IP Library › Granted Patent US 12,626,220
Granted Patent B2
US 12,626,220 · App. 18/516,354 · Granted May 12, 2026

Product distribution system and method

Inventors: Severence M. MacLaughlin (Palm Beach, FL); Ram Prasad Bora (Palm Beach, FL); Dhiraj Sharma (Palm Beach, FL)
Assignee: DeLorean Artificial Intelligence, Inc.
G06Q10/087G06Q30/0202
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,626,220
App. No.
18/516,354
Filed
Nov 21, 2023
Granted
May 12, 2026
Kind
B2
Art Unit
3627
USPC
705/28
Abstract

Product distribution including receiving external conditions data indicative of environmental conditions concerning distribution of products from a point of sale (POS), determining a POS product distribution model for the POS, including first and second product distribution models operable to determine distribution of the first and second products to the POS based on external conditions data, determining, based on application of the external conditions data to the POS product distribution model, a product distribution to the POS, including application of the external conditions data to the first and second product distribution model to determine distributions of the first and second products to the POS, and providing supply instructions for the POS, including first and second product supply instructions to cause the product supply network to provide the distribution of the first and second product to the POS.

Claims (219)

1 . A product distribution system comprising:

a distribution database storing distribution data comprising:

a first product distribution model configured to determine a distribution of a first product to a point of sale based on external conditions data; and

a second product distribution model configured to determine a distribution of a second product to a point of sale based on external conditions data; and

non-transitory computer readable storage medium comprising program instructions stored thereon that are executable by a processor to perform the following operations for product distribution:

determining, by a distribution engine, historical external conditions data indicative of environmental conditions impacting product distribution;

determining, by the distribution engine, first historical external conditions data comprising a first subset of the historical external conditions data that is indicative of one or more environmental conditions affecting distribution of the first product to one or more points of sale;

determining, by the distribution engine, second historical external conditions data comprising a second subset of the historical external conditions data that is indicative of one or more environmental conditions affecting distribution of the second product to one or more points of sale;

determining, by the distribution engine, distribution performance data indicative of distribution of products to one or more points of sale and disposition of the first and second products by the one or more points of sale;

training, by the distribution engine based on the first historical external conditions data and the distribution performance data, the first product distribution model, the first product distribution model associated with a first set of external condition data types identified as relevant to distribution of the first product during training;

training, by the distribution engine based on the second historical external conditions data and the distribution performance data, the second product distribution model, the second product distribution model associated with a second set of external condition data types identified as relevant to distribution of the second product during training;

receiving, by the distribution engine, external conditions data indicative of one or more environmental conditions affecting distribution of products to a first point of sale;

determining, by the distribution engine, a first point of sale (POS) product distribution model for the first point of sale, the first POS product distribution model comprising:

the first product distribution model configured to determine a distribution of the first product to the first point of sale based on external conditions data; and

the second product distribution model configured to determine a distribution of the second product to the first point of sale based on external conditions data,

the determining of the first POS product distribution model comprising:

determining characteristics of the first point of sale;

determining characteristics of the first product distribution model;

determining that the characteristics of the first product distribution model match characteristics of the first point of sale;

including, based on determining that the characteristics of the first product distribution model match characteristics of the first point of sale, the first product distribution model in the first POS product distribution model;

determining characteristics of the second product distribution model;

determining that the characteristics of the second product distribution model match characteristics of the first point of sale; and

including, based on determining that the characteristics of the second product distribution model match characteristics of the first point of sale, the second product distribution model in the first POS product distribution model;

extracting, by the distribution engine based on the first and second sets of external condition data types, POS-product external conditions comprising:

a first subset of the external conditions data that corresponds to the first point of sale and the first product; and

a second subset of the external conditions data that corresponds to the first point of sale and the second product;

determining, by the distribution engine based on application of the POS-product external conditions data to the first POS product distribution model, a product distribution to the first point of sale, comprising:

determining, by the distribution engine based on application of the first subset of the external conditions data to the first product distribution model, a distribution of the first product to the first point of sale; and

determining, by the distribution engine based on application of the second subset of the external conditions data to the second product distribution model, a distribution of the second product to the first point of sale,

