Protected Data Segmentation for Dynamic Order Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current dynamic order management systems rely heavily on phone calls and instant messaging, leading to leaks of confidential information, inefficiencies, miscommunication, and unfulfilled orders due to unintentional or nefarious actions by parties, and delays caused by underlyer shifts during order execution.
Innovation Solution
A multi-system paradigm that bifurcates data into protected and unprotected categories, obfuscates sensitive information, and models products as single units for efficient and secure dynamic order management, using an execution system to manage and execute orders while maintaining anonymity and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If confidential information is transmitted to respondents during dynamic order management, then respondents can make informed bids and offers, but data security is compromised and information leaks occur
Solution Approach 1:
The patent segments order data into protected attributes (e.g., identity, reserve price, order type) and unprotected attributes (e.g., underlyer, quantity). The execution system selectively transmits only unprotected attributes to respondent systems, enabling them to make bids and offers without accessing confidential information. This segmentation resolves the contradiction by maintaining data security while preserving operational capability.
Solution Approach 2:
The execution system acts as an intermediary between the originator system and respondent systems. It receives the dynamic order, bifurcates the data, and transmits only the necessary unprotected data to respondents. This intermediary role enables respondents to execute orders effectively while preventing confidential information from being exposed, thus resolving the security-operations contradiction.
2Productivity
If all order data is transmitted to multiple systems for processing, then comprehensive order management is achieved, but network congestion and storage requirements increase
Solution Approach 1:
The execution system extracts and withholds protected data from the data transmission process. Instead of transmitting all order attributes to respondent systems, only the unprotected attributes necessary for bid and offer formulation are transmitted. This extraction reduces network data volume and storage requirements while maintaining comprehensive order management through centralized execution control.
Solution Approach 2:
By segmenting order data into protected and unprotected categories, the system transmits only the minimal necessary data subset to multiple respondent systems. This segmentation reduces network congestion and storage requirements across the distributed system while maintaining comprehensive order management capabilities through the execution system's centralized coordination.
3Reliability
If protected data is stored in multiple locations for redundancy, then data availability is improved, but storage requirements and interception risk increase
Solution Approach 1:
The patent extracts protected data from the distributed storage architecture and retains it exclusively in the execution system. Only unprotected data is distributed to originator and respondent systems. This extraction reduces storage requirements and interception risks across the network while maintaining data availability through the execution system's centralized secure storage and controlled disclosure.
Data Source
AI summary
A system for receiving a product dataset associated with a product comprising one or more subproducts, a first corresponding product dataset associated with a first corresponding product, and a second corresponding product dataset associated with a second corresponding product. A system may determine a product model output corresponding to the product, wherein the product model output is a single, value-weighted indicator of the product. A system may determine a first variability index corresponding to the first corresponding product based at least on the product model output and the first corresponding product dataset, and a second variability index corresponding to the second corresponding product based at least on the product model output and the second corresponding product dataset. A system may display a visual indicator on a graphical user interface the first variability index and the second variability index.