NLP System for Dynamic Project Prioritization
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Solution Overview
Problem
Current program and project management systems for medication providers face challenges in efficiently prioritizing and optimizing resource allocation due to manual review processes that are time-consuming, biased, and lack critical information, leading to suboptimal decision-making.
Innovation Solution
A system and method that utilize natural language processing to dynamically score projects, identify resource domains, and provide data-driven value prioritization, using a processor and memory with instructions to receive data objects, identify free form textual information, generate feature vectors, and employ an artificial intelligence engine with a machine learning model to predict resource domains and their probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review processes are used for project prioritization and resource allocation, then human judgment and flexibility are maintained, but the process becomes time-consuming and prone to bias
Solution Approach 1:
The patent replaces manual review processes with an automated natural language processing system that uses machine learning models to analyze project descriptions, extract features, and generate priority scores. This substitution eliminates human bias and time constraints while maintaining or improving decision accuracy through consistent, data-driven evaluation of multiple projects simultaneously.
Solution Approach 2:
The system enables projects to be automatically evaluated and prioritized without human intervention by using AI models to process project descriptions, extract relevant features, calculate priority scores, and generate rankings. This self-service approach allows the system to handle large volumes of projects independently, freeing human reviewers from time-consuming manual analysis.
2Loss of information
If manual review processes are used for project analysis, then human expertise can be applied, but critical information may be missed and decision-making becomes suboptimal
Solution Approach 1:
The patent segments the project analysis process into distinct components: text preprocessing, feature extraction (using unigrams, bigrams, trigrams), machine learning model processing, and priority scoring. This segmentation allows the system to systematically analyze different aspects of project descriptions without missing critical information, while maintaining high productivity through automated parallel processing of multiple projects.
Solution Approach 2:
The patent introduces an intermediary AI system that acts as a bridge between project descriptions and decision-making. This intermediary automatically extracts and analyzes critical information from unstructured text, identifies key features and patterns, and transforms them into structured priority scores, ensuring no critical information is lost while dramatically improving decision-making efficiency.
3Adaptability or versatility
If traditional resource allocation methods are used, then simplicity is maintained, but resource optimization and value prioritization are suboptimal
Solution Approach 1:
The patent changes the parameters of resource allocation by introducing dynamic priority scores based on multiple features extracted from project descriptions. Instead of using static or simple allocation methods, the system calculates comprehensive scores considering various project attributes, enabling flexible and adaptive resource allocation that optimizes value while the automated nature maintains manageable system complexity.
Data Source
AI summary
A method includes receiving at least one data object and identifying, for a first aspect of the at least one data object, text strings of free form textual information having a first text string type. The method also includes generating updated free form textual information by removing the at least one text string having the first text string type. The method also includes generating one or more feature vectors based on the updated free form textual information using at least one of a unigram, a bigram, and a trigram. The method also includes using an artificial intelligence engine that uses at least one machine learning model configured to provide, using the one or more feature vectors, an output that includes at least one prediction indicating at least one resource domain and a weight value indicating a probability that the at least one resource domain corresponds to the free form textual information.