# US 12,602,639 B2 · Methods and systems for time-variant variable prediction and management for supplier procurement

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- Number: US 12,602,639 B2
- Document ID: US12602639B2
- Status: Granted
- Kind: B2
- Application number: 18/650,747
- Filed: 2024-04-30
- Published: 2024-09-05
- Granted: 2026-04-14
- Earliest claimed: 2021-02-17
- Assignees: Indie Tech Limited
- Applicants: Indie Tech Limited
- Inventors: Sophia Gabrielle Contreras Stone; John Symeon Scoufaris
- Web page: https://patentgrep.com/patent/US12602639B2

## CPC

- G06Q 10/0635: Risk analysis of enterprise or organisation activities
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06N 3/09: Supervised learning
- G06N 20/00: Machine learning
- G06Q 10/0835: Relationships between shipper or supplier and carriers
- G06N 3/08: Learning methods
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Methods and systems for determining a time-variant supplier variable related to a contract. The method includes receiving, by a processor having a supplier variable module, a user historical data set, a global historical data set and an external data set. The method includes generating, by the supplier variable module, a set of outcomes; outputting, by the supplier variable module, the set of outcomes. The set of outcomes comprises at least one time-variant supplier variable. In an example, the supplier variable module includes a supplier variable model, which includes a machine learning model.

## Claims

### Claim 1

A computer-implemented method comprising:
receiving, by a marketplace application, sets of data, the sets of data comprising i) user historical data in relation to one or more parties to one or more respective contracts executed through the marketplace application, the one or more parties including at least one of one or more approved suppliers, and the user historical data including qualitative supplier risk assessment information, ii) a global historical data set, and iii) an external data set, wherein an accessible local database or a remote database includes the sets of data and a list of the one or more approved suppliers for each enterprise that is a user of the marketplace application;
training, by a processor using a training data set, a first set of risk models in a neural network including multiple layers of neurons, each neuron having a weight value and receiving inputs from a previous layer of the neural network, the training data set including portions of information contained in the user historical data, the global historical data set, and the external data set, the training of the first set of risk models including adjusting one or more weights of the first set of risk models;
receiving or deriving, by a supplier variable module of the marketplace application, including the first set of risk models: contract attributes of a supplier contract or a potential supplier contract;
generating, by the supplier variable module including the first set of risk models, based on the sets of data and the contract attributes: a first set of outcomes for the supplier contract or the potential supplier contract;
generating, by the marketplace application, at least one recommendation based on the first set of outcomes, the at least one recommendation directed to a risk mitigation action that when executed, causes a reduction or mitigation in risk associated with the supplier contract or the potential supplier contract; and
training the first set of risk models in the neural network using data associated with updated user historical data, updated global historical data, updated external data and the first set of outcomes, to generate a second set of risk models by adjusting one or more second weights of the first set of risk models.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
receiving or deriving, by the supplier variable module of the marketplace application, including the second set of risk models: updated contract attributes of the supplier contract or the potential supplier contract; and
generating, by the supplier variable module including the second set of risk models, based on the sets of data including the updated user historical data, the updated global historical data, the updated external data and the updated contract attributes: a second set of outcomes for the supplier contract or the potential supplier contract.

### Claim 3 (depends on 1)

The method of claim 1, wherein the marketplace application includes one or more supplier variable modules including the supplier variable module, wherein the marketplace application is a global marketplace representing a multi-tenanted marketplace environment and including one or more sub-marketplaces of approved suppliers, each of the one or more sub-marketplaces of approved suppliers respectively including one of the one or more supplier variable modules including a respective set of supplier-specific risk models for a respective enterprise that is a user of the marketplace application.

### Claim 4 (depends on 3)

The method of claim 3, wherein the method further comprises:
updating the sets of data based on the one or more sub-marketplaces of approved suppliers.

