Automated Insight Generation from Document Trees
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Solution Overview
Problem
Conventional methods for generating insights from organizational documents are expensive, time-consuming, and subjective, relying on human experts who are limited in processing diverse data and prone to inconsistency.
Innovation Solution
An automated approach that transforms documents into structured tree-like objects, applies rules to extract insight elements, and uses machine-learning models to generate objective insights, enabling scalable and repeatable analysis of organizational data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts are used to generate insights from organizational documents, then the insights can be nuanced and contextually understood, but the process becomes expensive, time-consuming, and subjective
Solution Approach 1:
The patent replaces the mechanical system of human expert analysis with an automated computational system that uses natural language processing, machine learning models, and structured data processing to generate insights from organizational documents, thereby eliminating the time and cost constraints of human analysis while maintaining objective consistency
Solution Approach 2:
The patent creates a digital copy of the expert analysis process through automated systems that replicate insight generation capabilities using algorithms and machine learning models trained on organizational data, allowing multiple documents to be analyzed simultaneously without additional time or cost
2Adaptability or versatility
If human experts analyze documents to derive insights, then contextual understanding is achieved, but consistency and repeatability are compromised due to subjectivity
Solution Approach 1:
The patent transforms the analysis process by changing parameters from subjective human judgment to objective computational metrics, using structured data extraction, standardized parsing rules, and machine learning models that produce consistent, repeatable results while maintaining adaptability to different document types and organizational contexts
3Loss of information
If conventional manual methods are used for insight generation, then deep analysis is possible, but scalability is limited
Solution Approach 1:
The patent segments the document analysis process into distinct computational stages including text extraction, structured data parsing, insight element identification, and model-based insight generation, allowing each segment to be optimized independently and enabling parallel processing of multiple documents to achieve scalability without sacrificing analysis depth
Solution Approach 2:
The patent creates a universal automated analysis system that can process multiple types of organizational documents (financial reports, operational reports, strategic plans) using the same core methodology and machine learning models, enabling scalable analysis across diverse data types and organizational functions
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for generating an insight, comprising: receiving a request to generate an insight for a document from a user associated with the document; receiving the document; generating a tree based on the document; parsing the tree based on a set of rules to generate a set of results associated with the tree; mapping the set of results to a subset of insight elements of a set of insight elements associated with a set of trees including the tree; providing, to a first machine-learning model, the subset of insight elements; receiving, from the first machine-learning model, an insight for the document based on the subset of insight elements; and returning the insight for the document to the user.