Natural Language Order Fill Module for Trading Tickets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional trading ticket entry processes are structured, time-consuming, and inflexible, often leading to errors and delays, especially for time-sensitive trades, as they require specific formats and do not support copy-paste functionality.
Innovation Solution
Implementing a natural language order fill module with a free-form text box that uses neural networks and a bi-directional LSTM model to interpret and automatically fill trading ticket fields from user input, including abbreviations and varying sentence syntax, and allows for copied and pasted data from previous entries or emails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a structured ticket entry form is used, then data accuracy is improved, but entry time and operational complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and structuring trade detail data before the user needs to enter it. The natural language processing engine analyzes incoming trade details (from emails, chats, or other sources) and pre-structures the data into relevant fields, so when the user needs to create a ticket, the information is already organized and ready for verification rather than requiring manual entry from scratch.
Solution Approach 2:
The system creates copies of trade detail information from various sources (emails, chat messages, external systems) and transforms these copies into structured ticket data. Instead of requiring users to manually re-type information, the system copies the relevant data, structures it according to ticket requirements, and presents it for user confirmation, dramatically reducing entry time while maintaining accuracy through automated field mapping.
2Manufacturing precision
If a structured ticket entry form is used, then data accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system inverts the traditional approach by not requiring users to fill out structured forms manually. Instead, it accepts unstructured natural language input from users (or from external sources) and automatically transforms it into the required structured ticket format. This reversal maintains data accuracy through automated validation while dramatically improving ease of operation, as users can enter information in their natural communication style rather than adapting to rigid form structures.
3Adaptability or versatility
If manual ticket entry is performed, then flexibility is reduced, but device complexity decreases
Solution Approach 1:
The system introduces a natural language processing engine as an intermediary between the user's natural language input and the structured ticket system. This intermediary layer handles the complexity of parsing, understanding, and transforming unstructured input into structured data, while presenting a simple interface to users. The intermediary absorbs the system complexity, allowing users to benefit from increased flexibility without directly interacting with the complex processing mechanisms.
4Productivity
If multiple tickets are entered manually, then productivity decreases, but error rate increases
Solution Approach 1:
The system enables self-service by automatically processing trade detail information and generating ticket entries without requiring manual data entry. The natural language processing engine autonomously analyzes incoming trade details, extracts relevant information, maps it to appropriate ticket fields, and creates structured tickets. This self-service capability dramatically improves productivity by eliminating repetitive manual entry while reducing error rates through automated validation and consistent field mapping.
Data Source
AI summary
Various methods, apparatuses/systems, and media for implementing a natural language order fill module are disclosed. A database stores data corresponding to trade details of a particular trade. A processor is operatively coupled to the database via a communication network. The processor configures a free-form text box to receive user input data corresponding to the trade details (e.g., abbreviations data, varying sentence syntax data, special characters data and details data, etc.) of the particular trade; implements neural networks algorithm to interpret and extract key trade details data from the receive user input data corresponding to the trade details of the particular trade regardless of text structure of the input data; and automatically fills in corresponding trading ticket fields in real-time based on the extracted key trade details data.