Prediction algorithm for real-time business decisions
This technology is a stochastic control system that uses simulation models and reinforcement learning to analyze threat events across multiple operational levels and suggest critical decisions regarding business profitability and logistic efficiency.
Unmet Need: An integrated real-time system that can assess business risk and uncertainty
Computer automation and prediction methods for complex business process analyses are often problematic due to the multiple levels of business operations and lack of integration between them. Furthermore, at any level of operation, variables may arise that can cause delays in one area of the business that subsequently have downstream effects on the efficiency of the entire business. The current fragmented approaches overlook this complexity of businesses and resulting decisions are made in isolation of other departments. This lack of integration leads to missed synergies and loss of efficiency within the business.
The Technology: A computer-based algorithm for integrated business decisions
This technology offers an integrated computer-based approach for uniting risk assessments at various operational levels to produce the most informed decision in uncertain situations. This technology uses Decision Support Threat Simulator (DSTS), which utilizes computer-based simulations to accurately analyze threat events in real-time and subsequently prioritize responses. Furthermore, the unified reinforcement learning algorithm encompasses learning matrices across all operational levels, which process situations at individual levels and integrate incoming information to produce optimal outcomes based on prior scenarios. In this manner, the technology is capable of not only integrating risk information at each level of the business but also learning from past scenarios in order to improve future decisions.
Applications:
- Supervisory and regulatory control of businesses
- Portfolio management and strategic planning of a business
- Risk assessment
- Sub-sea decisions on the timing of gas, oil, and water production
- Training power grid control operators
- Training employees for responding to threats or natural disasters
- Guiding car navigation systems
Advantages:
- Real-time risk assessment and decision making
- Integrated decision making across multiple input channels or operational levels
- Increases efficiency and accuracy of business planning
- Increases profitability
- Machine learning optimizes future outcomes
Lead Inventor:
Patent Information:
Related Publications:
Tech Ventures Reference:
IR M04-006
Licensing Contact: Dovina Qu