Support Content Generation via AI Feature Extraction
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Solution Overview
Problem
Help desk software often requires users to share sensitive information or incomplete screenshots to describe IT and software issues, which can compromise privacy and hinder effective issue resolution.
Innovation Solution
A method and system for generating support content using a trained classification model to identify software features from user descriptions, modifying base images to include relevant information, and transmitting tailored support content without sensitive data, leveraging machine learning and AI techniques for efficient and accurate issue representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users share screenshots to describe IT issues, then issue description completeness is improved, but user privacy is compromised due to sensitive information exposure
Solution Approach 1:
The system extracts only the necessary diagnostic information from user descriptions and screenshots, separating useful technical details from sensitive personal data. The classification model identifies and extracts feature-specific information needed for issue resolution while deliberately excluding sensitive fields such as personal identifiers, account information, and confidential data.
Solution Approach 2:
A trained classification model acts as an intermediary between the user's raw screenshot and the support personnel. This intermediary automatically processes the screenshot, identifies relevant software features and issue characteristics, and generates a sanitized support content that conveys the technical problem without exposing sensitive information.
2Object-affected harmful factors
If generic support content is used, then privacy is preserved, but issue resolution accuracy decreases
Solution Approach 1:
The system applies local quality by tailoring the support content to the specific software feature and issue type identified through the classification model. Instead of using uniform generic content, the system generates customized support information that addresses the particular technical problem while maintaining privacy protection, thereby improving resolution accuracy.
Solution Approach 2:
The classification model changes the parameters of the support content based on the analyzed issue characteristics. It dynamically adjusts the support content to match the identified software feature, error type, and contextual information from the user description, ensuring the content is both specific and privacy-protecting.
3Measurement precision
If manual analysis of user descriptions is performed, then accuracy is improved, but processing time increases
Solution Approach 1:
The system implements self-service by automatically analyzing user descriptions and screenshots through the trained classification model without requiring manual intervention. The model independently identifies software features, extracts relevant information, and generates appropriate support content, eliminating the need for time-consuming manual analysis while maintaining high accuracy.
Solution Approach 2:
The classification model is pre-trained on extensive datasets of IT issues and software features, enabling it to perform rapid accurate classification during actual use. This preliminary training action allows the system to quickly process new user descriptions without requiring time-consuming manual analysis, as the model has already learned the patterns and characteristics of various issues.
Data Source
AI summary
Aspects of the present disclosure relate to support content generation. An issue description is received from a user. A software feature associated with the issue description is identified using a trained classification model. A base image associated with the software feature is obtained. The base image is modified to add information indicated in the issue description, wherein the modified base image is generated support content. The generated support content is transmitted.