Session Emotional Vectors for Privacy-Safe Real-Time Personalization
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Solution Overview
Problem
Conventional personalization systems rely on identity-based architectures, historical data, and static decision trees, failing to adapt fluidly to moment-by-moment user behavior and violating evolving privacy laws, while lacking emotional intelligence and adaptability across diverse interaction surfaces.
Innovation Solution
A real-time personalization system models each session as a deformable emotional object (Vectra) using emotional trait vectors, dynamically adapting goal strategies and tone without identity or stored history, employing goal mutation logic and contextual warping for continuous, privacy-compliant experiences across various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If identity-based personalization systems are used, then personalization accuracy is improved, but privacy compliance deteriorates
Solution Approach 1:
The patent extracts and removes identity-based data (cookies, login information, persistent tracking) from the personalization system, replacing it with ephemeral session-based behavioral signals. This extraction allows the system to maintain personalization accuracy through real-time behavior analysis while eliminating privacy compliance issues associated with persistent identity tracking.
Solution Approach 2:
The system changes the fundamental parameter of data persistence from permanent (identity-based) to transient (session-based). By using ephemeral trait vectors that reset with each session, the system maintains measurement precision for personalization while automatically satisfying privacy compliance requirements that prohibit persistent tracking.
2Device complexity
If static decision trees are used, then system complexity is reduced, but adaptability to real-time behavior deteriorates
Solution Approach 1:
The patent transforms the static decision tree into a dynamic system using deformable trait vectors that continuously adapt to real-time user behavior. The emotional trait vectors (etv) are dynamically updated based on session behavior, allowing the system to maintain low complexity while achieving high real-time adaptability through fluid, behavior-driven personalization.
Solution Approach 2:
The system performs preliminary action by pre-defining a structured framework of emotional traits and decision nodes, but leaves the specific trait values and path selections dynamic. This preliminary structuring maintains system simplicity while enabling real-time adaptability through behavior-driven trait modulation.
3Loss of information
If historical profiling is used, then personalization depth is improved, but real-time responsiveness deteriorates
Solution Approach 1:
Instead of using heavy historical profiles, the system creates lightweight copies of behavioral patterns through ephemeral trait vectors that capture essential user characteristics for the current session. This copying approach maintains personalization depth by inferring traits from current behavior while achieving real-time responsiveness by avoiding retrieval and processing of historical data.
Solution Approach 2:
The patent employs cheap, short-living trait vector objects that are created anew for each session and discarded afterward. These disposable objects provide sufficient personalization depth for the session duration while ensuring real-time responsiveness by eliminating dependence on expensive, persistent historical data structures.
4Object-affected harmful factors
If emotional trait vectors are computed per session, then privacy compliance is improved, but computational overhead increases
Solution Approach 1:
The system applies local quality by computing emotional trait vectors only for the specific session context rather than maintaining global user profiles. Each session receives customized trait computation based on its unique behavioral signals, achieving privacy compliance through localized processing while managing computational overhead through context-specific optimization.
Data Source
AI summary
A system and method for real-time, identity-free personalization using deformable emotional trait vectors to dynamically adapt digital and voice-based experiences. Each user session is modeled as a behavioral object known as a Vectra, composed of fluidic traits—such as mass, viscosity, temperature, volatility, and texture—that evolve continuously in response to live behavioral, contextual, environmental, and voice-derived signals. These Vectras traverse a dynamically warped emotional space, the Vectraverse, influenced by ambient conditions including time of day, noise level, inventory urgency, and engagement rhythm. Gravitational pull toward predefined emotional goal attractors modulates system behavior, while a goal mutation engine reclassifies session intent when confidence decays or friction spikes. Outputs include tone modulation, content pacing, offer framing, and gamified reward logic—all executed without storing identity, login credentials, or historical profiles. The system supports modular deployment across voice, screen, signage, mobile, and in-room environments, and integrates with large language models, AI agents, and third-party personalization stacks via privacy-safe APIs and federated learning. Designed for zero-ID personalization, the platform enables emotionally intelligent, context-aware engagement across any surface or session.