relevant items within the first data set;

automatically and subsequent to accessing the unique identifiers, utilize the artificial intelligence module performing an approval/rejection process on the first data set checking the validity of the plurality of relevant items from additional data sources, including:

electronically collect a pulled second data set into the database using the unique identifiers;

cross-reference the first data set with the second data set;

generate first data set validation findings based on results of the cross-referencing;

electronically notifying an administrative entity of the first data set validation findings; and

accessing a first data set rejection/approval decision of the administrative entity; and

automatically, as part of a constant training cycle, train the artificial intelligence module based on the generated first data set validation findings and the accessed first data set rejection/approval decision improving data searching performance and data validating performance of the artificial intelligence module.

17. The system of claim 16 , wherein the instructions, which, when executed, cause the one or more processors to collect the first data set comprise instructions, which, when executed, cause the one or more processors to collect the first data set from the user; and

wherein instructions, which, when executed, cause the one or more processors to collect the second data comprise instructions, which, when executed, cause the one or more processors to collect the second data from at least one of: a public internet or a non-public internet.

18. The system of claim 17 , wherein the instructions, which, when executed, further cause the one or more processors to:

assign a weight to the second data set; and

determine a confidence score for the first data set validation findings based on the assigned weight.

19. The system of claim 16 , wherein the instructions, which, when executed, further cause the one or more processors to:

verify quality of the first data set and the second data set prior to cross-referencing the first data set with the second data set.

20. The system of claim 16 , wherein the instructions, which when executed, cause the one or more processors to generate first data set validation findings comprise instructions, which, when executed, cause the one or more processors to generate a reason to reject the first data set; and

wherein instructions, which, when executed, cause the one or more processors to electronically notify an administrative entity of the first data set validation findings comprise instructions, which, when executed, cause the one or more processors to notify the administrative entity of the reason to reject the first data set.

Assignments (1)

Continuity (13)

References Cited (6)

Cited By (3)

US 12,288,259 US 12,288,260 US 12,597,002