Unified Data Hub for Financial Document Digitization

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Traditional data storage and processing systems face challenges such as data silos, high computational and storage costs, network bandwidth constraints, and difficulties in maintaining accurate data lineage, which hinder efficient data management and access in financial services industries.

Innovation Solution

The Information Delivery Platform (IDP) integrates a scalable data store, connector grids, and advanced analytics, enabling data aggregation, processing, and analytics while optimizing data quality and accessibility, and providing a centralized repository for structured, semi-structured, and unstructured data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional data storage and processing systems are used to manage financial services data, then data can be stored and processed, but data silos form, computational and storage costs increase, and data accessibility deteriorates

Engineering Contradiction:

Improvedata consistencyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent merges multiple data sources and storage systems into a unified data hub that consolidates structured, semi-structured, and unstructured data. This integration eliminates data silos while maintaining data consistency through centralized management and standardized data models, directly resolving the contradiction between reliability and system complexity.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The data hub is designed as a universal platform that handles multiple data types (structured, semi-structured, unstructured) and supports various analytical workloads simultaneously. This multi-functional architecture improves data accessibility and consistency without proportionally increasing system complexity, as the same infrastructure serves multiple purposes.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Quantity of substance

If traditional data storage systems are used, then data can be retained, but storage costs increase and data accessibility decreases

Engineering Contradiction:

Improvedata volumeVSAvoiddata accessibility

Core Design Contradiction:

Quantity of substanceVSEase of operation

Solution Approach 1:

The patent segments data into different categories (structured, semi-structured, unstructured) and stores them in optimized formats within the unified data hub. This segmentation allows efficient retrieval and processing of specific data types without accessing entire datasets, improving accessibility while managing storage costs effectively.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The data hub acts as an intermediary layer between raw data sources and analytical applications. It provides standardized data access interfaces and preprocessing capabilities, enabling easy data retrieval and processing without requiring direct access to underlying storage systems, thus improving ease of operation while handling large data volumes.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Loss of information

If data is aggregated from multiple sources, then data comprehensiveness improves, but network bandwidth consumption increases

Engineering Contradiction:

Improvedata completenessVSAvoidnetwork bandwidth

Core Design Contradiction:

Loss of informationVSLoss of energy

Solution Approach 1:

The patent implements preliminary data aggregation and preprocessing at the data hub before data is requested by analytical applications. Data is consolidated, validated, and formatted in advance, reducing the need for repeated data retrieval and processing. This preliminary action ensures data completeness while minimizing network bandwidth consumption during actual analytics operations.

Inventive Principle:

Principle #10Preliminary action

4Productivity

If traditional processing systems are used, then existing operations can be maintained, but computational costs increase and query performance decreases

Engineering Contradiction:

Improvequery performanceVSAvoidcomputational cost

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent changes the structural parameters of the data system by implementing a unified data hub with optimized data models and storage formats. This structural change enables more efficient data retrieval and processing operations, improving query performance while reducing computational costs compared to traditional distributed processing systems.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20240386380A1Systems and methods for digitization services

Publication Date: 2024.11.21 BANK OF MONTREAL

AI summary

Embodiments relate to systems and processes for digital document services, having at least a processor and a non-transient data memory storage, the data memory storage containing machine-readable instructions for execution by the processor, the machine-readable instructions configured to, when executed by the processor, provide a document service. The document service can extract data from a plurality of source systems; load and store the data at a data hub implemented by a non-transient data store; receive a request to generate a document package at the data hub, the request indicating a target unit; generate and store the document package using a subset of data from the data at the data hub, the document package having at least one electronic signature request; detect a signature event at the data hub; and transmit the document package to the target unit.