Personal Intelligence

This patent discloses a system for providing real-time digital insights about people encountered in daily life by analyzing their facial features and the user's biometric response. By identifying a person's face and retrieving associated "Facial Insights" from a database, the system allows users to view, add, and synchronize annotations in a shared or private digital layer. A critical component is the use of biometric pacing, which adjusts the density of these insights based on the wearer's real-time stress or cognitive load. 

The inventor identifies that social interactions often lack immediate, actionable context, especially when meeting new people or identifying professionals in public spaces. Existing facial recognition systems are typically "static" and do not account for the user's internal state, often overwhelming the wearer with too much data at inappropriate times. Furthermore, there is no standardized way for individuals to "tag" themselves with verified information that others can access securely and privately. 

The system acts as a "Biometric Social Filter" for smart glasses. When a user looks at a person, the system identifies the face and retrieves ranked annotations—such as professional titles, social links, or mutual interests—from a secure database. To prevent "information sprawl" and ensure safety, the system monitors the user’s heart rate and pupil dilation; if stress is detected, the system automatically simplifies or hides the digital overlays. Users can proactively update their own "Facial Insight" profile, choosing what information is public and what remains restricted to specific groups or IDs. 

1. Professional Networking & CRM Integration

Conference attendees can instantly see a person's LinkedIn profile or recent publications as a floating AR tag above their head, enabling better-informed introductions.

2. High-Security Personnel Identification

Security teams can identify authorized staff or "flagged" individuals in a crowd, receiving instant biometric alerts if a potential threat is detected.

3. Adaptive Customer Service

Retail staff or hospitality workers can identify VIP customers and view their preferences and past history, with the system pacing the data to match the worker's current task load.

4. Assisted Social Living for Cognitive Support

Individuals with memory impairments (such as prosopagnosia or early-stage dementia) receive discreet names and relationship reminders for the people they encounter.

5. Verified Personal Branding

Public speakers or celebrities can "tag" themselves with verified digital business cards or links to their work, allowing audiences to interact with their professional portfolio in real-time.

This patent discloses a personal AI architecture designed to operate exclusively on a user’s local hardware to manage their "Epistemic Model"—a digital representation of their knowledge, beliefs, and private experiences. The system utilizes a Consent-Governed Gateway to ensure that any data exchange with external models or networks is strictly filtered and anonymized. This allows a user to benefit from global AI capabilities while maintaining absolute sovereignty over their sensitive personal context. 

Modern AI systems rely on "Cloud-First" architectures where a user’s most private data—emails, photos, and biometric logs—must be uploaded to a central server to provide personalized assistance. This creates a "Privacy Paradox" where users must choose between high-quality AI personalization and the security of their personal information. Furthermore, standard AI models lack a mechanism to verify which parts of a user's knowledge are "private" versus "sharable," leading to accidental data leakage during collaborative tasks. 

The system acts as a Personal Knowledge Firewall. It builds a local "Knowledge Graph" that learns the user's patterns and preferences entirely on-device. When a user interacts with a third-party AI (like a public LLM), the local system intercepts the query and applies Epistemic Masking—stripping away personal identifiers while retaining the necessary context to get a high-quality answer. The Consent-Governed Engine requires explicit user authorization before any fragment of the local epistemic model is shared, ensuring the user remains the sole owner of their digital self. 

1. "Sovereign" Personal Assistant

A highly personalized AI that manages your schedule, health data, and finances locally, providing expert advice without your data ever leaving your smart glasses or phone.

2. Privacy-Safe Collaborative Research

Scientists and engineers can collaborate on shared projects using AI, with the system ensuring that proprietary "knowledge fragments" are masked from other participants.

3. Local-Only Medical Diarization

Healthcare providers can use the AI to summarize patient consultations in real-time, ensuring all sensitive medical data stays on a local, HIPAA-compliant device.

4. Secure Legal and Financial Drafting

Lawyers and financial advisors can use generative AI to draft complex documents while the system redacts sensitive client names and account details before reaching the cloud.

5. Anonymized Social Discovery

The system can match you with others based on shared interests or biometrics (as in your other patents) without actually sharing your raw identity or private data with the social network.

A distributed, consent-governed multi-agent AI architecture that integrates multimodal personal data—physiological, communication, visual, contextual, and neural—into a structured, weighted deliberation framework. The system transforms raw signals into bounded feature representations, orchestrates specialized reasoning agents, and computes consensus outputs using calibrated confidence, reliability, and temporal decay. It incorporates human participation, counterfactual robustness testing, privacy budgeting, and cryptographic verification to deliver transparent, privacy-aware, and self-calibrating decision support.

Conventional AI assistants rely on single-model reasoning and lack structured multi-agent deliberation, dynamic consent enforcement, signal weighting, robustness testing, and formal human integration. They do not adequately manage heterogeneous personal data streams or quantify decision stability, creating risks of overconfidence, privacy exposure, opaque logic, and unreliable outputs in high-stakes financial, medical, legal, and enterprise decisions.

The invention introduces a consent-aware orchestration engine that selectively activates specialized agents based on query context and authorization rules, applies calibrated weighting using confidence and longitudinal reliability metrics, and computes consensus through structured arbitration. It further performs counterfactual recomputation, causal sensitivity analysis, privacy budget accounting, and audit logging, while allowing human voting, constraints, and veto authority within the deliberation loop.

1. Enterprise Strategic Decision Systems
Provides board-level AI arbitration for M&A, capital allocation, and risk strategy by integrating financial, operational, and contextual data into a weighted, auditable consensus framework.

2. Regulated Financial Advisory Platforms
Delivers compliance-ready investment and advisory recommendations using consent governance, reliability-weighted arbitration, and cryptographic verification for transparent decision traceability.

3. Clinical Decision Support Systems
Integrates patient data and physiological signals into a privacy-governed, multi-agent framework that enhances robustness and confidence in treatment and risk evaluations.

4. Spatial & Wearable Computing Platforms
Enables gaze-aware and biometric-informed assistance in AR glasses and wearable devices through real-time signal abstraction and spatial consent enforcement.

5. Government & Defense Intelligence Fusion
Supports high-security, multi-source intelligence analysis with structured arbitration, robustness profiling, and audit-verifiable governance controls.