Contextual Natural Language Censoring With Exception Filtering
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Solution Overview
Problem
Electronic devices struggle with outputting audio and text responses that include impermissible phrases such as curse words, inappropriate content, or infringing material, without effective mechanisms to censor or manage these violations of content policies.
Innovation Solution
A remote system performs contextual natural language censoring by identifying impermissible phrases and permissible exceptions using configuration data, queries, and filtration processes, and can censor or manage applications based on content policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If electronic devices output audio and text responses with complete content, then information completeness is improved, but content policy violations occur due to impermissible phrases
Solution Approach 1:
The patent introduces an intermediary censoring system that sits between the content generation process and the final output. This intermediary layer analyzes the generated content, identifies impermissible phrases, and applies appropriate censoring actions (replacement, removal, or masking) while preserving the overall structure and meaning of the response. This resolves the contradiction by filtering harmful content without completely blocking information flow.
Solution Approach 2:
The system dynamically changes the state of identified impermissible phrases by applying different censoring parameters: complete removal for severe violations, partial masking for moderate violations, or contextual modification for minor violations. This allows the system to maintain information completeness where appropriate while enforcing content policies where necessary, resolving the contradiction through selective parameter application.
2Reliability
If electronic devices implement content censoring mechanisms, then content policy compliance is improved, but response accuracy deteriorates due to censorship of legitimate content
Solution Approach 1:
The patent applies different censoring qualities to different parts of the content based on local analysis. Each phrase or word is evaluated independently, and only those specific elements that violate content policies are modified. The rest of the content remains unchanged and fully accurate. This localised approach ensures high compliance while minimising impact on overall response accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where censoring decisions are continuously refined based on analysis of the generated content and its context. The censoring module receives feedback about potential false positives and adjusts its sensitivity and rules accordingly, improving both compliance accuracy and response precision over time through iterative optimisation.
3Measurement precision
If electronic devices perform contextual analysis for censoring, then censoring accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the content analysis process into discrete, manageable components: tokenisation of the response, individual phrase evaluation against content policies, context window analysis for ambiguous cases, and hierarchical decision-making layers. This segmentation allows accurate contextual analysis to be performed in modular steps, reducing overall computational complexity while maintaining high censoring accuracy.
Data Source
AI summary
Systems and methods for contextual natural language censoring are disclosed. For example, configuration data indicating details associated with content provided by a client device and/or about the client device may be received and may be utilized to determine impermissible and permissible exceptions for a given client. One or more queries may be generated utilizing the impermissible and permissible exceptions, and when input data is received from the client device and/or in association with the client identifier, the queries may be utilized to evaluate the input data for impermissible and permissible exceptions. The results may be filtered based on user preferences, the input data may be censored, the input data may be prevented from being exposed to a user device, the application associated with the input data may be removed from availability, and/or a maturity setting may be changed for the application, for example.