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US-12626302-B2 - Data retrieval and validation for private credit asset onboarding

US12626302B2US 12626302 B2US12626302 B2US 12626302B2US-12626302-B2

Abstract

In certain aspects of the disclosure, a computer-implemented method includes collecting, via an artificial intelligence module, a first set of data associated with a private credit asset and creating identifiers associated with the first set of data. The method includes collecting a second set of data associated with the private credit asset based on the identifiers and comparing the first set of data and the second set of data based on the identifiers. The method includes determining validation of the first set of data based on the comparison and generating a result of approval or rejection based on the comparison.

Inventors

  • Damien Patton
  • Christian Gratton

Assignees

  • TRETE Inc.

Dates

Publication Date
20260512
Application Date
20251114

Claims (14)

  1. 1 . A computer-implemented method comprising: electronically collecting a first data set, purportedly defining characteristics of a private credit asset, into a database; accessing unique identifiers corresponding to each of one or more items within the first data set; formulating an object containing the one or more items; assigning an additional unique identifier to the private credit asset; creating a job on a queue containing the additional unique identifier and the object; and pinging an artificial intelligence module there is an object in the queue; automatically and subsequent to accessing the unique identifiers, utilizing the artificial intelligence module validating relevancy of the one or more items to the private credit asset, including: sending out an asynchronous search for matches on the one or more items contained in the object electronically collecting a second data set into the database using the unique identifiers, the second data set considered as corresponding to the private credit asset based on the unique identifiers; cross-referencing the first data set with the second data set; and confirming relevancy of the one or more items to the private credit asset based on results of the cross-referencing; listing the private credit asset at a financial exchange; and automatically, as part of a constant training cycle, training the artificial intelligence module based on confirming the relevancy of the one or more items improving data validating performance of the artificial intelligence module.
  2. 2 . The method of claim 1 , further comprising: receiving user permission permitting a third-party system to provide the first data set; and deploying an extraction model into the third-party system; and wherein electronically collecting a first data set comprises utilizing the extraction model pushing the first data set from the third-party system into the database.
  3. 3 . The method of claim 1 , wherein electronically collecting a second data set into the database comprises: pulling a part of the second data set from a public data source into the database; and pulling another part of the second data set from a non-public data source into a database; wherein cross-referencing the first data set with the second data set comprises cross-referencing the first data set with the part of the second data set and cross-referencing the first data set with the other part of second data; and wherein confirming relevancy of the one or more items comprises confirming the veracity of the one or more items.
  4. 4 . The method of claim 1 , wherein confirming relevancy of the one or more items to the private credit asset comprises a model: scoring the cross-referencing by generating a confidence score representing a probability the second data set is a fit for the first data set; and determining the confidence score satisfies a control threshold.
  5. 5 . The method of claim 1 , wherein the artificial intelligence module sending out an asynchronous search for matches comprises the artificial intelligence module sending out an asynchronous search across multiple search engines; and further comprising utilizing pattern matching on returned links determining authoritativeness of corresponding data sources utilized to collect the second data set.
  6. 6 . The method of claim 1 , further comprising electronically collecting a third data set into the database, the third data set collected 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.
  7. 7 . 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.
  8. 8 . A system comprising: a processor; system memory coupled to the processor and storing instructions configured to cause the processor to: electronically collect a first data set, purportedly defining characteristics of a private credit asset, into a database; access unique identifiers corresponding to each of one or more items within the first data set; formulate an object containing the one or more items; assign an additional unique identifier to the private credit asset; create a job on a queue containing the additional unique identifier and the object; and ping an artificial intelligence module there is an object in the queue; automatically and subsequent to accessing the unique identifiers, utilize the artificial intelligence module validating relevancy of the one or more items to the private credit asset, including: send out an asynchronous search for matches on the one or more items contained in the object electronically collecting a second data set into the database using the unique identifiers, the second data set considered as corresponding to the private credit asset based on the unique identifiers; cross-reference the first data set with the second data set; and confirm relevancy of the one or more items to the private credit asset based on results of the cross-referencing; list the private credit asset at a financial exchange; and automatically, as part of a constant training cycle, train the artificial intelligence module based on confirming the relevancy of the one or more items improving data validating performance of the artificial intelligence module.
  9. 9 . The system of claim 8 , further comprising instructions configured to cause the processor to: receiving user permission permitting a third-party system to provide the first data set; and deploying an extraction model into the third-party system; and wherein electronically collecting a first data set comprises utilizing the extraction model pushing the first data set from the third-party system into the database.
  10. 10 . The system of claim 8 , wherein instructions configured to cause the processor to electronically collect a second data set into the database comprise instructions configured to cause the processor to: pull a part of the second data set from a public data source into the database; and pull another part of the second data set from a non-public data source into a database; wherein instructions configured to cause the processor to cross-reference the first data set with the second data set comprise instructions configured to cause the processor to cross-reference the first data set with the part of the second data set and cross-reference the first data set with the other part of second data; wherein instructions configured to cause the processor to confirm relevancy of the one or more items comprise instructions configured to cause the processor to confirm the veracity of the one or more items.
  11. 11 . The system of claim 8 , wherein instructions configured to cause the processor to confirm relevancy of the one or more items to the private credit asset comprise instructions for a model configured to cause the processor to: score the cross-referencing by generating a confidence score representing a probability the second data set is a fit for the first data set; and determine the confidence score satisfies a control threshold.
  12. 12 . The system of claim 8 , wherein instructions configured to cause the processor to cause the artificial intelligence module to send out an asynchronous search for matches comprises the instructions configured to cause the processor to cause the artificial intelligence module to send out an asynchronous search across multiple search engines; and further comprising instructions configured to cause the processor to utilize pattern matching on returned links determining authoritativeness of corresponding data sources utilized to collect the second data set.
  13. 13 . The system of claim 8 , further comprising instructions configured to cause the processor to electronically collect a pulled third data set into the database, the third data set collected from a different data source than the second data set; and wherein instructions configured to cause the processor to cross-reference the first data set with the second data set comprise instructions configured to cause the processor to cross-reference the first data set with the second data set and the third data set.
  14. 14 . The system of claim 8 , further comprising instructions configured to cause the processor 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.

