Housing Search Chatbot With Fair Housing Query Filtering
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Solution Overview
Problem
Existing search engines and techniques struggle with understanding natural language requests, providing accurate and specific housing-related information, and adhering to fair housing rules, particularly in responding to free-form queries.
Innovation Solution
A chatbot system utilizing a trained large language model (LLM) with a fair housing-compliant filter to generate responses, incorporating a query filter that rejects non-compliant queries, uses authoritative sources, and maintains context through interaction sessions, ensuring accurate and compliant information generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a chatbot system uses a trained large language model to respond to free-form natural language queries, then the ability to understand and respond to natural language requests is improved, but the difficulty in adhering to fair housing rules increases
Solution Approach 1:
The patent introduces a fair housing filter as an intermediary component between the user's natural language query and the large language model. This filter preprocesses incoming queries to identify and remove references to protected characteristics (race, religion, sex, disability, etc.) before the query reaches the LLM, ensuring that the model cannot generate discriminatory responses while preserving the ability to understand and respond to natural language requests about housing availability and characteristics.
2Quantity of substance
If existing search engines provide broad information access, then the quantity of information available is improved, but the accuracy and specificity of housing-related information decreases
Solution Approach 1:
The patent implements local quality by creating a specialized information retrieval system tailored specifically for housing-related queries. Instead of using a general-purpose search engine, the system incorporates housing-specific knowledge bases, authoritative housing data sources, and context-aware processing that understands housing terminology, preferences, and constraints. This allows the system to provide accurate and specific housing information while maintaining broad accessibility through natural language interfaces.
3Reliability
If a system blocks non-compliant queries to ensure fair housing rules are followed, then fair housing compliance is improved, but the loss of information increases
Solution Approach 1:
The patent extracts and removes only the problematic portions of queries that reference protected characteristics, rather than blocking entire queries. The fair housing filter identifies and extracts references to race, religion, sex, disability, familial status, national origin, and other protected classes, then removes or redacts these portions before processing. This allows the system to maintain compliance with fair housing rules while preserving and processing the remaining useful information in the query, such as housing preferences, location requirements, and budget constraints.
Data Source
AI summary
Techniques are described for performing automated operations related to generating and providing housing-related information, such as to automatically provide housing-related information that is compliant with fair housing rules, such as to automatically respond to free-form natural language requests for housing-related information of various types by using a fair housing-compliant filter to block and/or modify requests that are not compliant with fair housing rules. In at least some situations, the described techniques generates responses using a trained large language model that maintains context over an interaction session with a user involving multiple user queries and corresponding responses, and ensuring accurate response information by restricting the generation of the response information in particular ways that include using the fair housing-compliant filter and by identifying and providing citations to authoritative sources used to generate the response information.