# US 2026/0087553 A1 · Optimized Personalized Advisor Digital Twin Training Engine for Wealth Management

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- Number: US 2026/0087553 A1
- Document ID: US20260087553A1
- Status: Published application
- Kind: A1
- Application number: 19/405,637
- Filed: 2025-12-02
- Published: 2026-03-26
- Earliest claimed: 2025-12-02
- Inventors: George William Bickerstaff, III
- Web page: https://patentgrep.com/patent/US20260087553A1

## CPC

- G06Q 40/06: Asset management; Financial planning or analysis
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

An optimized personalized advisor digital twin training engine employs a custom-configured LSTM neural network and BERT-based natural language processing to create advisor-specific digital twins, simulating real-time client interactions in wealth management. Adaptive refinement using a proprietary Q-learning-based reinforcement learning algorithm ensures at least 95% behavioral accuracy with latency below 5 milliseconds, while proprietary rule-based templates embed financial compliance requirements. Delivered via a Moodle-compatible HTML5 platform with performance analytics achieving at least 90% scoring accuracy, and logged in a Corda blockchain with a custom protocol for auditability, the system enhances training effectiveness and supports cross-firm scalability as of Dec. 2, 2025.

## Claims

### Claim 1

A computer-implemented method for personalized advisor training in wealth management, comprising: (a) Collecting advisor data, including communications, decisions, and behavioral patterns, from integrated financial platforms via proprietary secure APIs, stored in a vector database with latency below 5 milliseconds; (b) Generating advisor-specific digital twins using a custom-configured LSTM neural network and a fine-tuned BERT-based natural language processing model, achieving at least 90% modeling accuracy for wealth management behaviors; (c) Refining digital twins with real-time data using a proprietary Q-learning-based reinforcement learning algorithm, improving behavioral accuracy to at least 95% with latency below 5 milliseconds, updated via real-time data streams; (d) Simulating client interaction scenarios based on digital twins using proprietary rule-based templates, embedding financial compliance requirements specific to wealth management, with generation latency below 5 milliseconds; (e) Delivering scenarios via a Moodle-compatible HTML5 platform optimized for real-time rendering, providing performance analytics with at least 90% scoring accuracy; (f) Logging all actions in a Corda blockchain ledger with a custom cryptographic signature protocol for auditability.

### Claim 2 (depends on 1)

The method of claim 1, wherein twin generation achieves at least 90% accuracy for multiple advisors across firms.

### Claim 3 (depends on 1)

The method of claim 1, wherein twin refinement improves accuracy to at least 95% on a regular basis using a feedback loop.

### Claim 4 (depends on 1)

The method of claim 1, wherein scenarios achieve full compliance with financial regulations specific to wealth management.

### Claim 5 (depends on 1)

The method of claim 1, wherein performance analytics prioritize scenarios involving high-net-worth clients using a proprietary weighting algorithm.

### Claim 6 (depends on 1)

The method of claim 1, wherein the platform includes web-compatible performance visualizations rendered in real-time.

### Claim 7 (depends on 1)

The method of claim 1, wherein blockchain logging ensures compliance with regulatory audits through a custom protocol.

### Claim 8 (depends on 1)

A system for personalized advisor training, comprising: a processor and a non-transitory memory storing instructions to perform the method of claim 1.

### Claim 9 (depends on 8)

The system of claim 8, wherein APIs are proprietary integrations with financial platforms for real-time data collection.

### Claim 10 (depends on 8)

The system of claim 8, wherein scenarios utilize proprietary rule-based templates for operational efficiency.

### Claim 11 (depends on 8)

The system of claim 8, wherein analytics achieve at least 90% scoring accuracy using a custom comparison model.

### Claim 12 (depends on 1)

A non-transitory computer-readable medium storing instructions to perform the method of claim 1, wherein digital twins support cross-firm scalability through a modular architecture.