AI Receipt Context Prompting for Accurate Expense Field Extraction
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Solution Overview
Problem
Existing document integration systems require significant human expertise and effort for bulk document intake, especially when dealing with diverse formats, languages, and electronic channels, and lack the ability to accurately integrate documents into target database structures without manual intervention.
Innovation Solution
Utilizing generative artificial intelligence (AI) to automate the document intake process by transforming documents into ERP-compatible schema, recognizing data elements, and continuously refining processes through a document fingerprint-driven adaptive learning system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI is used to automate document intake, then productivity and operational efficiency are improved, but device complexity increases
Solution Approach 1:
The patent introduces a document fingerprint-driven adaptive learning system as an intermediary between the generative AI model and the target database structure. This intermediary layer handles the complexity of mapping diverse document formats to standardized schemas, allowing the AI to focus on content extraction while the fingerprint system manages structural transformation and integration.
Solution Approach 2:
The system segments the document processing workflow into distinct phases: document intake, fingerprint generation, schema transformation, and database integration. By dividing the complex automation process into manageable segments, each handled by specialized components, the system achieves high productivity while keeping individual complexity levels manageable.
2Productivity
If manual intervention is eliminated, then productivity improves, but measurement precision may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where document fingerprints and extraction results are continuously monitored and used to refine the adaptive learning system. This feedback loop allows the system to learn from previous extractions and improve accuracy over time, maintaining high precision even as automation increases processing speed.
Solution Approach 2:
The system performs preliminary actions by generating document fingerprints and pre-processing documents before final extraction. This preliminary analysis prepares the data structure in advance, enabling faster and more accurate extraction during the main processing phase, thus maintaining precision while improving throughput.
3Adaptability or versatility
If documents are integrated into ERP-compatible schema, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent creates a universal schema transformation layer that handles multiple document formats and electronic channels by converting them all into a standardized ERP-compatible structure. This universal intermediary layer absorbs the complexity of format diversity, allowing the system to maintain high adaptability while keeping the core integration logic simple and reusable.
Data Source
AI summary
Systems, methods, and computer-readable media are provided for detecting user-specific context for a receipt and embedding the user-specific context in a prompt to provide a hint that helps a large language model detect value(s) for field(s) from the receipt. The receipt may then be integrated with an expense management system.