Semantic Analysis Engine for Document Recognition
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Solution Overview
Problem
Current computer systems lack the capability to semantically analyze documents and voice data, requiring significant human input for review and analysis, particularly in industries like finance, insurance, and law, where automated analysis of large datasets and call logs is difficult, leading to inefficiencies and uncertainty in decision-making.
Innovation Solution
An analytics computing device equipped with a processor and database that uses semantic analysis and machine learning to extract content data from documents and voice recordings, generating predictions and recommendations for case outcomes, including a predicted case value amount, by storing and processing documents with OCR and natural language processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If computers use traditional OCR and word search tools to analyze documents, then text extraction is possible, but semantic understanding capability is lacking
Solution Approach 1:
The patent replaces traditional mechanical text extraction methods (OCR, keyword search) with machine learning-based semantic analysis systems. The ML models automatically understand document content, extract meaningful information, and generate insights without requiring manual text processing rules, thereby achieving semantic understanding while managing system complexity through automated learning.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw document text and meaningful information extraction. These ML models act as mediators that bridge the gap between unstructured document content and structured semantic understanding, enabling computers to comprehend document meaning rather than just extract text.
2Productivity
If manual review and analysis of documents is performed, then accurate understanding is achieved, but productivity is reduced
Solution Approach 1:
The patent implements self-service document analysis through machine learning systems that automatically perform semantic understanding, information extraction, and analysis without human intervention. The ML models independently process documents, extract relevant information, and generate insights, enabling high-volume document analysis while eliminating manual review time and significantly improving productivity.
3Loss of information
If call recording is used to retain conversations, then contact information is preserved, but automated analysis capability is lacking
Solution Approach 1:
The patent replaces manual analysis of recorded conversations with machine learning-based automated analysis systems. The ML models automatically transcribe, understand, and extract meaningful information from voice recordings and call logs, enabling automated analysis while preserving all conversational information without requiring human reviewers.
4Extent of automation
If documents are manually labeled and organized, then accurate categorization is achieved, but device complexity increases
Solution Approach 1:
The patent implements self-service document organization where machine learning models automatically categorize, label, and organize documents based on their semantic understanding of content. The system independently performs classification and organization tasks without manual intervention, achieving high-level automation while the ML models handle the complexity of understanding document contexts and relationships.
Data Source
AI summary
An analytics computing device is provided. The analytics computing device may include a processor in communication with a memory. The processor may (1) store, in the memory, a plurality of documents in association with a case identifier; (2) electronically extract content data from the plurality of documents using a semantic analysis engine; (3) generate a case record in the memory including the extracted content data associated with the case identifier, the case record having a predefined data format; (4) execute a machine learning model configured to output a predicted value amount by inputting at least a portion of the extracted content data included in the case record into the machine learning model, the machine learning model trained using a plurality of historical case records and a plurality of historical value amounts; and/or (5) cause the predicted value amount outputted by the machine learning model to be displayed.