### Claim 5 (depends on 3)

The method of claim 3, further comprising:
generating, by the one or more supplier variable modules of the one or more sub-marketplaces of approved suppliers, based on the sets of data: a set of outcomes for an approved supplier; and
monitoring, by the global marketplace, the set of outcomes for the approved supplier over time and across the one or more sub-marketplaces of approved suppliers.

### Claim 6 (depends on 3)

The method of claim 3, wherein the global marketplace includes an industry risk model.

### Claim 7 (depends on 3)

The method of claim 3, wherein the global marketplace includes a global market price model.

### Claim 8 (depends on 7)

The method of claim 7, further comprising:
generating, by the one or more supplier variable modules of the one or more sub-marketplaces of approved suppliers, based on the sets of data: a set of outcomes for an approved supplier expressed in terms of price;
generating, by the global market price model, based on the set of outcomes for the approved supplier expressed in terms of price, a current rate card and market based inputs for supply and demand, a current market price for a supplier category.

### Claim 9 (depends on 8)

The method of claim 8, wherein the global market price model receives, as input, the market based inputs for supply and demand, and wherein a market price estimate is generated using the current rate card with an addition of a geometric Brownian motion stochastic process.

### Claim 10 (depends on 8)

The method of claim 8, wherein the current market price for the supplier category reflects a location of a supplier.

### Claim 11 (depends on 8)

The method of claim 8, further comprising:
generating recommendations based on the current market price, the recommendations including at least one of:
a current and forecast market price for a supplier;
recommendations for comparative pricing for related products or services;
price-based attribution; or
supply and demand.

### Claim 12 (depends on 8)

The method of claim 8, wherein the one or more supplier variable modules comprises a plurality of supplier variable modules.

### Claim 13 (depends on 1)

The method of claim 1, wherein the marketplace application includes one or more supplier variable modules including the supplier variable module, further comprising a further model generating and/or expressing outputs from the first set of outcomes from the one or more supplier variable modules.

### Claim 14 (depends on 13)

The method of claim 13, wherein the further model is a price model.

### Claim 15 (depends on 13)

The method of claim 13, further comprising training the further model.

### Claim 16 (depends on 13)

The method of claim 13, wherein the one or more supplier variable modules comprises a plurality of supplier variable modules.

### Claim 17 (depends on 1)

The method of claim 1, wherein the first set of outcomes for the supplier contract or the potential supplier contract includes a counterparty risk estimate, a contract risk estimate, a supply risk estimate, and a market price estimate and generating the first set of outcomes comprises:
generating, by a counterparty supplier risk model of the supplier variable module: a counterparty supplier risk estimate;
generating, by a counterparty enterprise risk model of the supplier variable module: a counterparty enterprise risk estimate;
wherein the supplier variable module includes a contract risk model configured to generate the contract risk estimate,
wherein the supplier variable module includes a supply risk model configured to generate the supply risk estimate,
wherein the supplier variable module includes a market price model configured to generate the market price estimate, and
wherein the counterparty risk estimate includes the counterparty enterprise risk estimate and the counterparty supplier risk estimate.

### Claim 18 (depends on 17)

The method of claim 17, wherein the counterparty enterprise risk model and the counterparty supplier risk model receive, as input, the qualitative supplier risk assessment information, and wherein the counterparty enterprise risk estimate is generated by aggregating one or more itemized inputs and relative risk weightings of the qualitative supplier risk assessment information, wherein the counterparty supplier risk estimate is generated according to the following equation:
Counterparty
⁢
Supplier
⁢
Risk
⁢
(
t
)
=
Current
⁢
Exposure
⁢
(
t
)
+
Potential
⁢
Future
⁢
Exposure
⁢
(
t
)
+
Settlement
⁢
Risk
⁢
(
t
)
-
Collateral
⁢
(
t
)
;
wherein Collateral(t) is a collateral.

### Claim 19 (depends on 17)

The method of claim 17, wherein the counterparty risk estimate, the counterparty enterprise risk estimate, the counterparty risk estimate, the contract risk estimate, the supply risk estimate, and the market price estimate are respective time-variant supplier variables.

