Identifying data of interest using machine learning

Patent metadata

Field	Value
Publication number	US-12210836-B2
Application number	US-202318206296-A
Country	US
Kind code	B2
Filing date	Jun 6, 2023
Priority date	Dec 11, 2018
Publication date	Jan 28, 2025
Grant date	Jan 28, 2025

Abstract

Systems and methods for identifying data of interest are disclosed. The system may retrieve unstructured data from an internet data source via an alert system or RSS feed. The system may input the unstructured data into various models and scoring systems to determine whether the data is of interest. The models and scoring systems may be executed in order or in parallel. For example, the system may input the unstructured data into a Naïve Bayes machine learning model, a long short-term memory (LSTM) machine learning model, a named entity recognition (NER) model, a semantic role labeling (SRL) model, a sentiment scoring algorithm, and/or a gradient boosted regression tree (GBRT) machine learning model. Based on determining that the unstructured data is of interest, a data alert may be generated and transmitted for manual review or as part of an automated decisioning process.

First claim

What is claimed is:

1. A method comprising:
retrieving, by a processor, unstructured data from an internet data source, wherein the retrieval is performed as a parallel process to evaluate data from various data sources;
preprocessing, by the processor, the unstructured data by performing a part-of-speech tagging process;
inputting, by the processor, the preprocessed unstructured data into a plurality of machine learning models and a sentiment scoring engine in parallel to process the preprocessed unstructured data to identify a given set of topics;
generating, a plurality of confidence scores as outputs of the plurality of machine learning models and the sentiment scoring engine, representing probabilities indicating whether the preprocessed unstructured data is of interest to an entity;
combining the plurality of confidence scores to obtain a combined confidence score representing a probability indicating whether the preprocessed unstructured data is of interest to the entity; and
generating, by the processor, a data alert in response to identifying the data of interest.

2. The method of claim 1 , further comprising transmitting, by the processor, the data alert to a financial decisioning system to be used in a financial decisioning process of an account of a business.

3. The method of claim 2 , wherein the financial decisioning process comprises:
closing or limiting credit accounts, extending lines of credit, opening transaction accounts, or closing transaction accounts.

4. The method of claim 1 , wherein the internet data source comprises: news articles, blogs, social media, or forums.

5. The method of claim 1 , wherein the plurality of machine learning models comprise: a Naïve Bayes model, a long-short term memory (LSTM) model, a name entity recognition (NER) model, or a semantic role labeling (SRL) model.

6. The method of claim 1 , further comprising:
inputting, by the processor, the preprocessed unstructured data into the plurality of machine learning models and the sentiment scoring engine in a stage wise manner.

7. A non-transitory computer readable medium that when executed by one or more processors or a computing system, cause the computing system to perform operations comprising:
retrieving unstructured data from an internet data source, wherein the retrieval is performed as a parallel process to evaluate data from various data sources;
preprocessing the unstructured data by performing a part-of-speech tagging process;
inputting the preprocessed unstructured data into a plurality of machine learning models and a sentiment scoring engine in parallel to process the preprocessed unstructured data to identify a given set of topics;
generating, a plurality of confidence scores as outputs of the plurality of machine learning models and the sentiment scoring engine, representing probabilities indicating whether the preprocessed unstructured data is of interest to an entity;
combining the plurality of confidence scores to obtain a combined confidence score representing a probability indicating whether the preprocessed unstructured data is of interest to the entity; and
generating a data alert in response to identifying the data of interest.

8. The non-transitory computer readable medium of claim 7 , wherein the operations further comprise transmitting the data alert to a financial decisioning system to be used in a financial decisioning process of an account of a business.

9. The non-transitory computer readable medium of claim 8 , wherein the financial decisioning process comprises: closing or limiting credit accounts, extending lines of credit, opening transaction accounts, or closing transaction accounts.

10. The non-transitory computer readable medium of claim 7 , wherein the internet data source comprises: news articles, blogs, social media, or forums.

11. The non-transitory computer readable medium of claim 7 , wherein the plurality of machine learning models comprise: a Naïve Bayes model, a long-short term memory (LSTM) model, a name entity recognition (NER) model, or a semantic role labeling (SRL) model.

12. The non-transitory computer readable medium of claim 7 , wherein the operations further comprise:
inputting the preprocessed unstructured data into the plurality of machine learning models and the sentiment scoring engine in a stage wise manner.

13. A computing system comprising:
a memory storing instructions; and
one or more processors, coupled to the memory, and configured to process the stored instructions to:
retrieve unstructured data from an internet data source, wherein the retrieval is performed as a parallel process to evaluate data from various data sources;
preprocess the unstructured data by performing a part-of-speech tagging process;
input the preprocessed unstructured data into a plurality of machine learning models and a sentiment scoring engine in parallel to process the preprocessed unstructured data to identify a given set of topics;
generate, a plurality of confidence scores as outputs of the plurality of machine learning models and the sentiment scoring engine, representing probabilities indicating whether the preprocessed unstructured data is of interest to an entity;
combine the plurality of confidence scores to obtain a combined confidence score representing a probability indicating whether the preprocessed unstructured data is of interest to the entity; and
generate a data alert in response to identifying the data of interest.

14. The computing system of claim 13 , wherein the one or more processors are further configured to:
transmit the data alert to a financial decisioning system to be used in a financial decisioning process of an account of a business, and
wherein the financial decisioning process comprises: closing or limiting credit accounts, extending lines of credit, opening transaction accounts, or closing transaction accounts.

15. The computing system of claim 13 , wherein the internet data source comprises: news articles, blogs, social media, or forums.

16. The computing system of claim 13 , wherein the plurality of machine learning models comprise: a Naïve Bayes model, a long-short term memory (LSTM) model, a name entity recognition (NER) model, or a semantic role labeling (SRL) model.

17. The computing system of claim 13 , wherein the one or more processors are further configured to:
input the preprocessed unstructured data into the plurality of machine learning models and the sentiment scoring engine in a stage wise manner.

Assignees

• American Express Travel Related Services Co Inc

Inventors

• Batra Ravi
• Bose Sandeep
• Fragoso Mario
• Ghuman Ravneet
• Gudur Madhu Sudan Reddy
• Madnani Suraj
• Merryweather Curtis T
• Varma Ravi
• Yadav Vinod

Classifications

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06F40/295

  Named entity recognition · CPC title

• G06N20/00

  Machine learning · CPC title