Similarity Map Analysis for Document Identification Accuracy

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Solution Overview

Problem

Conventional identification methodologies for compliance personnel in heavily regulated industries are inefficient and prone to errors due to the manual interpretation of natural language documents, which is difficult to scale and results in documentation inconsistencies.

Innovation Solution

An automated method using natural language processing, dimensionality reduction, and similarity mapping to contextualize and analyze large volumes of structured and unstructured data, transforming raw data into structured data sets and displaying similarity plots to identify relevant information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual interpretation methodologies are used to identify relevant documents, then compliance personnel can analyze natural language documents, but the process is inefficient, difficult to scale, and prone to identification errors and documentation inconsistencies

Engineering Contradiction:

Improveidentification accuracyVSAvoidprocessing efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces the mechanical manual interpretation process with an automated computational system. The system uses natural language processing, dimensionality reduction, and similarity mapping algorithms to automatically analyze documents, eliminating human manual interpretation while maintaining or improving identification accuracy through consistent automated processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing the automated processing system to independently perform document analysis without requiring compliance personnel to manually interpret each document. The system serves itself by automatically retrieving, processing, and analyzing documents through programmed algorithms, freeing personnel from repetitive manual tasks.

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If manual interpretation processes are used by compliance personnel, then documents can be analyzed, but the process is difficult to scale and results in documentation inconsistencies

Engineering Contradiction:

ImprovescalabilityVSAvoiddocumentation consistency

Core Design Contradiction:

Adaptability or versatilityVSStability of the object's composition

Solution Approach 1:

The patent creates a universal automated system that can handle multiple document types and analysis tasks through a single platform. The system retrieves documents from various sources, performs dimensionality reduction, generates similarity mappings, and produces consistent results across different document sets, enabling scalable deployment without compromising documentation consistency.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system maintains documentation consistency by standardizing processing parameters across all document analyses. Through automated dimensionality reduction and similarity mapping with consistent algorithmic parameters, the system ensures that the same processing rules are applied uniformly to all documents, eliminating variability introduced by manual interpretation.

Inventive Principle:

Principle #35Parameter changes

3Extent of automation

If conventional identification methodologies are used, then compliance personnel can identify document corpus, but the process requires advanced training and proficiency

Engineering Contradiction:

Improveautomation levelVSAvoidsystem complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent segments the complex document analysis task into distinct automated components: document retrieval from multiple sources, natural language processing, dimensionality reduction, similarity mapping, and result visualization. This segmentation allows the system to handle complexity through modular automated processes rather than requiring human expertise in each aspect.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary automated processing layer between the raw documents and the compliance personnel. This intermediary system performs the complex analysis tasks through programmed algorithms, acting as a mediator that translates unstructured documents into structured similarity mappings without requiring personnel to directly engage with the complex processing details.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11720587B2Method and system for using target documents camouflaged as traps with similarity maps to detect patterns

Publication Date: 2023.08.08 JPMORGAN CHASE BANK NA

AI summary

A method for providing contextual analytics of target information by using similarity mapping is disclosed. The method includes retrieving, via a communication interface, raw data from several sources based on a predetermined characteristic of the raw data, the raw data including natural language data; receiving, via a graphical user interface, a target document; converting, by using a natural language processing technique, the raw data into structured data based on a predetermined parameter; refining the target document to generate a target data set; generating a structured data set from the structured data by using a dimensionality reduction technique; and displaying, via the graphical user interface, a graphical element, the graphical element including a similarity plot of the structured data set and the target data set.