RFP Proposal Code Assignment for Unstructured Data Matching
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Solution Overview
Problem
Existing computer processes struggle with effectively processing unstructured data such as text, images, and videos, which are not easily analyzed and structured for efficient automation and decision-making.
Innovation Solution
An apparatus and method that utilizes a machine learning model to assign proposal codes to unstructured data objects within a request for proposal (RFP), transforming them into structured formats using optical character recognition, natural language processing, and machine learning to identify and categorize implicit data objects, enabling efficient evaluation and matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If unstructured data is processed using traditional computer processes, then data processing capability is maintained at basic levels, but data analysis accuracy and automation efficiency deteriorate
Solution Approach 1:
The patent replaces traditional mechanical data processing systems with machine learning models that can automatically identify, extract, and classify implicit data objects from unstructured text. The machine learning model learns patterns from training data and autonomously performs data structuring without requiring complex manual intervention or predefined rigid frameworks, thereby improving accuracy while managing complexity through intelligent automation.
Solution Approach 2:
The system transforms unstructured data into structured format by changing the data's organizational parameters. The machine learning model reorganizes raw text data into categorized implicit data objects with assigned proposal codes, converting the data from an unstructured state to a structured state that can be efficiently processed by algorithms, thus improving measurement precision through parameter reorganization.
2Productivity
If machine learning models are used to assign proposal codes, then data structuring capability is improved, but computational resource requirements worsen
Solution Approach 1:
The system performs preliminary training of the machine learning model using training data before actual data processing. During training, the model learns to identify and classify implicit data objects, proposal codes, and their relationships. Once trained, the model can efficiently process new unstructured data without requiring the same computational resources, as the learning has already been performed in advance, thus improving productivity while managing resource consumption through pre-computation.
Data Source
AI summary
An apparatus for assigning one or more proposal codes to a request for proposal is disclosed. The apparatus includes a processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a plurality of profiles and at least one RFP. The memory instructs the processor to identify a set of implicit data objects for the at least one RFP. The memory instructs the processor to assign one or more proposal codes to each implicit data object of the set of implicit data objects. The memory instructs the processor to generate a vendor score for each profile as a function of a comparison of each profile to the one or more proposal codes. The memory instructs the processor to match at least one profile of the plurality of profiles to the at least one RFP as a function of the vendor score.