the product distribution to the first point of sale comprising the distribution of the first product to the first point of sale and the distribution of the second product to the first point of sale; and

providing, by the distribution engine to a product supply network, supply instructions for the first point of sale, the supply instructions for the first point of sale comprising:

first product supply instructions configured to cause the product supply network to provide the distribution of the first product to the first point of sale; and

second product supply instructions configured to cause the product supply network to provide the distribution of the second product to the first point of sale,

the product supply network configured to:

distribute, responsive to the first product supply instructions, the first product to the first point of sale; and

distribute, responsive to the second product supply instructions, the second product to the first point of sale;

obtaining, by the distribution engine, updated distribution performance data indicative of disposition of the first and second products by the first point of sale, the updated distribution performance data comprising:

first updated distribution performance data indicative of distribution and disposition of the first product by the first point of sale following the first product supply instructions; and

second updated distribution performance data indicative of distribution and disposition of the second product by the first point of sale following the second product supply instructions;

retraining, by the distribution engine based on the updated distribution performance data, the first product distribution model to generate an updated first product distribution model configured to determine a distribution of the first product to a point of sale based on external conditions data;

retraining, by the distribution engine based on the updated distribution performance data, the second product distribution model to generate an updated second product distribution model configured to determine a distribution of the second product to a point of sale based on external conditions data; and

determining, by the distribution engine, updated product supply instructions for distributing the first product and second product to the first point of sale,

the product supply network configured to distribute, responsive to the updated product supply instructions, the first product and the second product to the first point of sale.

2 . The system of claim 1 , the operations further comprising:

receiving, by the distribution engine, second external conditions data indicative of one or more environmental conditions affecting distribution of products to a second point of sale;

determining, by the distribution engine, a second POS product distribution model for the second point of sale, the second POS product distribution model comprising:

a third product distribution model configured to determine a distribution of the first product to the second point of sale based on external conditions data; and

a fourth product distribution model configured to determine a distribution of the second product to the second point of sale based on external conditions data;

determining, by the distribution engine based on application of the second external conditions data to the second POS product distribution model, a product distribution to the second point of sale, comprising:

determining, by the distribution engine based on application of the second external conditions data to the third product distribution model, a distribution of the first product to the second point of sale,

determining, by the distribution engine based on application of the second external conditions data to the fourth product distribution model, a distribution of the second product to the second point of sale,

the product distribution to the second point of sale comprising the distribution of the first product to the second point of sale and the distribution of the second product to the second point of sale; and

providing, by the distribution engine to the product supply network, supply instructions for the second point of sale, the supply instructions for the second point of sale comprising:

third product supply instructions configured to cause the product supply network to provide the distribution of the first product to the second point of sale; and

fourth product supply instructions configured to cause the product supply network to provide the distribution of the second product to the second point of sale.

3 . The system of claim 1 , the operations further comprising:

determining, by the distribution engine, updated historical external conditions data comprising:

first updated conditions affecting the distribution of the first product to the first point of sale responsive to the first product supply instructions; and

second updated conditions affecting the distribution of the second product to the first point of sale responsive to the second product supply instructions,

the retraining of the first product distribution model to generate the updated first product distribution model being further based on the updated historical external conditions data, and

the retraining of the second product distribution model to generate the updated second product distribution model being further based on the updated historical external conditions data.

4 . The system of claim 3 , the operations further comprising dynamically adjusting distribution instructions based on external condition updates comprising:

receiving, by the distribution engine, updated external conditions data indicative of one or more environmental conditions affecting distribution of products to the first point of sale;

determining, by the distribution engine, an updated POS product distribution model for the first point of sale, the updated POS product distribution model comprising:

the updated first product distribution model; and

the updated second product distribution model;

determining, by the distribution engine based on application of the updated external conditions data to the updated POS product distribution model, an updated product distribution to the first point of sale, comprising:

determining, by the distribution engine based on application of the updated external conditions data to the updated first product distribution model, an updated distribution of the first product to the first point of sale,

determining, by the distribution engine based on application of the updated external conditions data to the updated second product distribution model, an updated distribution of the second product to the first point of sale,

the updated product distribution to the first point of sale comprising the updated distribution of the first product to the first point of sale and the updated distribution of the second product to the first point of sale; and

providing, by the distribution engine to the product supply network, updated supply instructions for the first point of sale, the updated supply instructions for the first point of sale comprising:

third product supply instructions configured to cause the product supply network to provide the updated distribution of the first product to the first point of sale; and

fourth product supply instructions configured to cause the product supply network to provide the updated distribution of the second product to the first point of sale.

