AI Quote Request Templates for Equity Derivative Pricing

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Solution Overview

Problem

The manual entry of financial product attributes from unstructured natural language in equity derivative sales is error-prone and inefficient, requiring a system to automate the sequencing of pricing requests and communicate pricing details effectively.

Innovation Solution

A system utilizing AI and ML tools to automatically format natural language requests for equity derivative products into standardized template formats, extract attributes, and retrieve price quotes from pricing systems, incorporating NLP techniques for unstructured communication.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If manual entry of financial product attributes from unstructured natural language is used, then flexibility in handling diverse client requests is maintained, but error rate increases and processing efficiency decreases

Engineering Contradiction:

Improveaccuracy of attribute extractionVSAvoidprocessing speed of pricing requests

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system enables self-service by automatically extracting attributes from unstructured natural language requests using NLP and ML techniques. The AI-based processor autonomously identifies financial product attributes, formats them into standardized templates, and transmits them to pricing systems without requiring manual data entry, thereby eliminating human error while maintaining processing flexibility.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual entry process with an automated AI-based system. Natural language processing algorithms and machine learning models substitute human operators, automatically parsing unstructured communication channels (chat, voice, email) to extract structured financial product attributes, thereby improving both accuracy and processing speed simultaneously.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated AI-based processing is implemented, then processing efficiency and accuracy improve, but system complexity increases

Engineering Contradiction:

Improveprocessing speed of pricing requestsVSAvoidcomplexity of pricing system architecture

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The complex AI-based pricing system is segmented into distinct functional modules: a natural language processing module for extracting attributes from unstructured communication, a template generation module for formatting requests, and a pricing system interface module for transmitting and receiving quotes. This modular segmentation manages system complexity by organizing functions into independent, manageable components while maintaining high processing efficiency.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces standardized templates as an intermediary layer between the unstructured natural language input and the pricing system. These templates act as a mediator that translates diverse client requests into a consistent format, simplifying the interface requirements and reducing the complexity of direct integration between multiple communication channels and the pricing system.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If multiple unstructured communication channels are supported, then client interaction flexibility improves, but difficulty in extracting and formatting attributes increases

Engineering Contradiction:

Improveability to handle diverse communication formatsVSAvoiddifficulty of attribute extraction from unstructured text

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The NLP-based attribute extraction system is designed with universal functionality to handle multiple unstructured communication channels including chat, voice, and email. The machine learning model is trained to recognize and extract financial product attributes across different communication formats and languages, providing a unified solution that maintains adaptability while managing extraction complexity through standardized processing pipelines.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12579576B2Method and system for artificial intelligence based enhancement of sale processes

Publication Date: 2026.03.17 JPMORGAN CHASE BANK NA

AI summary

The method includes: receiving a communication that relates to a request for a price quote for at least one equity derivative product; extracting from the communication, attributes of the requested price quote for the at least one equity derivative product; generating based on the extracted attributes, a template request that has a predetermined format for each of the at least one equity derivative product; displaying, via a graphical user interface (GUI), each template request for review by a user, wherein the GUI includes an input mechanism for at least one of accepting and modifying each template request; transmitting each reviewed template request to a pricing system; receiving, from the pricing system, a quote for each of the at least one equity derivative product; and displaying, by the GUI, the price quote.