Operational Difficulty Prediction Using Hierarchical GRU Networks
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Solution Overview
Problem
Current methods for determining operational difficulty of tasks described in text documents are inefficient and inaccurate, often relying on linguistic features like bag-of-words analysis and language models, which disregard valuable information and are not suitable for real-world applications.
Innovation Solution
A method using a deep-learning neural network that generates word vectors and statistical information vectors from domain-specific and general-language text collections, processed through gated recurrent units (GRUs) and an ordinal regression unit to quantify operational difficulty, incorporating hierarchical attention networks for accurate difficulty prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If bag-of-words analysis and language models are used to determine operational difficulty, then the method is simple to implement, but the accuracy is poor and valuable information is lost
Solution Approach 1:
The patent segments the text processing into multiple hierarchical levels (word level, sentence level, paragraph level, document level) with separate GRU networks for each level. This segmentation allows the system to capture information at different granularities while maintaining computational feasibility, resolving the contradiction between implementation simplicity and measurement accuracy.
Solution Approach 2:
The patent transforms the traditional single-level text analysis into a multi-dimensional hierarchical structure by processing text at word, sentence, paragraph, and document levels simultaneously. This dimensional expansion enables comprehensive information capture without overwhelming computational complexity, improving accuracy while remaining implementable.
2Ease of operation
If traditional document classification methods are used, then the process is straightforward, but operational difficulty ranking information is lost due to disregard of class ordering
Solution Approach 1:
The patent changes the output parameter from discrete class labels to continuous ordinal regression values that preserve ranking information. By using ordinal regression instead of standard classification, the system maintains the ordering relationship among difficulty levels while simplifying the processing pipeline, thus reducing information loss without complicating the operation.
3Ease of manufacture
If existing automated methods for determining operational difficulty are used, then the implementation is relatively simple, but the results are inaccurate for real-world applications
Solution Approach 1:
The patent combines multiple processing components (word vectors, statistical information vectors, GRU networks at multiple levels, and ordinal regression) into a composite hierarchical system. This composite approach integrates diverse information sources and processing techniques to achieve high accuracy in real-world applications while maintaining a unified and implementable framework.
4Measurement precision
If deep-learning neural networks with multiple vectors and GRUs are used, then the accuracy of operational difficulty determination is improved, but the device complexity increases
Solution Approach 1:
The complex neural network is segmented into modular components: word vector generation, statistical information vector generation, four hierarchical GRU networks (word, sentence, paragraph, document levels), and ordinal regression. This segmentation makes the complex system more manageable and easier to implement while maintaining high accuracy through comprehensive text analysis at multiple levels.
Data Source
AI summary
A method of determining an operational difficulty of a process described in a document including text includes generating text data corresponding to the text of the document, and generating a word vector output by processing the text data through a plurality of word vectors, the plurality of word vectors based on a domain-specific text collection and a general-language text collection. The method further includes generating a statistical information output by processing the text data through a plurality of statistical information vectors, the plurality of statistical information vectors based on the domain-specific text collection and the general-language text collection, and generating a gated recurring unit (GRU) output by processing (i) the word vector output, (ii) the statistical information output, (iii) word-level data based on the text data, (iv) sentence-level data based on the text data, and (v) paragraph-level data based on the text data through at least one GRU.