17846359. JOINT LEARNING OF TIME-SERIES MODELS LEVERAGING NATURAL LANGUAGE PROCESSING simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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JOINT LEARNING OF TIME-SERIES MODELS LEVERAGING NATURAL LANGUAGE PROCESSING

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Shikhar Kwatra of San Jose CA (US)

Shubhi Asthana of Santa Clara CA (US)

PAWAN RAGHUNATH Chowdhary of San Jose CA (US)

Indervir Singh Banipal of Austin TX (US)

JOINT LEARNING OF TIME-SERIES MODELS LEVERAGING NATURAL LANGUAGE PROCESSING - A simplified explanation of the abstract

This abstract first appeared for US patent application 17846359 titled 'JOINT LEARNING OF TIME-SERIES MODELS LEVERAGING NATURAL LANGUAGE PROCESSING

Simplified Explanation

The patent application describes methods, computer program products, and systems for maximizing renewals of purchase orders. It involves using a classifier machine learning model to identify relevant metrics for predicting whether customers will renew purchase orders. The method also includes applying a tone analyzer natural language processing (NLP) model to determine the current sentiments of customers and recommending which customers to pursue with additional resources based on their sentiments and risks of non-renewal.

  • Utilizes a classifier machine learning model to identify relevant metrics for predicting purchase order renewals
  • Predicts risks of non-renewal for purchase orders using the identified metrics
  • Applies a tone analyzer natural language processing (NLP) model to determine the current sentiments of customers
  • Recommends which customers to pursue with additional resources based on their sentiments and risks of non-renewal

Potential Applications

This technology can be applied in various industries and sectors where purchase orders and customer renewals are significant, such as:

  • E-commerce platforms
  • Subscription-based services
  • Supply chain management
  • Retail businesses

Problems Solved

This technology addresses the following problems:

  • Difficulty in identifying relevant metrics for predicting purchase order renewals
  • Lack of efficient methods to assess risks of non-renewal for purchase orders
  • Inability to determine the sentiments of customers accurately
  • Challenges in deciding which customers to focus on for maximizing renewals

Benefits

The use of this technology offers several benefits:

  • Improved accuracy in predicting purchase order renewals
  • Enhanced risk assessment for non-renewal of purchase orders
  • Better understanding of customer sentiments through natural language processing
  • Optimal allocation of resources towards customers with higher chances of renewal


Original Abstract Submitted

Disclosed are methods, computer program products, and systems for maximizing renewals of purchase orders. One embodiment of the method may comprise utilizing a classifier machine learning model to identify metrics that are most relevant to whether customers will renew purchase orders, predicting respective risks of non-renewal for the purchase orders using the identified metrics, applying a tone analyzer natural language processing (NLP) model to determine current sentiments for respective customers, and recommending which of the respective customers to pursue with additional resources based the respectively determined sentiments and risks of non-renewal.

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