Automated Q&A Response Clustering for Permissions-Aware Search
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Solution Overview
Problem
Existing search engines struggle to provide relevant search results while respecting access control lists, leading to unauthorized access to sensitive documents and inefficient information retrieval within enterprise environments.
Innovation Solution
A permissions-aware search and knowledge management system that utilizes machine learning and natural language processing to identify user access rights, rank search results based on user interactions, and generate automated responses to user queries, incorporating user suggestions and document verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a search engine displays all relevant search results, then information retrieval completeness is improved, but unauthorized access to sensitive documents occurs
Solution Approach 1:
The patent applies local quality by making search result visibility user-specific. Each user sees a customized subset of search results based on their individual access rights and permissions. The search engine evaluates access control lists (ACLs) for each document and filters results dynamically per user, ensuring that sensitive documents are only displayed to authorized users while maintaining complete information retrieval for authorized users.
2Reliability
If access control lists are enforced for each search result, then security is improved, but search result relevance and user experience deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-evaluating and caching access control information for documents before generating search results. The system determines user permissions and document accessibility in advance, storing this information in the search index. When a search is executed, the pre-computed access control data is quickly retrieved and applied, avoiding real-time permission checks that would slow down result generation and maintain both security and relevance.
3Productivity
If automated responses are generated using machine learning, then response efficiency is improved, but accuracy of answers deteriorates
Solution Approach 1:
The patent applies feedback by implementing a hybrid response generation system where machine learning models generate automated responses that are then verified and refined through feedback mechanisms. User interactions, corrections, and validations are fed back into the system to continuously improve the accuracy of automated responses. The system learns from user feedback to adjust and enhance the precision of its automated answer generation while maintaining high response efficiency.
Data Source
AI summary
Methods and apparatuses for providing automatic response to user comments and questions are described. Method include identifying a plurality of question and answer sets in a communications channel, determining a first group of questions from the plurality of question and answer sets that are semantically related, determining a second group of answers from the plurality of question and answer sets corresponding to the first group of questions, the second group of answers being semantically related, identifying at least one question from the first group of questions that has a confidence value exceeding a threshold based on a number of semantically related questions and answers in the first group of questions and the second group of answers, and automatically posting the corresponding answer to the at least one question from the first group of questions to the communications channel.