AI Innovation Impact Scoring System
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Solution Overview
Problem
Large organizations face challenges in efficiently analyzing and selecting innovations due to a high volume of submissions, leading to delays in identifying and implementing projects that could improve operations, resulting in degraded performance and resource misallocation.
Innovation Solution
An enterprise management system utilizing multiple machine learning models for classification, content analysis, ranking, and impact assessment to determine an impact score for innovations, facilitating project development and resource allocation based on historical data and user submissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of innovation submissions is performed by representatives, then judgments about impact can be made, but the volume of submissions overwhelms the analysis capacity leading to delays
Solution Approach 1:
The patent introduces machine learning models as intermediary systems between the large volume of innovation submissions and the human representatives. These models automatically analyze submissions, extract features, and generate preliminary impact assessments, thereby handling the high-volume filtering task while preserving human judgment capability for final decisions on high-priority innovations.
Solution Approach 2:
The patent replaces the manual mechanical analysis process with automated machine learning-based analysis. The system uses trained models to automatically process submissions, classify innovations, and estimate impact scores, substituting human manual review with computational analysis that can handle large volumes without degradation in analysis quality.
2Reliability
If more resources are allocated to analyze innovations, then analysis quality can be maintained, but resource misallocation occurs due to inability to prioritize effectively
Solution Approach 1:
The patent transforms the analysis process by changing key parameters through machine learning models that automatically extract and evaluate multiple innovation features (feasibility, complexity, resource requirements, expected impact). These parameter-based assessments enable systematic prioritization and allocation of analysis resources to innovations with highest potential impact.
Solution Approach 2:
The system implements feedback mechanisms where machine learning models continuously learn from historical innovation data and outcomes. This feedback loop improves the accuracy of impact predictions and resource allocation recommendations over time, enabling more efficient resource distribution while maintaining analysis quality.
3Measurement precision
If human representatives manually evaluate each submission, then detailed judgments can be made, but the time required for evaluation increases significantly
Solution Approach 1:
The patent segments the evaluation process into multiple stages: automated machine learning-based preliminary analysis for all submissions, followed by human representative review only for high-priority innovations that pass initial filtering. This segmentation reduces the time burden on human evaluators while maintaining comprehensive assessment through the automated phase.
Solution Approach 2:
The system performs preliminary analysis of all innovation submissions using machine learning models before human representatives conduct detailed evaluations. This preliminary action includes automated feature extraction, classification, and impact scoring, which prepares and prioritizes submissions for human review, significantly reducing the time required for human evaluation.
Data Source
AI summary
In some implementations, a system may receive a submission associated with an innovation associated with an entity. The system may analyze, using a first machine learning model, the submission to identify a classification associated with the innovation. The system may analyze, using a second machine learning model in association with the classification, content of the submission to identify a characteristic of the innovation. The system may determine, using a third machine learning model and based on the characteristic, a ranking of the innovation relative to individual innovations in the subset of innovations. The system may determine, using a fourth machine learning model, an impact score associated with the innovation. The system may perform, based on the impact score satisfying a threshold, an action associated with a project involving the innovation.