First Artificial Intelligence Methods and Systems for Asset Trendspotting (PNN), Cyber Security (DeepCyber), and Portable Big Data Cloud (MCPS)

• US 20160269378A1
• Filed: 03/14/2015
• Published: 09/15/2016
• Est. Priority Date: 03/14/2015
• Status: Abandoned Application

• Current Assignee: Gewei Ye
• Original Assignee: Gewei Ye
• Inventors: Ye, Gewei
• Application Number:
  US14/658,156
• Publication Number:
  US 20160269378A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

New methods, systems, and apparatus, including computer programs called PNN (predictive neural network) artificial intelligence (AI) machines are disclosed for financial and cyber-security predictions. The PNN AI machines use unique neural network algorithms to optimize and select the best method with the highest back-test accuracy for the predictions. The PNN machines predict multiple relevant entities (e.g., stocks) to cross validate the future trends, and help respond to future financial and cyber-security crises like weather forecast to severe weather conditions. The PNN AI machines are applied in two fields: PNN Financial for investment and trading, and DeepCyber for cyber security. Extended from the PNN artificial neural networks (intelligence) machines, a group of DeepCyber methods based on the Mobile Cloud Pangu Servers (MCPS) cloud platform are disclosed to defend networks and computer applications for cyber security.

Claims

1. A group of computer-implemented systems called the PNN (Predictive Neural Networks) AI machines comprising:

distributed computers that provide intuitive Web user interfaces (see FIG. 1);

distributed computers that execute the PNN algorithms to optimize and select the best result with the highest back-test accuracy (see FIG. 4,7);

computer programs that implemented artificial neural networks and sentiment algorithms (see FIG. 4,7,9);

3. PNN Financial charts that are produced from the systems of claim 1 (see FIG. 2).

5. systems of claim 1 that process financial prediction requests within minutes for over 10,000 stocks, 1500 ETFs (Exchange-traded Funds), and 1900 mutual funds, 24 by 7 (FIG. 2);

2. the first data science computer system of artificial neural networks and deep learning for financial predictions (see FIG. 3);

4. back-test accuracy validation method for the PNN charts:

the real-time high back-testing accuracy percentages show the validities of the PNN machines in the financial predictions, which is based on testing the PNN artificial neural networks (intelligence) and sentiment algorithms against historical price data (see FIG. 4);

6. methods of cross-validating financial prediction results with multiple relevant securities (e.g., SCO and UCO for oil industry) and creating very high back-test accuracies based on the artificial neural-networks algorithms (see FIG. 2, 8).

7. systems that power PNN Financial on the MCPS (Mobile Cloud on Pangu Servers) cloud platform (see FIG. 12).

8. A group of computer-implemented methods called the MCPS and DeepCyber solutions comprising:

novel artificial neural-networks (intelligence) methods and systems called DeepCyber on the MCPS (Mobile Cloud on Pangu Servers) cloud (see FIG. 10);

MCPS-based DeepCyber artificial intelligence solutions;

a series of U.S. military-grade software as a service (SaaS) cloud-based cyber-security solutions (see FIG. 22);

9. DeepCyber artificial-intelligence and deep-learning cloud-based engines, including the first artificial neural-networks (ANN) cloud engine (see FIG. 33) and the automatic causal modeling (ACM) cloud engines (see FIG. 9 and FIG. 19);

10. DeepCyber Value at Risk (VaR) smart cloud engines with Big Data analytics (see FIG. 18 and FIG. 23);

11. DeepCyber Cyber Fault-tolerant Attack Recovery (CFAR) engine with artificial-intelligence capabilities for zero-day cyber-attacks that are not signature-based (see FIG. 36);

12. DeepCyber multi-zone (firewall) smart security architecture (MSA) to defend network intrusions (see FIG. 38);

13. DeepCyber solutions designed for U.S. Department of Defense DARPA (Defense Advanced Research Projects Agency)'"'"'s BAA (Broad Agency Announcement) specifications (DARPA-BAA-15-13 and DARPA-BAA-14-64) to defend the most sophisticated cyber-attacks in the world (see FIG. 12, 37);

14. MCPS cloud:

the integrated cloud-based platform of artificial intelligence and Big Data includes similar capabilities of combining AWS (Amazon Web Services), DropBox, Cloudera, and SAS (see FIG. 11);

15. MCPS data center:

a powerful, portable and interoperable platform for cyber defense (e.g., DeepCyber), financial predictions (e.g., PNN Financial), drones, data center, and Internet of Things (see FIG. 13, 14, 15, 16);

16. MCPS analytics:

the unique game-changing portable cloud data center, originally designed for 18 DARPA specs including processing Big Data and artificial-intelligence workload of advanced analytics and artificial intelligence (see FIG. 12).

17. MCPS portable cloud platform:

the first portable cloud data center initially designed for ground and aerial vehicles (see FIG. 11, 13, 16).