Neural Network Contact Extraction from Physical Media
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Solution Overview
Problem
Manually entering information from physical mediums and updating tasks in database systems is time-consuming and tedious, especially for busy professionals like sales representatives and marketing personnel, who need to manage multiple tasks and contacts efficiently.
Innovation Solution
A cloud-based system that uses neural network models, such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks, to capture and extract contact information from images of physical mediums and update task details from voice instructions, allowing users to enter and update data without manual keying-in, using mobile devices and cloud servers for image recognition and natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual keying-in is used to enter information from physical mediums, then data entry accuracy can be maintained through user review, but time consumption increases significantly
Solution Approach 1:
The patent uses optical copying (camera imaging) to capture physical mediums like business cards and flyers, converting them into digital images that can be processed automatically. This replaces manual transcription while maintaining accuracy through subsequent OCR and machine learning validation steps.
Solution Approach 2:
The patent replaces the mechanical process of manual keying-in with an automated system combining OCR technology and machine learning models. The neural networks automatically extract and categorize information from images, substituting human manual input with intelligent automated processing.
2Productivity
If automated information extraction is implemented using machine learning, then time consumption is reduced, but system complexity increases
Solution Approach 1:
The patent segments the information extraction process into distinct functional modules: image capture, OCR text recognition, machine learning-based categorization, and database integration. This modular segmentation manages system complexity by organizing the automated extraction process into manageable, independent components.
Solution Approach 2:
The patent introduces an intermediary processing layer between image capture and database storage, consisting of neural network models that bridge the gap between raw image data and structured information. This intermediary layer handles the complexity of automated extraction while presenting a simple interface to users.
3Ease of operation
If multiple tasks are managed manually, then task tracking can be done with simple tools, but productivity decreases due to tedious updates
Solution Approach 1:
The patent creates a universal task management system that handles multiple task types and stages through a single integrated platform. The system can capture information from various physical mediums, process different types of data, and update multiple task fields simultaneously, providing multi-functionality that improves productivity while maintaining ease of use through unified access.
Data Source
AI summary
Described herein are systems and methods for facilitating the information entry and task updates to a task database in a cloud server. The task database is in synchronization with a customer relationship management (CRM) system. The systems and methods described herein enable users to update the task database and enter information into the task database in a timely manner such that the task database can stay updated. The updated database can be used to construct a suggested task set at the beginning of a period of time to meet a preset target sales value for the end of the period of time. In one embodiment, a system includes a mobile application to capture contact information from a physical medium as an image, and to send the image to a cloud server, where a trained neural network model is to extract contact details and send the contact details back to the mobile application for editing and confirmation by a user. The confirmed contact details can then be persisted into the task database as a new task, part of a new task, or part of an existing task.