5 . A method of product distribution, the method comprising:

determining, by a distribution engine, historical external conditions data indicative of environmental conditions impacting product distribution;

determining, by the distribution engine, first historical external conditions data comprising a first subset of the historical external conditions data that is indicative of one or more environmental conditions affecting distribution of the first product to one or more points of sale;

determining, by the distribution engine, second historical external conditions data comprising a second subset of the historical external conditions data that is indicative of one or more environmental conditions affecting distribution of the second product to one or more points of sale;

determining, by the distribution engine, distribution performance data indicative of distribution of products to one or more points of sale and disposition of the first and second products by the one or more points of sale;

training, by the distribution engine based on the first historical external conditions data and the distribution performance data, a first product distribution model, the first product distribution model associated with a first set of external condition data types identified as relevant to distribution of the first product during training;

training, by the distribution engine based on the second historical external conditions data and the distribution performance data, a second product distribution model, the second product distribution model associated with a second set of external condition data types identified as relevant to distribution of the second product during training;

receiving, by the distribution engine, external conditions data indicative of one or more environmental conditions affecting distribution of products to a first point of sale;

determining, by the distribution engine, a first point of sale (POS) product distribution model for the first point of sale, the first POS product distribution model comprising:

the first product distribution model configured to determine a distribution of the first product to the first point of sale based on external conditions data; and

the second product distribution model configured to determine a distribution of the second product to the first point of sale based on external conditions data,

the determining of the first POS product distribution model comprising:

determining characteristics of the first point of sale;

determining characteristics of the first product distribution model;

determining that the characteristics of the first product distribution model match characteristics of the first point of sale;

including, based on determining that the characteristics of the first product distribution model match characteristics of the first point of sale, the first product distribution model in the first POS product distribution model;

determining characteristics of the second product distribution model;

determining that the characteristics of the second product distribution model match characteristics of the first point of sale; and

including, based on determining that the characteristics of the second product distribution model match characteristics of the first point of sale, the second product distribution model in the first POS product distribution model;

extracting, by the distribution engine based on the first and second sets of external condition data types, POS-product external conditions comprising:

a first subset of the external conditions data that corresponds to the first point of sale and the first product; and

a second subset of the external conditions data that corresponds to the first point of sale and the second product;

determining, by the distribution engine based on application of the POS-product external conditions data to the first POS product distribution model, a product distribution to the first point of sale, comprising:

determining, by the distribution engine based on application of the first subset of the external conditions data to the first product distribution model, a distribution of the first product to the first point of sale; and

determining, by the distribution engine based on application of the second subset of the external conditions data to the second product distribution model, a distribution of the second product to the first point of sale,

the product distribution to the first point of sale comprising the distribution of the first product to the first point of sale and the distribution of the second product to the first point of sale; and

providing, by the distribution engine to a product supply network, supply instructions for the first point of sale, the supply instructions for the first point of sale comprising:

first product supply instructions configured to cause the product supply network to provide the distribution of the first product to the first point of sale; and

second product supply instructions configured to cause the product supply network to provide the distribution of the second product to the first point of sale,

the product supply network configured to:

distribute, responsive to the first product supply instructions, the first product to the first point of sale; and

distribute, responsive to the second product supply instructions, the second product to the first point of sale;

obtaining, by the distribution engine, updated distribution performance data indicative of disposition of the first and second products by the first point of sale, the updated distribution performance data comprising:

first updated distribution performance data indicative of distribution and disposition of the first product by the first point of sale following the first product supply instructions; and

second updated distribution performance data indicative of distribution and disposition of the second product by the first point of sale following the second product supply instructions;

retraining, by the distribution engine based on the updated distribution performance data, the first product distribution model to generate an updated first product distribution model configured to determine a distribution of the first product to a point of sale based on external conditions data;

retraining, by the distribution engine based on the updated distribution performance data, the second product distribution model to generate an updated second product distribution model configured to determine a distribution of the second product to a point of sale based on external conditions data; and

determining, by the distribution engine, updated product supply instructions for distributing the first product and second product to the first point of sale,

the product supply network configured to distribute, responsive to the updated product supply instructions, the first product and the second product to the first point of sale.

