Client Buyer User Interface for Anonymous Intent Personalization

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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.

Innovation Solution

A rules-based architecture that generates structured engagement data from anonymous users using declared intent, enabling privacy-respecting personalization and AI training without identity capture, allowing seamless integration with human and AI agents.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If behavior tracking is used to personalize user experiences, then personalization capability is improved, but privacy risks increase and data accuracy deteriorates

Engineering Contradiction:

Improvepersonalization capabilityVSAvoidprivacy risks

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary actions by collecting explicit user preferences and declared intents before any behavioral tracking occurs. Users proactively provide their preferences, goals, and requirements through structured forms and interactions, establishing a foundation for personalization that does not rely on invasive tracking methods.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary layer between user behavior and data collection mechanisms. Instead of directly tracking user actions, the system uses declared intent data as an intermediary that captures user preferences explicitly. This intermediary approach allows personalization while maintaining privacy, as the system works with user-provided information rather than inferred behavioral data.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of information

If identity capture is performed early to enable personalization, then data completeness is improved, but user trust deteriorates and privacy risks increase

Engineering Contradiction:

Improvedata completenessVSAvoiduser trust

Core Design Contradiction:

Loss of informationVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary data collection by capturing user preferences, goals, and requirements before requesting identity information. Users can engage with the platform and provide meaningful data about their needs without immediately disclosing their identity, allowing the system to build a complete preference profile in advance.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent segments the data collection process into distinct phases: first collecting anonymous or pseudonymous preference data, then optionally linking this data to user identity later. This segmentation allows the system to gather comprehensive preference information without requiring immediate identity capture, thereby maintaining user trust while ensuring data completeness.

Inventive Principle:

Principle #1Segmentation

3Productivity

If AI systems are trained on inferred or aggregated data, then model development speed is improved, but training accuracy deteriorates and relevance to real-time intent is lost

Engineering Contradiction:

Improvemodel development speedVSAvoidtraining accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system enables self-service by having users directly provide their preferences, goals, and requirements through structured inputs. This self-reported data serves as high-quality training material for AI systems, eliminating the need for complex inference algorithms to guess user intent. Users essentially train the system themselves by providing explicit feedback about their preferences and needs.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical process of behavioral tracking and inference with a more direct approach where users explicitly state their preferences. Instead of using complex algorithms to infer intent from user actions, the system directly captures declared intents, providing cleaner and more accurate training data for AI models.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Object-affected harmful factors

If structured engagement data is generated from anonymous users, then data privacy is improved, but data actionability must be enhanced to overcome the lack of user identification

Engineering Contradiction:

Improvedata privacyVSAvoiddata actionability

Core Design Contradiction:

Object-affected harmful factorsVSLoss of information

Solution Approach 1:

The system changes the parameters of data collection by focusing on what matters most for actionability: user preferences, goals, requirements, and declared intents. Instead of collecting traditional personally identifiable information, the system captures structured preference data that is immediately actionable for personalization and recommendation purposes, making the data highly valuable despite anonymous collection.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250335973A1Systems And Methods For Creating And Operating A Client Buyer User Interface For Engagement Platforms And Other Applications

Publication Date: 2025.10.30 PANKEY ERIC L

AI summary

Systems and methods are provided for creating and operating a client buyer 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 integration, comprehensive user interfaces, visualization of transaction progress, interactive responses and input features, and market insights are provided to facilitate transactions, and which can be used by human and/or artificial intelligence agents to enhance the particular application.