Cognitive Assignment Engine Graphical Ontology Analysis
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Solution Overview
Problem
Current cognitive assignment engines (CAEs) are unable to process messages lacking sufficient textual content, such as those with generic subject lines or attachments like screenshots, as they rely solely on textual analysis and cannot determine the appropriate response team or handle messages with no text.
Innovation Solution
A self-learning cognitive assignment engine system that infers semantic meaning from graphical content by identifying and classifying ontological structures within images, generating a message identifier to retrieve associated templates and assign the message to the appropriate responder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the CAE system relies solely on textual analysis to infer semantic meaning, then the inference accuracy is improved when text is available, but the system cannot process messages with insufficient or no textual content
Solution Approach 1:
The system is enhanced to perform multiple types of analysis: textual content analysis and graphical content analysis. The graphical analysis module processes images, screenshots, and visual elements to extract ontological structures, allowing the CAE to handle messages regardless of whether they contain text, graphics, or both types of content
Solution Approach 2:
Ontological structures serve as an intermediary representation layer between the input message (textual or graphical) and the semantic meaning inference. By converting graphical content into structured ontological representations, the system bridges the gap between different content types and the inference engine
2Adaptability or versatility
If the system expands to analyze graphical content in addition to text, then the adaptability to process various message types is improved, but the system complexity increases
Solution Approach 1:
The CAE system is divided into distinct modular components: a textual analysis module and a graphical analysis module. Each module independently processes its designated content type and contributes to the overall semantic inference, making the system easier to manage, maintain, and extend while handling diverse message formats
3Productivity
If the system uses graphical content analysis to process messages with no text, then the productivity of message triage is improved, but the difficulty of detecting and measuring semantic meaning increases
Solution Approach 1:
The system pre-defines ontological structures and their associated semantic meanings before processing incoming messages. By having a prepared framework of expected graphical patterns and their interpretations, the system can quickly match incoming graphical content to known ontological structures, reducing the complexity of real-time semantic extraction
Data Source
AI summary
A cognitive assignment engine (CAE) system attempts to infer semantic meaning from textual content of an incoming message in order to use the inferred meaning to assign the message to an appropriate responder. If the message contains insufficient textual content, the system identifies ontological structures comprised by the message's graphical content and classifies each structure as a function of the structure's location within the graphical content or of an intrinsic characteristic of the structure. The system then generates a message identifier by performing a computation on these classifications and uses the identifier to retrieve a previously stored graphical template that comprises ontological structures similar to those of the incoming message. The system associates the incoming message with a semantic meaning previously associated with the template, enabling the system to classify the message and to assign the message to the correct responder.