IM Bot RPA with CNN Text Extraction

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Solution Overview

Problem

Conventional robotic process automation (RPA) lacks integration of advanced AI and conversational user interfaces, and struggles with efficient textual-content extraction from digital images, especially those with unknown formats or complex backgrounds, limiting its effectiveness in delivering automated workflows via instant messaging and mobile Internet.

Innovation Solution

The system incorporates a chatbot application, software RPA manager, and instant-messaging platform connected to public IM platforms, utilizing improved convolutional neural network (CNN) methods for textual-content extraction, including CT-SSD and Watershed U-Net Segmentation for text detection and CTC-CNN for text recognition, enabling efficient robotic workflows and automated processes without requiring external app downloads.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional OCR techniques are used for textual-content extraction, then the process is simple and fast, but it is not effective for images of unknown formats or on complex backgrounds

Engineering Contradiction:

Improvetext extraction accuracyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent transitions from conventional OCR parameter settings to deep learning model parameters (CNN architectures, learning rates, batch sizes) to adapt the extraction system to handle diverse image formats and complex backgrounds, thereby improving reliability without permanently increasing operational complexity

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces the mechanical rule-based OCR system with an intelligent deep learning-based system that can automatically adapt to different image types and backgrounds, solving the limitation of conventional OCR while managing complexity through automated model selection and training

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If custom-made mobile apps are developed for individual enterprises, then the automation functionality is tailored and effective, but the cost is high and external customers are reluctant to download them

Engineering Contradiction:

Improveworkflow customizationVSAvoiddeployment cost

Core Design Contradiction:

Adaptability or versatilityVSEase of manufacture

Solution Approach 1:

The patent creates a universal RPA platform accessible through public IM platforms that can serve multiple enterprises and customers simultaneously, eliminating the need for custom app development for each enterprise while maintaining adaptability through configurable workflows and bot personalities

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent introduces public instant messaging platforms as intermediaries between enterprises and external customers, allowing RPA workflows to be delivered without requiring customers to download dedicated apps, thereby reducing deployment costs while maintaining accessibility and customization

Inventive Principle:

Principle #24Intermediary (Mediator)

3Extent of automation

If conventional RPA with rule-based algorithms is used, then the system is simple to implement, but it lacks intelligence and user-friendliness

Engineering Contradiction:

Improveautomation capabilityVSAvoiduser-friendliness

Core Design Contradiction:

Extent of automationVSEase of operation

Solution Approach 1:

The patent replaces rule-based algorithms with deep learning algorithms that provide intelligent automation capabilities, enabling the system to understand natural language, interpret unstructured data, and adapt to varying requirements, thereby enhancing both automation extent and user-friendliness simultaneously

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent enables the RPA system to autonomously learn and adapt to user needs through continuous training on interaction data, reducing the need for manual programming and configuration while improving user-friendliness through natural conversational interfaces

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11539643B2Systems and methods of instant-messaging bot for robotic process automation and robotic textual-content extraction from images

Publication Date: 2022.12.27 D8AI INC

AI summary

Systems and methods of instant-messaging bot for robotic process automation (RPA) and robotic textual-content extraction from digital images include a chatbot application, a software RPA manager, and an instant-messaging (IM) platform, all built for an enterprise. The enterprise IM platform is connected to one or more public IM platforms over the Internet. The RPA manager contains multiple modules of enterprise workflows and receives instructions from the enterprise chatbot for executing individual workflows. The system allows enterprise users connected to the enterprise IM platform, and external users connected to the public IM platforms, to use instant messaging to initiate enterprise workflows that are automated with the help of the enterprise chatbot and delivered via instant messaging. Furthermore, textual-content extraction from digital images is incorporated in the RPA manager as an enterprise workflow, and provides improved convolutional neural network (CNN) methods for textual-content extraction.