Machine Learning Contract Proposal System
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Solution Overview
Problem
The process of revising contracts for current contracts often requires multiple rounds, consuming significant power, processing, and network resources due to the use of computing devices and distributed revisions across sectors of an organization.
Innovation Solution
A method that unifies files from various data sources into a single format using scripts, updates a machine learning model with historical contracting information, and applies it to current contracts to predict probabilities and recommend modifications based on contract phases, reducing the need for revisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple rounds of revision are performed using computing devices to develop proposals and documents, then the quality and accuracy of contract proposals improve, but power consumption and processing resource usage increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing historical contracting data and pre-training machine learning models before actual contract proposals are needed. This allows the ML model to provide guidance during the proposal development process, reducing the number of revision rounds required and thereby lowering power consumption while maintaining proposal accuracy.
Solution Approach 2:
The patent replaces the traditional mechanical revision process (manual multiple rounds of editing and review) with an intelligent system based on machine learning. The ML model analyzes historical data and provides automated recommendations, substituting human-intensive mechanical revision cycles with computational analysis that consumes less power per iteration.
2Manufacturing precision
If revisions are performed across multiple sectors of an organization using distributed computing, then comprehensive review and improved proposal quality are achieved, but network resource consumption increases
Solution Approach 1:
The system merges historical contracting data from multiple organizational sectors into a unified dataset for ML training. By consolidating data sources and using a centralized ML model that can be deployed across sectors, the system reduces redundant network communication while maintaining comprehensive review capabilities through the model's access to integrated historical information.
Solution Approach 2:
The machine learning model serves as a universal tool that can be applied across different organizational sectors for contract proposal review. This multi-functional approach eliminates the need for separate review systems in each sector, reducing overall network resource consumption while maintaining comprehensive review quality through the model's ability to handle diverse contract types.
3Reliability
If numerous rounds of revision are conducted to develop contract proposals, then the reliability of the final contract document improves, but the time required for contract development increases
Solution Approach 1:
The system implements feedback by using the ML model to analyze historical contracting outcomes and provide actionable recommendations for improving current proposals. This feedback loop allows for more efficient revisions, as the model identifies specific areas for improvement based on patterns learned from historical data, reducing the number of rounds needed to achieve reliable contract documents.
Solution Approach 2:
The ML model performs preliminary analysis of contract proposals by evaluating them against historical success patterns before full review cycles begin. This preliminary assessment identifies potential issues early, allowing teams to address problems proactively rather than through multiple reactive revision rounds, thereby reducing development time while maintaining document reliability.
Data Source
AI summary
In some implementations, a planning system may receive multiple files in multiple formats and associated with historical contracting information. The planning system may convert the plurality of files into a unified data format, to generate a unified set of data, and may update a machine learning model based on the unified set of data. The planning system may receive input associated with a current contract and may select a set of factors based on a phase associated with the current contract. The planning system may apply the machine learning model to the input to generate a probability associated with the current contract and may provide instructions for a user interface that visually depicts the probability. The planning system may additionally generate recommended modifications to increase the probability. The recommended modifications may be fed back into a training (and retraining) cycle for the machine learning model to increase accuracy.