Adaptive intelligence and shared infrastructure lending transaction enablement platform responsive to crowd sourced information

Patent metadata

Field	Value
Publication number	US-12524820-B2
Application number	US-202418805663-A
Country	US
Kind code	B2
Filing date	Aug 15, 2024
Priority date	May 6, 2018
Publication date	Jan 13, 2026
Grant date	Jan 13, 2026

Abstract

A system may include a non-transitory computer-readable storage medium storing instructions for execution and one or more processors that execute the instructions. The instructions may cause the one or more processors to configure at least one parameter of a crowdsourcing request related to obtaining information relating to a collateral for a loan, publish the crowdsourcing request related to obtaining the information relating to the collateral for the loan to a group of information suppliers, collect and process a response from an information supplier of the group of information suppliers, where the response includes information on a condition of the collateral for the loan, process the response provided by the information supplier to determine whether an information supply event relating to the response is successful, and respond to a determination of a successful information supply event.

First claim

What is claimed is:

1 . A system for monitoring an asset used as collateral for a loan, the system comprising:
a set of sensors located within sensor range of the asset, wherein the sensors are configured to transmit sensor data streams through a distributed IOT network; a data processing system configured to: receive the sensor data streams from the set of sensors, wherein the sensor data streams comprise at least one of quality data, physical condition data, location data, usage data, or maintenance data for the asset; process the sensor data streams to generate machine learning inputs, wherein the processing comprises aggregating data extracted from the sensor data streams; process the machine learning inputs using a machine learning classifier that generates an output indicating a condition of the asset, wherein the system trains the machine learning classifier to recognize a condition of the asset using a training set of machine learning inputs and actual outcomes, wherein the training comprises iteratively updating parameters of the machine learning classifier based on comparing predicted conditions to the actual outcomes; and
a loan management system configured to:
automatically determine whether the asset satisfies a threshold indicating a minimum collateral value for the loan based on the output indicating the condition of the asset; and upon determining that the asset does not satisfy the threshold indicating a minimum collateral value for the loan: automatically modify a data structure containing terms of the loan to indicate a foreclosure status; record the modified data structure containing the terms of the loan in a distributed database; and list the asset for sale on an electronic marketplace at a sale price that is based on the condition of the asset.

2 . The system of claim 1 , wherein the condition of the asset comprises at least one of: a quality level of the asset, a maintenance status of the asset, a damage state of the asset, or a deterioration level of the asset.

3 . The system of claim 1 , wherein the set of sensors comprises at least one of: image sensors, temperature sensors, pressure sensors, humidity sensors, velocity sensors, acceleration sensors, weight sensors, or position sensors.

4 . The system of claim 1 , wherein the training set comprises historical sensor data correlated with documented asset conditions.

5 . The system of claim 1 , wherein processing the sensor data to generate machine learning inputs further comprises performing data normalization, deduplication, and synchronization of the sensor data streams.

6 . The system of claim 1 , wherein listing the asset comprises:
identifying similar assets listed on the electronic marketplace; and determining the sale price based on prices of the identified similar assets and the condition of the asset.

7 . The system of claim 1 , wherein the machine learning classifier is a neural network comprising:
a plurality of input nodes configured to receive the machine learning inputs; a plurality of hidden nodes arranged in multiple layers; and a plurality of output nodes configured to indicate the condition of the asset.

8 . A method for monitoring an asset used as collateral for a loan, the method comprising:
receiving sensor data streams from a set of sensors located within sensor range of the asset, wherein the sensors transmit the sensor data streams through a distributed IOT network, and wherein the sensor data streams comprise at least one of quality data, physical condition data, location data, usage data, or maintenance data for the asset; processing the sensor data streams to generate machine learning inputs, wherein the processing comprises aggregating data extracted from the sensor data streams; processing the machine learning inputs using a machine learning classifier that generates an output indicating a condition of the asset, wherein the machine learning classifier is trained to recognize a condition of the asset using a training set of machine learning inputs and actual outcomes, wherein the training comprises iteratively updating parameters of the machine learning classifier based on comparing predicted conditions to the actual outcomes; automatically determining whether the asset satisfies a threshold indicating a minimum collateral value for the loan based on the output indicating the condition of the asset; and upon determining that the asset does not satisfy the threshold indicating a minimum collateral value for the loan: automatically modifying a data structure containing terms of the loan to modify a loan parameter of the loan; and recording the modified data structure containing the terms of the loan in a distributed database.

9 . The method of claim 8 , wherein modifying the loan parameter comprises automatically increasing an interest rate to correspond to an unsecured loan rate.

10 . The method of claim 8 , wherein modifying the loan parameter comprises modifying a payment schedule.

11 . The method of claim 8 , wherein modifying the loan parameter comprises modifying a principal balance.

12 . The method of claim 8 , wherein modifying the loan parameter comprises modifying a duration of the loan.

13 . The method of claim 8 , wherein modifying the loan parameter comprises modifying a covenant of the loan.

14 . The method of claim 8 , further comprising:
automatically initiating an inspection process to verify the condition of the asset; and updating the loan parameter based on results of the inspection process.

15 . The method of claim 8 , wherein the condition of the asset comprises at least one of: a quality level of the asset, a maintenance status of the asset, a damage state of the asset, or a deterioration level of the asset.

16 . The method of claim 8 , wherein the set of sensors comprises at least one of: image sensors, temperature sensors, pressure sensors, humidity sensors, velocity sensors, acceleration sensors, weight sensors, or position sensors.

17 . The method of claim 8 , wherein the training set comprises historical sensor data correlated with documented asset conditions.

18 . The method of claim 8 , wherein processing the sensor data to generate machine learning inputs further comprises performing data normalization, deduplication, and synchronization of the sensor data streams.

19 . The method of claim 8 , wherein the machine learning classifier is a neural network comprising:
a plurality of input nodes configured to receive the machine learning inputs; a plurality of hidden nodes arranged in multiple layers; and a plurality of output nodes configured to indicate the condition of the asset.

20 . The method of claim 8 , further comprising:
monitoring external marketplace data to determine a current market value of assets that are similar to the asset, wherein automatically determining whether the asset satisfies the threshold is based on both the condition of the asset and the current market value of the assets that are similar to the asset.

Assignees

• Strong Force Tx Portfolio 2018 Llc

Inventors

• Cella Charles Howard

Classifications

• H04L9/0637

  Modes of operation, e.g. cipher block chaining [CBC], electronic codebook [ECB] or Galois/counter mode [GCM] · CPC title

• G06Q50/26

  Government or public services (business processes related to the transportation industry G06Q50/40) · CPC title

• G06Q50/188

  Electronic negotiation · CPC title

• G06Q50/18

  Legal services · CPC title

• G06Q40/08

  Insurance · CPC title