Buyer Agent User Interface with Rules-Based Intent Capture
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Solution Overview
Problem
Current engagement platforms rely heavily on behavior tracking or premature identity capture, leading to a lack of actionable data, privacy risks, and inaccurate AI training, failing to reflect user intent and personalize experiences effectively.
Innovation Solution
A rules-based architecture that captures structured engagement data from anonymous users using declared intent, enabling privacy-respecting personalization and AI training, and providing a unified, privacy-centric data stream for both human and AI systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If behavior tracking is used to personalize user experiences, then personalization capability is improved, but privacy risks and data reliability deteriorate
Solution Approach 1:
The patent introduces an intermediary data collection mechanism that captures user preferences and intent through explicit input forms and interactions, rather than relying on behavioral tracking. This intermediary approach filters out unreliable inferred data while collecting only the necessary structured data needed for personalization, thereby improving data reliability while maintaining personalization capability.
Solution Approach 2:
The patent replaces the mechanical behavioral tracking system with a rules-based architecture that processes structured data from explicit user inputs. This substitution eliminates the need for covert behavioral analysis and replaces it with transparent, rules-based data collection that improves data quality and reduces privacy concerns.
2Extent of automation
If behavior tracking is used to train AI models, then AI training capability is improved, but accuracy of training data deteriorates
Solution Approach 1:
The patent implements preliminary action by collecting and structuring user preference data before AI training occurs. The system captures explicit user inputs, interaction patterns, and preference declarations in advance, organizing them into structured formats that are then used for training. This preliminary data preparation ensures high accuracy in training data quality while enabling robust AI model training.
3Productivity
If anonymous user data is collected without structured processing, then user engagement is improved, but data usability and actionable insights deteriorate
Solution Approach 1:
The patent applies segmentation by dividing the data processing pipeline into distinct stages: data collection, data structuring, and data utilization. The system segments anonymous user data into structured formats with specific fields (user preferences, interaction history, intent indicators), making the data actionable while maintaining anonymity. This segmentation preserves user engagement through anonymous interaction while transforming raw data into usable insights.
Solution Approach 2:
The patent changes the parameters of data representation by transforming anonymous user interactions from unstructured formats into structured data with defined schemas. The system modifies data parameters to include organized fields for preferences, interactions, and intent, thereby converting unusable anonymous data into actionable insights while maintaining user anonymity and engagement.
Data Source
AI summary
Systems and methods for creating and operating a buyer agent user interface relating to transactions (e.g., real estate or other transactions) and/or other information from engagement platforms and other applications. Robust system infrastructure, specialized user interfaces, transaction stage visualization, feedback and customization features, and dynamic market insights are used to facilitate transactions, and which can be used by human and/or artificial intelligence agents to enhance the particular application.