Invoice Data Clustering via Neural Network Category Vectors
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Solution Overview
Problem
Identifying relationships and suggesting additional products for businesses based on existing invoices is challenging due to the large volume of data and complexity in understanding invoice terms and categories.
Innovation Solution
A method and system using a machine learning model with a dense layer, embedding layer, and neural network layer to generate category vectors from invoice strings, allowing for clustering and suggesting products by comparing these vectors to trained clusters, thereby providing accurate recommendations for businesses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If invoice data is stored in large repositories to maintain comprehensive transaction records, then data completeness is improved, but data analysis complexity increases
Solution Approach 1:
The patent segments invoice data into structured fields (invoice string, category vector, product information) and organizes them into manageable components. The machine learning model divides the analysis process into distinct layers (embedding layer, neural network layer, dense layer) that process different aspects of the data separately, making the overall complex task more tractable.
Solution Approach 2:
The patent introduces category vectors as intermediary representations between raw invoice data and final analysis results. These vectors serve as a bridge that transforms unstructured invoice text into a standardized format that can be efficiently clustered and analyzed, reducing the complexity of direct data processing.
2Measurement precision
If machine learning models are used to generate category vectors from invoice strings, then product suggestion accuracy is improved, but computational processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-training the machine learning model on extensive invoice data to learn effective category vector representations. The model is pre-trained to recognize patterns and relationships in invoice data, so that during actual use, it can quickly generate accurate category vectors without requiring complex real-time computations.
Solution Approach 2:
The patent transforms the invoice string into a category vector by changing the parameter representation from raw text to numerical vectors. This parameter transformation allows the system to work with compact numerical representations instead of processing large volumes of text data in real-time, reducing computational overhead while maintaining accuracy.
3Adaptability or versatility
If clusters are trained using category vectors from multiple businesses to provide generalized suggestions, then suggestion versatility is improved, but training data requirements increase
Solution Approach 1:
The patent creates universal category vectors that can represent products across different businesses and industries. The machine learning model is designed to learn generalized patterns from diverse invoice data, enabling the same clustering system to provide relevant suggestions for multiple different businesses without requiring business-specific customization. This multi-functional approach allows one system to serve many different use cases.
Data Source
AI summary
Methods and systems classify and cluster invoice data. An invoice is obtained. A category vector is generated from an invoice string of the invoice with a dense layer of a machine learning model that includes an embedding layer, a neural network layer, and the dense layer. A suggestion is selected with a selection engine and in response to comparing the category vector to a set of clusters. The suggestion is presented.