6 . The method of claim 5 , further comprising:

distributing, by the product supply network responsive to the first product supply instructions, the first product to the first point of sale;

distributing, by the product supply network responsive to the second product supply instructions, the second product to the first point of sale; and

distributing, by the product supply network responsive to the updated product supply instructions, the first product and the second product to the first point of sale.

7 . The method of claim 5 , further comprising:

receiving, by the distribution engine, second external conditions data indicative of one or more environmental conditions affecting distribution of products to a second point of sale;

determining, by the distribution engine, a second POS product distribution model for the second point of sale, the second POS product distribution model comprising:

a third product distribution model configured to determine a distribution of the first product to the second point of sale based on external conditions data; and

a fourth product distribution model configured to determine a distribution of the second product to the second point of sale based on external conditions data;

determining, by the distribution engine based on application of the second external conditions data to the second POS product distribution model, a product distribution to the second point of sale, comprising:

determining, by the distribution engine based on application of the second external conditions data to the third product distribution model, a distribution of the first product to the second point of sale,

determining, by the distribution engine based on application of the second external conditions data to the fourth product distribution model, a distribution of the second product to the second point of sale,

the product distribution to the second point of sale comprising the distribution of the first product to the second point of sale and the distribution of the second product to the second point of sale; and

providing, by the distribution engine to the product supply network, supply instructions for the second point of sale, the supply instructions for the second point of sale comprising:

third product supply instructions configured to cause the product supply network to provide the distribution of the first product to the second point of sale; and

fourth product supply instructions configured to cause the product supply network to provide the distribution of the second product to the second point of sale.

8 . The method of claim 5 , further comprising:

determining, by the distribution engine, updated historical external conditions data comprising:

first updated conditions affecting the distribution of the first product to the first point of sale responsive to the first product supply instructions; and

second updated conditions affecting the distribution of the second product to the first point of sale responsive to the second product supply instructions,

the retraining of the first product distribution model to generate the updated first product distribution model being further based on the updated historical external conditions data, and

the retraining of the second product distribution model to generate the updated second product distribution model being further based on the updated historical external conditions data.

9 . The method of claim 8 , further comprising dynamically adjusting distribution instructions based on external condition updates comprising:

receiving, by the distribution engine, updated external conditions data indicative of one or more environmental conditions affecting distribution of products to the first point of sale;

determining, by the distribution engine, an updated POS product distribution model for the first point of sale, the updated POS product distribution model comprising:

the updated first product distribution model; and

the updated second product distribution model;

determining, by the distribution engine based on application of the second external conditions data to the updated POS product distribution model, an updated product distribution to the first point of sale, comprising:

determining, by the distribution engine based on application of the updated external conditions data to the updated first product distribution model, an updated distribution of the first product to the first point of sale,

determining, by the distribution engine based on application of the updated external conditions data to the updated second product distribution model, an updated distribution of the second product to the first point of sale,

the updated product distribution to the first point of sale comprising the updated distribution of the first product to the first point of sale and the updated distribution of the second product to the first point of sale; and

providing, by the distribution engine to the product supply network, updated supply instructions for the first point of sale, the updated supply instructions for the first point of sale comprising:

third product supply instructions configured to cause the product supply network to provide the updated distribution of the first product to the first point of sale; and

fourth product supply instructions configured to cause the product supply network to provide the updated distribution of the second product to the first point of sale.

