17932463. SYSTEMS AND METHODS FOR PROVIDING CUSTOMER-BEHAVIOR-BASED DYNAMIC ENHANCED ORDER CONVERSION simplified abstract (Verizon Patent and Licensing Inc.)

SYSTEMS AND METHODS FOR PROVIDING CUSTOMER-BEHAVIOR-BASED DYNAMIC ENHANCED ORDER CONVERSION

Organization Name

Verizon Patent and Licensing Inc.

Inventor(s)

Prakash Ranganathan of Villupuram (IN)

Miruna Jayakrishnasamy of Vellore (IN)

SYSTEMS AND METHODS FOR PROVIDING CUSTOMER-BEHAVIOR-BASED DYNAMIC ENHANCED ORDER CONVERSION - A simplified explanation of the abstract

This abstract first appeared for US patent application 17932463 titled 'SYSTEMS AND METHODS FOR PROVIDING CUSTOMER-BEHAVIOR-BASED DYNAMIC ENHANCED ORDER CONVERSION

Simplified Explanation

The patent application describes a device that processes customer data using machine learning models to generate recommendations for the customer.

• The device receives dynamic and static customer data.
• It calculates additional customer data based on the dynamic and static customer data.
• The device uses machine learning models to determine next action and next sequence predictions.
• It generates a recommendation for the customer based on the various outputs.

Potential Applications

This technology could be applied in various industries such as e-commerce, marketing, and customer service to provide personalized recommendations to customers based on their data.

Problems Solved

This technology helps businesses better understand their customers' preferences and behaviors, allowing them to tailor their offerings and communications to improve customer satisfaction and loyalty.

Benefits

The benefits of this technology include increased customer engagement, higher conversion rates, improved customer retention, and overall enhanced customer experience.

Potential Commercial Applications

One potential commercial application of this technology could be in online retail, where personalized product recommendations can lead to increased sales and customer satisfaction.

Possible Prior Art

One possible prior art for this technology could be recommendation systems used in e-commerce platforms or streaming services, which also analyze customer data to provide personalized suggestions.

How does the device ensure the privacy and security of customer data during processing?

The patent application does not provide details on the privacy and security measures implemented to protect customer data.

What are the potential limitations or challenges of implementing this technology in real-world scenarios?

The patent application does not address any potential limitations or challenges that may arise when implementing this technology in practical applications.

Original Abstract Submitted

A device may receive dynamic customer data and static customer data, and may calculate additional customer data based on the dynamic customer data and the static customer data. The device may process the static customer data, the dynamic customer data, and the additional customer data, with a first machine learning model, to determine a next action prediction, and may process the static customer data, the dynamic customer data, and the additional customer data, with a second machine learning model, to determine a next sequence prediction. The device may concatenate the static customer data, the dynamic customer data, the additional customer data, the next action prediction, and the next sequence prediction to generate concatenated data, and may process the concatenated data, with a plurality of machine learning models, to calculate various outputs, and may generate a recommendation for the customer based on the various outputs.