### Claim 20 (depends on 1)

The method of claim 1, wherein the marketplace application is a web-based marketplace application.

### Claim 21 (depends on 1)

The method of claim 1, further comprising:
generating, by the supplier variable module, the training data set;
wherein the training data set comprises the first set of outcomes and at least a portion of the sets of data.

### Claim 22 (depends on 21)

The method of claim 21, further comprising training the supplier variable module with the training data set.

### Claim 23 (depends on 21)

The method of claim 21, further comprising:
generating, by the supplier variable module, a testing data set; wherein the testing data set comprises at least a portion of the sets of data; and
testing the supplier variable module with the testing data set.

### Claim 24

A non-transitory computer readable memory containing instructions which, when executed by a processor of a marketplace application server, cause the processor to:
receive, by a marketplace application, sets of data, the sets of data comprising i) user historical data in relation to one or more parties to one or more respective contracts executed through the marketplace application, the one or more parties including at least one of one or more approved suppliers, and the user historical data including qualitative supplier risk assessment information, ii) a global historical data set, and iii) an external data set, wherein an accessible local database or a remote database includes the sets of data and a list of the one or more approved suppliers for each enterprise that is a user of the marketplace application;
train, by a processor using a training data set, a first set of risk models in a neural network including multiple layers of neurons, each neuron having a weight value and receiving inputs from a previous layer of the neural network, the training data set including portions of information contained in the user historical data, the global historical data set, and the external data set, the training of the first set of risk models including adjusting one or more weights of the first set of risk models;
receive or derive, by a supplier variable module of the marketplace application, including the first set of risk models: contract attributes of a supplier contract or a potential supplier contract;
generate, by the supplier variable module including the first set of risk models, based on the sets of data and the contract attributes: a first set of outcomes for the supplier contract or the potential supplier contract;
generate, by the marketplace application, at least one recommendation based on the first set of outcomes, the at least one recommendation directed to a risk mitigation action that when executed, causes a reduction or mitigation in risk associated with the supplier contract or the potential supplier contract; and
train the first set of risk models in the neural network using data associated with updated user historical data, updated global historical data, updated external data and the first set of outcomes, to generate a second set of risk models by adjusting one or more second weights of the first set of risk models.

### Claim 25

A system comprising:
a local database including first sets of data related to a supplier contract or a potential supplier contract;
a marketplace application server including a processor, the marketplace application server having access to a remote database including second sets of data related to the supplier contract or the potential supplier contract, the processor configured to:
receive, by a marketplace application, sets of data, the sets of data comprising i) user historical data in relation to one or more parties to one or more respective contracts executed through the marketplace application, the one or more parties including at least one of one or more approved suppliers, and the user historical data including qualitative supplier risk assessment information, ii) a global historical data set, and iii) an external data set, wherein an accessible local database or a remote database includes the sets of data and a list of the one or more approved suppliers for each enterprise that is a user of the marketplace application;
train, by a processor using a training data set, a first set of risk models in a neural network including multiple layers of neurons, each neuron having a weight value and receiving inputs from a previous layer of the neural network, the training data set including portions of information contained in the user historical data, the global historical data set, and the external data set, the training of the first set of risk models including adjusting one or more weights of the first set of risk models;
receive or derive, by a supplier variable module of the marketplace application, including the first set of risk models: contract attributes of a supplier contract or a potential supplier contract;
generate, by the supplier variable module including the first set of risk models, based on the sets of data and the contract attributes: a first set of outcomes for the supplier contract or the potential supplier contract;
generate, by the marketplace application, at least one recommendation based on the first set of outcomes, the at least one recommendation directed to a risk mitigation action that when executed, causes a reduction or mitigation in risk associated with the supplier contract or the potential supplier contract; and
train the first set of risk models in the neural network using data associated with updated user historical data, updated global historical data, updated external data and the first set of outcomes, to generate a second set of risk models by adjusting one or more second weights of the first set of risk models.