10 . A non-transitory computer readable storage medium comprising program instructions stored thereon that are executable by a processor to perform the following operations for product distribution:

determining, by a distribution engine, historical external conditions data indicative of environmental conditions impacting product distribution;

determining, by the distribution engine, first historical external conditions data comprising a first subset of the historical external conditions data that is indicative of one or more environmental conditions affecting distribution of the first product to one or more points of sale;

determining, by the distribution engine, second historical external conditions data comprising a second subset of the historical external conditions data that is indicative of one or more environmental conditions affecting distribution of the second product to one or more points of sale;

determining, by the distribution engine, distribution performance data indicative of distribution of products to one or more points of sale and disposition of the first and second products by the one or more points of sale;

training, by the distribution engine based on the first historical external conditions data and the distribution performance data, a first product distribution model, the first product distribution model associated with a first set of external condition data types identified as relevant to distribution of the first product during training;

training, by the distribution engine based on the second historical external conditions data and the distribution performance data, a second product distribution model, the second product distribution model associated with a second set of external condition data types identified as relevant to distribution of the second product during training;

receiving, by the distribution engine, external conditions data indicative of one or more environmental conditions affecting distribution of products to a first point of sale;

determining, by the distribution engine, a first point of sale (POS) product distribution model for the first point of sale, the first POS product distribution model comprising:

the first product distribution model configured to determine a distribution of the first product to the first point of sale based on external conditions data; and

the second product distribution model configured to determine a distribution of the second product to the first point of sale based on external conditions data,

the determining of the first POS product distribution model comprising:

determining characteristics of the first point of sale;

determining characteristics of the first product distribution model;

determining that the characteristics of the first product distribution model match characteristics of the first point of sale;

including, based on determining that the characteristics of the first product distribution model match characteristics of the first point of sale, the first product distribution model in the first POS product distribution model;

determining characteristics of the second product distribution model;

determining that the characteristics of the second product distribution model match characteristics of the first point of sale; and

including, based on determining that the characteristics of the second product distribution model match characteristics of the first point of sale, the second product distribution model in the first POS product distribution model;

extracting, by the distribution engine based on the first and second sets of external condition data types, POS-product external conditions comprising:

a first subset of the external conditions data that corresponds to the first point of sale and the first product; and

a second subset of the external conditions data that corresponds to the first point of sale and the second product;

determining, by the distribution engine based on application of the POS-product external conditions data to the first POS product distribution model, a product distribution to the first point of sale, comprising:

determining, by the distribution engine based on application of the first subset of the external conditions data to the first product distribution model, a distribution of the first product to the first point of sale,

determining, by the distribution engine based on application of the second subset of the external conditions data to the second product distribution model, a distribution of the second product to the first point of sale,

the product distribution to the first point of sale comprising the distribution of the first product to the first point of sale and the distribution of the second product to the first point of sale; and

providing, by the distribution engine to a product supply network, supply instructions for the first point of sale, the supply instructions for the first point of sale comprising:

first product supply instructions configured to cause the product supply network to provide the distribution of the first product to the first point of sale; and

second product supply instructions configured to cause the product supply network to provide the distribution of the second product to the first point of sale,

the product supply network configured to:

distribute, responsive to the first product supply instructions, the first product to the first point of sale; and

distribute, responsive to the second product supply instructions, the second product to the first point of sale;

obtaining, by the distribution engine, updated distribution performance data indicative of disposition of the first and second products by the first point of sale, the updated distribution performance data comprising:

first updated distribution performance data indicative of distribution and disposition of the first product by the first point of sale following the first product supply instructions; and

second updated distribution performance data indicative of distribution and disposition of the second product by the first point of sale following the second product supply instructions;

retraining, by the distribution engine based on the updated distribution performance data, the first product distribution model to generate an updated first product distribution model configured to determine a distribution of the first product to a point of sale based on external conditions data;

retraining, by the distribution engine based on the updated distribution performance data, the second product distribution model to generate an updated second product distribution model configured to determine a distribution of the second product to a point of sale based on external conditions data; and

determining, by the distribution engine, updated product supply instructions for distributing the first product and second product to the first point of sale,

the product supply network configured to distribute, responsive to the updated product supply instructions, the first product and the second product to the first point of sale.

11 . The medium of claim 10 , the operations further comprising:

distributing, by the product supply network responsive to the first product supply instructions, the first product to the first point of sale;

distributing, by the product supply network responsive to the second product supply instructions, the second product to the first point of sale; and

distributing, by the product supply network responsive to the updated product supply instructions, the first product and the second product to the first point of sale.

