Job Feed Parser Using Neural Networks for Automated Mapping
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Solution Overview
Problem
The integration of job posting applications with multiple heterogeneous job boards is inefficient due to the need for manual configuration and technical skill, consuming significant time and resources, especially when dealing with different data formats like XML, JSON, or CSV.
Innovation Solution
The use of machine learning, specifically neural networks, to determine mappings between job posting system fields and items with job board tags and values, facilitated by copying and pasting job board specifications, allowing for automated conversion and integration across disparate platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration methods are used to map fields between job posting applications and job boards, then integration can be achieved, but the process consumes significant time and resources
Solution Approach 1:
The system performs self-service by automatically generating field mappings between the job posting application and job boards using machine learning. The neural network model autonomously analyzes job board specifications and creates mapping configurations without requiring manual intervention from developers or technical staff, thereby reducing both time consumption and resource requirements while maintaining integration accuracy.
Solution Approach 2:
The patent replaces the mechanical manual configuration process with an automated machine learning system. Instead of relying on human operators to manually map fields and configure integrations, a neural network model processes job board specifications and generates mappings automatically, substituting the mechanical human operation with an automated computational system that operates faster and with consistent accuracy.
2Extent of automation
If semi-automated tools are used to parse job postings and create proposed mappings, then some automation is achieved, but the tools require technical skill and remain time-consuming
Solution Approach 1:
The system eliminates the need for user intervention by performing the complete mapping generation process automatically. The neural network model independently parses job board specifications, understands the data structures, and generates accurate field mappings without requiring users to have technical skills or to review/edit the mappings manually.
Solution Approach 2:
The patent changes the operational parameters of the mapping tool by transitioning from a semi-automated approach requiring user input and review to a fully automated machine learning approach. The system adjusts the automation level parameter to maximum, where the neural network model handles the entire mapping generation process without human intervention, thereby improving ease of operation while maintaining high automation.
3Reliability
If multiple stakeholders and manual review processes are involved in configuration, then integration accuracy can be ensured, but the process becomes complex and resource-intensive
Solution Approach 1:
The patent extracts the mapping generation function from the complex multi-stakeholder configuration process and consolidates it into a single machine learning model. By taking out the field mapping task from the broader integration configuration process and handling it autonomously through the neural network, the system maintains mapping accuracy while dramatically simplifying the overall configuration process complexity.
Solution Approach 2:
The patent substitutes the mechanical multi-stakeholder review process with an automated machine learning system. Instead of involving multiple people in sequential review and approval steps, the neural network model performs the mapping generation and validation automatically, replacing the complex human coordination mechanism with a streamlined computational process that maintains accuracy without the complexity.
4Adaptability or versatility
If custom development activities are queued and prioritized for each job board integration, then specific integration requirements can be met, but productivity is reduced
Solution Approach 1:
The patent applies universality by designing a machine learning model that can handle multiple different job board specifications and integration requirements through a single unified system. The neural network model is trained to recognize and process various data formats and field structures, enabling it to generate appropriate mappings for different job boards without requiring separate custom development processes, thereby maintaining adaptability while improving productivity.
Solution Approach 2:
The system performs preliminary action by pre-training the machine learning model on diverse job board specifications and integration patterns. This preliminary training enables the model to quickly generate accurate mappings for new job boards without requiring time-consuming custom development and prioritization queues, thereby maintaining the ability to meet specific integration requirements while significantly improving integration speed and productivity.
Data Source
AI summary
Systems include reception of a plurality of tags associated with a first job board and, for each of the plurality of tags, one or more values associated with the first job board, input of each of the plurality of tags to a first neural network to determine a field of a job posting system associated with the tag, wherein the first neural network is trained based on a plurality of mappings from fields of the job posting system to tags associated with a plurality of job boards, input of each of the one or more values of each of the plurality of tags to a second neural network to determine an item of the job posting system associated with the value, wherein the second neural network is trained based on a plurality of mappings from items of the job posting system to values associated with the plurality of job boards, creation of first mappings from each of the plurality of tags associated with the first job board to the determined field associated with the tag, creation of second mappings from each of the one or more values of each of the plurality of tags to the determined item associated with the value, and storage of the first mappings and the second mappings in association with the first job board.