20240013278. SYSTEM AND METHOD FOR CONTEXT BASED RECOMMENDATIONS simplified abstract (JPMorgan Chase Bank, N.A.)

SYSTEM AND METHOD FOR CONTEXT BASED RECOMMENDATIONS

Organization Name

JPMorgan Chase Bank, N.A.

Inventor(s)

Krishna Kota of Garnet Valley PA (US)

Sunita Kumar of Glen Mills PA (US)

Anupam Arora of Middletown DE (US)

Preethi Motakuri of Bear DE (US)

Nikhita Devgan of Wilmington DE (US)

Rabeet Butt of Belcamp MD (US)

Saumil Patel of Bear DE (US)

Antonio Pires Ferreira of Hodgenville KY (US)

Vishnuvardhan Pondugula of Garnet Valley PA (US)

SYSTEM AND METHOD FOR CONTEXT BASED RECOMMENDATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240013278 titled 'SYSTEM AND METHOD FOR CONTEXT BASED RECOMMENDATIONS

Simplified Explanation

The patent application describes methods, apparatuses/systems, and media for automatically providing context-based product recommendations. The system involves a processor that establishes a communication link between an application and one or more Internet of Things (IoT) devices wearable by a customer. The processor receives input data from the IoT devices and assesses the customer's surroundings based on the received data. It generates surroundings data and customer context data based on the assessed surroundings and received input data. The system then implements a classification algorithm to identify products with matching offerings provided by the institution corresponding to the generated customer context data. It receives input corresponding to customer consent to receive the identified products and displays the identified products onto the application in response to receiving the customer consent.

• The patent application describes a system that automatically provides context-based product recommendations to customers.
• The system uses IoT devices worn by the customer to gather input data about their surroundings.
• The system assesses the customer's surroundings based on the received data and generates surroundings data.
• It also generates customer context data based on the received input data and surroundings data.
• The system then uses a classification algorithm to identify products with matching offerings provided by the institution.
• The identified products are displayed on the customer's application when they give consent to receive them.

Potential applications of this technology:

• E-commerce platforms can use this system to provide personalized product recommendations to customers based on their surroundings and context.
• Retail stores can implement this system to offer targeted promotions and discounts to customers based on their location and preferences.
• Service providers can use this technology to suggest relevant services to customers based on their current situation and needs.

Problems solved by this technology:

• This system solves the problem of manually searching for relevant products or services by automatically identifying and recommending them based on the customer's context.
• It addresses the challenge of providing personalized recommendations by considering the customer's surroundings and preferences.

Benefits of this technology:

• Customers can receive tailored product recommendations that match their current needs and preferences.
• Institutions can increase sales and customer satisfaction by offering personalized and relevant products or services.
• The system simplifies the process of finding and selecting products or services by automatically presenting suitable options to the customer.

Original Abstract Submitted

various methods, apparatuses/systems, and media for automatically providing context-based product recommendation are disclosed. a processor establishes a communication link between an application and one or more internet of things (iot) devices wearable by a customer having an account relationship with an institution; receives input data from the iot devices; assesses surroundings of the customer based on the received input data; generates surroundings data based on the assessed surroundings of the customer; generates customer context data based on the received input data and the surroundings data; implements a classification algorithm to identify products with matching offerings provided by the institution corresponding to the generated customer context data; receives input corresponding to customer consent to receive the identified products; and displays, in response to receiving the customer consent, the identified products onto the application via a predefined communication channel.