12 . The medium of claim 10 , the operations further comprising:

receiving, by the distribution engine, second external conditions data indicative of one or more environmental conditions affecting distribution of products to a second point of sale;

determining, by the distribution engine, a second POS product distribution model for the second point of sale, the second POS product distribution model comprising:

a third product distribution model configured to determine a distribution of the first product to the second point of sale based on external conditions data; and

a fourth product distribution model configured to determine a distribution of the second product to the second point of sale based on external conditions data;

determining, by the distribution engine based on application of the second external conditions data to the second POS product distribution model, a product distribution to the second point of sale, comprising:

determining, by the distribution engine based on application of the second external conditions data to the third product distribution model, a distribution of the first product to the second point of sale,

determining, by the distribution engine based on application of the second external conditions data to the fourth product distribution model, a distribution of the second product to the second point of sale,

the product distribution to the second point of sale comprising the distribution of the first product to the second point of sale and the distribution of the second product to the second point of sale; and

providing, by the distribution engine to the product supply network, supply instructions for the second point of sale, the supply instructions for the second point of sale comprising:

third product supply instructions configured to cause the product supply network to provide the distribution of the first product to the second point of sale; and

fourth product supply instructions configured to cause the product supply network to provide the distribution of the second product to the second point of sale.

13 . The medium of claim 10 , the operations further comprising:

determining, by the distribution engine, updated historical external conditions data comprising:

first updated conditions affecting the distribution of the first product to the first point of sale responsive to the first product supply instructions; and

second updated conditions affecting the distribution of the second product to the first point of sale responsive to the second product supply instructions,

the retraining of the first product distribution model to generate the updated first product distribution model being further based on the updated historical external conditions data, and

the retraining of the second product distribution model to generate the updated second product distribution model being further based on the updated historical external conditions data.

14 . The medium of claim 13 , the operations further comprising dynamically adjusting distribution instructions based on external condition updates comprising:

receiving, by the distribution engine, updated external conditions data indicative of one or more environmental conditions affecting distribution of products to the first point of sale;

determining, by the distribution engine, an updated POS product distribution model for the first point of sale, the updated POS product distribution model comprising:

the updated first product distribution model; and

the updated second product distribution model;

determining, by the distribution engine based on application of the updated external conditions data to the updated POS product distribution model, an updated product distribution to the first point of sale, comprising:

determining, by the distribution engine based on application of the updated external conditions data to the updated first product distribution model, an updated distribution of the first product to the first point of sale,

determining, by the distribution engine based on application of the updated external conditions data to the updated second product distribution model, an updated distribution of the second product to the first point of sale,

the updated product distribution to the first point of sale comprising the updated distribution of the first product to the first point of sale and the updated distribution of the second product to the first point of sale; and

providing, by the distribution engine to the product supply network, updated supply instructions for the first point of sale, the updated supply instructions for the first point of sale comprising:

third product supply instructions configured to cause the product supply network to provide the updated distribution of the first product to the first point of sale; and

