Neural Network Business Name Categorization for Energy Management
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Solution Overview
Problem
Current energy management systems face challenges in efficiently categorizing businesses for effective energy usage management, especially when business types are unknown, requiring extensive computing resources and manual validation.
Innovation Solution
An energy management system utilizing a neural network, predictive model, and dictionary reducer to calculate weights for single-word and bigram terms from business names, predicting accurate business categories with high confidence without requiring extensive computing resources or analyst intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional business name categorization methods are used, then manual validation and analyst intervention are required, but this increases device complexity and loss of time
Solution Approach 1:
The patent replaces manual analyst intervention and mechanical categorization processes with an automated neural network system. The neural network iteratively calculates weights for single-word and bigram terms, automatically predicting business categories without human validation, thereby resolving the contradiction between categorization accuracy and system complexity.
Solution Approach 2:
The patent introduces a dictionary reducer as an intermediary component that processes business names by calculating term weights and generating bigrams. This intermediary automatically bridges the gap between raw business names and categorized results, eliminating the need for manual analyst intervention while maintaining high categorization accuracy.
2Measurement precision
If extensive computing resources are allocated for business name categorization, then categorization accuracy improves, but energy consumption increases
Solution Approach 1:
The patent applies partial action by using a dictionary reducer that selectively processes only the most relevant terms (single-words and bigrams) from business names rather than analyzing entire names. This iterative weight calculation approach achieves high categorization accuracy while consuming significantly less computational energy compared to exhaustive analysis methods.
Solution Approach 2:
The patent segments business names into individual words and bigrams, calculating weights for each segment independently. This segmentation allows the neural network to focus computational resources on the most discriminative terms rather than processing the entire business name uniformly, reducing overall energy consumption while maintaining prediction accuracy.
3Reliability
If manual validation processes are implemented, then categorization reliability improves, but productivity decreases
Solution Approach 1:
The patent implements self-service through an automated neural network system that performs categorization without requiring manual validation. The system iteratively calculates term weights and generates category predictions autonomously, achieving both high reliability through iterative refinement and high productivity by eliminating human intervention bottlenecks.
Solution Approach 2:
The patent incorporates feedback mechanisms where the neural network iteratively refines its weight calculations based on prediction results. This iterative feedback loop ensures high categorization reliability by continuously improving predictions while maintaining high productivity through automated processing without manual validation steps.
Data Source
AI summary
An energy management system includes a neural network, a predictive model, and a dictionary reducer. The network iteratively calculates weights, resulting in a final set, for each of single-word terms and bigram terms of training data business names, each of the weights indicative of a likelihood of correlating a business category. The predictive employs sets of the weights to predict a first corresponding one of the plurality of business categories for each of the training data business names until employment of the final set accurately predicts a correct business category for the each of the training data business names, and subsequently employs the final set of the weights to predict a second corresponding one of the plurality of business categories for each of a plurality of operational business names. The dictionary reducer eliminates unessential terms taken to determine the plurality of single-word terms and bigram terms.