Description

CROSS REFERENCE TO RELATED APPLICATIONS The present application is a continuation of U.S. patent application Ser. No. 19/180,465, entitled “Data Retrieval And Validation For Asset Onboarding”, filed Apr. 16, 2025, which is incorporated herein in its entirety. U.S. patent application Ser. No. 19/180,465 is a continuation of U.S. patent application Ser. No. 18/963,508, now U.S. Pat. No. 12,299,743, entitled “Data Retrieval And Validation For Asset Onboarding”, filed Nov. 28, 2024, which is incorporated herein in its entirety. U.S. patent application Ser. No. 18/963,508, is a continuation of U.S. patent application Ser. No. 18/616,149, now U.S. Pat. No. 12,190,384, entitled “Data Retrieval And Validation For Asset Onboarding”, filed Mar. 25, 2024, which is incorporated herein in its entirety. U.S. patent application Ser. No. 18/616,149 claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 63/454,622, entitled “Transaction Platform With Synchronized Semi-Redundant Ledgers,” filed on Mar. 24, 2023, all of which is incorporated herein by reference in its entirety for all purposes. U.S. patent application Ser. No. 18/616,149 claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 63/509,257, entitled “Data Retrieval and Validation for Asset Onboarding,” filed on Jun. 20, 2023, all of which is incorporated herein by reference in its entirety for all purposes. U.S. patent application Ser. No. 18/616,149 claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 63/509,261, entitled “Data Validation and Assessment Valuation,” filed on Jun. 20, 2023, all of which is incorporated herein by reference in its entirety for all purposes. U.S. patent application Ser. No. 18/616,149 claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 63/509,264, entitled “Secure Identifier Integration,” filed on Jun. 20, 2023, all of which is incorporated herein by reference in its entirety for all purposes. U.S. patent application Ser. No. 18/616,149 claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 63/509,266, entitled “Dual Ledger Syncing,” filed on Jun. 20, 2023, all of which is incorporated herein by reference in its entirety for all purposes. U.S. patent application Ser. No. 18/616,149 claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 63/515,337, entitled “Metadata Process, with Static and Evolving Attributes, Introduced into Tokenization Standards,” filed on Jul. 24, 2023, all of which is incorporated herein by reference in its entirety for all purposes. U.S. patent application Ser. No. 18/616,149 claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 63/596,471, entitled “Real Asset Fractionalization Algorithm,” filed on Nov. 6, 2023, all of which is incorporated herein by reference in its entirety for all purposes. U.S. patent application Ser. No. 18/616,149 claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 63/600,381, entitled “Settlement and Approval Service,” filed on Nov. 17, 2023, all of which is incorporated herein by reference in its entirety for all purposes. U.S. patent application Ser. No. 18/616,149 claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 63/615,108, entitled “Live Syncing Capitalization Table System,” filed on Dec. 27, 2023, all of which is incorporated herein by reference in its entirety for all purposes. U.S. patent application Ser. No. 18/616,149 claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 63/615,128, entitled “Transaction Flow with Master Account Ledger and Escrow Ledger Interaction,” filed on Dec. 27, 2023, all of which is incorporated herein by reference in its entirety for all purposes. U.S. patent application Ser. No. 18/616,149 claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 63/615,136, entitled “Regenerative Model-Continuous Evolution System (“RM-CES”),” filed on Dec. 27, 2023, all of which is incorporated herein by reference in its entirety for all purposes. U.S. patent application Ser. No. 18/616,149 claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 63/615,145, entitled “Transaction & Settlement Validation Service (“TSVS”),” filed on Dec. 27, 2023, all of which is incorporated herein by reference in its entirety for all purposes. TECHNICAL FIELD The present disclosure generally relates to blockchain technology, e.g., cryptographically encoded ledgers distributed across a computing network, and more specifically relates to transaction platforms with semi-redundant ledgers. BACKGROUND There is a