fourth product supply instructions configured to cause the product supply network to provide the updated distribution of the second product to the first point of sale.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 28, 2024
From: MACLAUGHLIN, SEVERENCE M.; BORA, RAM PRASAD; SHARMA, DHIRAJ
To: DELOREAN ARTIFICIAL INTELLIGENCE, INC.
Reel/Frame 066591/0804 →
Continuity (1)
Related Publication 20250165918A1 · May 22, 2025
References Cited (114)
US 5966126A · Szabo · 1999 [cited by applicant]
US 8548937B2 · Saigal · 2013 [cited by applicant]
US 10483003B1 · Mcnair · 2019 [cited by applicant]
US 11093880B2 · Anderson · 2021 [cited by applicant]
US 11093884B2 · Devarakonda et al. · 2021 [cited by applicant]
US 11210300B2 · Ignatyev · 2021 [cited by applicant]
US 11282022B2 · Devarakonda et al. · 2022 [cited by applicant]
US 11514379B2 · Adrian · 2022 [cited by applicant]
US 11581092B1 · Mcnair · 2023 [cited by applicant]
US 11723560B2 · Constantin · 2023 [cited by applicant]
US 11790412B2 · Pachauri et al. · 2023 [cited by applicant]
US 20020143635A1 · Goodwin, III · 2002 [cited by applicant]
US 20050234761A1 · Pinto · 2005 [cited by applicant]
US 20060085408A1 · Morsa · 2006 [cited by applicant]
US 20060190310A1 · Gudla · 2006 [cited by applicant]
US 20070043609A1 · Imam et al. · 2007 [cited by applicant]
US 20070043611A1 · Newman · 2007 [cited by applicant]
US 20070112704A1 · Tomkins · 2007 [cited by applicant]
US 20070288297A1 · Karras · 2007 [cited by applicant]
US 20100010878A1 · Pinto · 2010 [cited by applicant]
US 20100287059A1 · Mccoy et al. · 2010 [cited by applicant]
US 20110270646A1 · Prasanna · 2011 [cited by applicant]
US 20110295783A1 · Zhao · 2011 [cited by applicant]
US 20120059779A1 · Syed · 2012 [cited by applicant]
US 20120284213A1 · Lin · 2012 [cited by applicant]
US 20120303412A1 · Etzioni et al. · 2012 [cited by applicant]
US 20130151311A1 · Smallwood · 2013 [cited by applicant]
US 20140081738A1 · Abraham · 2014 [cited by applicant]
US 20140101143A1 · Zhang · 2014 [cited by applicant]
US 20140372351A1 · Sun · 2014 [cited by applicant]
US 20150032526A1 · Calman et al. · 2015 [cited by applicant]
US 20150100356A1 · Bessler · 2015 [cited by applicant]
US 20150149237A1 · Brock · 2015 [cited by applicant]
US 20160063506A1 · Hicks · 2016 [cited by applicant]
US 20160239808A1 · Ryu · 2016 [cited by applicant]
US 20180060744A1 · Achin · 2018 [cited by applicant]
US 20180218247A1 · Lee et al. · 2018 [cited by applicant]
US 20180315141A1 · Hunn · 2018 [cited by applicant]
US 20190116265A1 · Avila et al. · 2019 [cited by applicant]
US 20190130425A1 · Lei · 2019 [cited by examiner]
US 20190139054A1 · Mathrubootham et al. · 2019 [cited by applicant]
US 20190259043A1 · Koneri · 2019 [cited by examiner]
US 20190378074A1 · Mcphatter · 2019 [cited by applicant]
US 20200184417A1 · Rosenfeld et al. · 2020 [cited by applicant]
US 20200210922A1 · Devarakonda · 2020 [cited by examiner]
US 20200293946A1 · Sachan · 2020 [cited by applicant]
US 20210014136A1 · Rath · 2021 [cited by applicant]
US 20210049700A1 · Nguyen · 2021 [cited by applicant]
US 20210202103A1 · Bostic · 2021 [cited by applicant]
US 20210310075A1 · Grail · 2021 [cited by applicant]
US 20210343411A1 · Zhang et al. · 2021 [cited by applicant]
US 20210390498A1 · Ohlsson et al. · 2021 [cited by applicant]
US 20220138785A1 · Zarakas et al. · 2022 [cited by applicant]
US 20220172257A1 · Mustafi · 2022 [cited by applicant]
US 20220263731A1 · Gupta et al. · 2022 [cited by applicant]
US 20220358514A1 · Duff · 2022 [cited by applicant]
US 20230119881A1 · Makhija · 2023 [cited by applicant]
US 20230177516A1 · He · 2023 [cited by applicant]
US 20230177537A1 · Maclaughlin · 2023 [cited by applicant]
US 20230267370A1 · Copeland · 2023 [cited by applicant]
US 20230307115A1 · Kumar · 2023 [cited by applicant]
US 20240054447A1 · Kwon · 2024 [cited by applicant]
US 20240119346A1 · Chang et al. · 2024 [cited by applicant]
US 20240177841A1 · Mamaghani · 2024 [cited by applicant]
US 20240378494A1 · Biswas · 2024 [cited by applicant]
US 20240386372A1 · Vanbrocklin · 2024 [cited by applicant]
US 20250181592A1 · Khan · 2025 [cited by applicant]
US 20250181602A1 · Khan · 2025 [cited by applicant]
CN 106096657A · 2016 [cited by applicant]
CN 107515898A · 2017 [cited by applicant]
CN 108520045A · 2018 [cited by applicant]
CN 105022740A · 2025 [cited by applicant]
JP 2007141036A · 2007 [cited by applicant]
