Automated Capacity Data Parsing for Logistics Matching
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Solution Overview
Problem
The existing logistics communication systems face challenges in efficiently matching transportation service providers' capacity with demand, due to issues with data formatting, privacy concerns, and limited ability to parse and process capacity messages from various sources, leading to inefficiencies in brokerage services and increased costs.
Innovation Solution
An automated system that uses machine learning to parse and format capacity data from communications such as emails, SMS, and other interfaces, automatically matching demand with available transportation services by identifying patterns and preferences, and predicting future capacity, while ensuring data privacy and compatibility with multiple brokerage formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated parsing and formatting of capacity data from various communication sources is implemented, then data processing efficiency and matching accuracy are improved, but system complexity and initial costs increase
Solution Approach 1:
The patent introduces an automated parsing and formatting system that acts as an intermediary between various communication sources (emails, SMS, interfaces) and the capacity data repository. This intermediary automatically extracts capacity information from diverse formats, standardizes it, and stores it in a unified structure, thereby improving data processing efficiency without requiring manual intervention for each communication source.
Solution Approach 2:
The system transforms capacity data from various communication formats into a standardized parameter structure. By changing the parameters of incoming data (parsing different formats) and converting them to a uniform format (preferred data format), the system achieves efficient processing and accurate matching while managing complexity through standardization.
2Measurement precision
If machine learning algorithms are used to identify patterns and predict future capacity, then matching accuracy and resource utilization are improved, but computational requirements and processing time increase
Solution Approach 1:
The patent implements machine learning algorithms that perform preliminary analysis of historical capacity data to identify patterns and predict future capacity availability. By conducting this analysis in advance, the system improves matching accuracy between available capacity and demand, allowing transportation service providers to proactively prepare for future opportunities rather than reactively responding to requests.
3Adaptability or versatility
If comprehensive capacity data aggregation from multiple sources is performed, then service matching capability is improved, but data privacy risks and security concerns increase
Solution Approach 1:
The patent extracts only the necessary capacity information from various communication sources while leaving sensitive personal data behind. The automated parsing system identifies and extracts specific capacity-related parameters (availability, capacity type, timeframes) from emails, SMS messages, and interface communications, aggregating this extracted data without capturing unrelated personal information, thereby maintaining privacy while improving matching capability.
4Manufacturing precision
If manual review and formatting of capacity messages is required, then data accuracy is improved, but processing speed and operational efficiency deteriorate
Solution Approach 1:
The patent implements an automated system that performs self-service parsing and formatting of capacity data. The system automatically receives communications from various sources, parses the capacity information, formats it according to preferred standards, and stores it in the database without requiring manual review or formatting operations. This self-service approach maintains data accuracy through automated validation rules while dramatically improving processing speed and operational efficiency.
Data Source
AI summary
Communications are received from a transportation service provider and are parsed for data indicating capacity data. The data is then matched to a stored format for capacity data and the parsed data is extracted from the data matched to the stored format to a preferred data format, and reviewed to determine if the stored data reflects capacity indicated by the received communication. The data is then combined with preference data and performance data concerning the transportation service data and used to automatically match a demand for transportation services with the capacity to provide the transportation services.