AI Content Correction Using Persona and Device Analysis
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Solution Overview
Problem
Current information handling systems, particularly in customer support scenarios, face challenges in accurately interpreting and responding to textual queries due to the lack of consideration for language-influencing factors such as user persona, social trends, and device properties, leading to inefficient communication and resolution of issues.
Innovation Solution
An AI-based machine learning module analyzes these language-influencing factors to modify textual content in real-time, ensuring that the text is more accurately and meaningfully conveyed, thereby facilitating better comprehension and resolution of issues between customers and support agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional textual communication is used without considering language-influencing factors, then the communication process is simple and fast, but the accuracy and comprehension of the textual content deteriorate
Solution Approach 1:
The system performs preliminary analysis of language-influencing factors (persona, social trends, device properties) before processing the textual content. This allows the system to pre-adjust the interpretation context, improving accuracy without adding complexity to the core communication flow.
Solution Approach 2:
The patent introduces an intermediary layer that analyzes language-influencing factors between the sender and receiver. This mediator processes the textual content through the lens of persona, social trends, and device properties, resolving the contradiction by adding analytical depth without complicating the actual communication exchange.
2Measurement precision
If AI-based analysis of multiple language-influencing factors is performed, then the accuracy and meaningfulness of textual content is improved, but the processing time and computational resources increase
Solution Approach 1:
The system segments the analysis of language-influencing factors into distinct components (persona analysis, social trends detection, device properties evaluation). This segmentation allows parallel processing of different factors, reducing overall processing time while maintaining comprehensive analysis accuracy.
Solution Approach 2:
The system applies partial analysis based on the specific context - not all language-influencing factors are analyzed with equal depth for every message. The system adjusts the level of analysis according to the situation, performing comprehensive analysis only when necessary, thus balancing accuracy with processing efficiency.
3Ease of operation
If textual content is corrected based on persona, social trends, and device properties, then the readability and comprehension are enhanced, but the complexity of the correction system increases
Solution Approach 1:
The correction system is designed as a universal multi-functional module that handles multiple types of corrections simultaneously - persona-based adjustments, social trends alignment, and device-property optimizations. This universal approach reduces overall system complexity by consolidating multiple correction functions into a single integrated system rather than separate specialized systems.
Data Source
AI summary
A machine learning (ML) module that analyzes multiple language-influencing factors to correct textual content in a more meaningful and efficient manner. In the context of product support, the ML module considers the factors such as a customer's persona that shapes the words a customer chooses while speaking/writing to a customer support agent, current social trends that create new words in the social media/social platforms related to the customer's support issue, and the device used by the customer to input the textual content because different words may be input by the customer when using a smart phone versus a desktop/laptop personal computer with a traditional keyboard. The ML module also may analyze the agent's persona to modify agent's response to the customer because the agent's persona can influence the content of the agent's text. The ML module automatically corrects textual content in real-time before it is sent to the relevant recipient.