KR 1020180029593A · 2018 [cited by applicant]
KR 20200099284A · 2020 [cited by applicant]
KR 1020210042709A · 2021 [cited by applicant]
WO 2020163381A1 · 2020 [cited by applicant]
WO 2020245727A1 · 2020 [cited by applicant]
International Search Report and Written Opinion for International application No. PCT/US2023/080751 dated Aug. 20, 2024, pp. 1-11. [cited by applicant]
“LightGBM”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-3. [cited by applicant]
“Logistic regression”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-30. [cited by applicant]
“Long short-term memory”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-14. [cited by applicant]
“Machine learning”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-34. [cited by applicant]
“Naive Bayes classifier”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-12. [cited by applicant]
“Random forest”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-13. [cited by applicant]
“Support vector machine”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-13. [cited by applicant]
“Training, validation, and test data sets”, retrieved Nov. 16, 2023, pp. 1-6. [cited by applicant]
“XGBoost”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-4. [cited by applicant]
“Artifical neural network”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-38. [cited by applicant]
“Autoregressive model”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-12. [cited by applicant]
“BERT (language model)”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-6. [cited by applicant]
“CatBoost”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-3. [cited by applicant]
“Decision tree learning”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-12. [cited by applicant]
“Decomposition of time series”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-3. [cited by applicant]
“Deep learning”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-45. [cited by applicant]
“Ensemble learning”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-15. [cited by applicant]
“Feedforward neural network”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-7. [cited by applicant]
“Gated recurrent unit”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-4. [cited by applicant]
“Gradient boosting”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-10. [cited by applicant]
“K-nearest neighbors algorithm”, Wikipedia.org, retrieved Nov. 16, 2023, pp. 1-11. [cited by applicant]
“Cluster analysis”, Wikipedia.org, retrieved Nov. 20, 2023, pp. 1-22. [cited by applicant]
International Search Report and Written Opinion for International Application No. PCT/US2023/080231 dated Aug. 12, 2024, pp. 1-13. [cited by applicant]
International Search Report and Written Opinion for International application No. PCT/US2024/039962 dated Apr. 14, 2025, pp. 1-14. [cited by applicant]
Non-Final Office Action for U.S. Appl. No. 18/482,741 dated May 19, 2025, pp. 1. [cited by applicant]
Office Action for China Application No. 202080101992.0 dated Mar. 27, 2025, pp. 1-4. [cited by applicant]
Li, Dan, et al. “Machine learning-aided risk stratification system for the prediction of coronary artery disease.” International Journal of Cardiology 326 (2021): 30-34. (Year: 2021). [cited by applicant]
Non-Final Office Action for U.S. Appl. No. 17/999,045 dated Jun. 30, 2025, pp. 1-26. [cited by applicant]
Office Action for Chinese Application No. 202080101992.0 dated Jul. 30, 2025, pp. 1-9. [cited by applicant]
Non-Final Office Action for U.S. Appl. No. 18/786,540 dated Sep. 11, 2025, pp. 1-48. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US2023/076284, mailed on Jul. 2, 2024, pp. 1-13. [cited by applicant]
Extended European Search Report received for European Application No. 20938380.1, mailed on Nov. 30, 2023, pp. 1-5. [cited by applicant]
Non-Final Office Action for related U.S. Appl. No. 17/999,045 dated Jul. 2, 2024, pp. 1-44. [cited by applicant]
Artificial Intelligence-Enhanced Predictive Insights for Advancing Financial Inclusion: A Human-CentricAI-Thinking ApproachMeng-Leong How; Sin-Mei Cheah; Khor, Aik Cheow; Chan, Yong Jiet. Big Data and Cognitive Computin… [cited by applicant]
P. Ongsulee, V. Chotchaung, E. Bamrungsi and T. Rodcheewit, “Big Data, Predictive Analytics and Machine Learning,” 2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE), Bangkok, Thailand, 2018, p… [cited by applicant]