Derivative works

Defensive Disclosure: System and Method for Facilitating Bilateral and Multilateral Decision-Making

Publication Date: October 26, 2023

Subject: This document discloses a series of derivative inventions and improvements upon the core technological principles described in U.S. Patent US8069073B2, titled "System and method for facilitating bilateral and multilateral decision-making." The purpose of this disclosure is to establish prior art for a range of foreseeable and less obvious applications, thereby rendering them obvious or non-novel for future patent applications. The core innovation of US8069073B2, a system for computer-assisted preference elicitation and negotiation, is herein expanded into new domains and integrated with emerging technologies.

I. Core Claim 1: A computer-implemented method for facilitating a multi-attribute value exchange between at least two parties.

Derivation 1.1: Material & Component Substitution

• 1.1.1. Quantum Annealing for Preference Optimization:

  • Enabling Description: The core method's computational substrate for determining optimal outcomes is replaced with a quantum annealing processor. User-defined attributes and constraints are mapped to a Quadratic Unconstrained Binary Optimization (QUBO) problem. The quantum annealer explores a vast solution space to identify globally optimal or near-optimal deal structures that satisfy the complex, and potentially conflicting, preferences of all parties simultaneously. This is particularly advantageous for negotiations with a high number of variables and participants, where classical computation would be intractable.

• 1.1.2. Neuromorphic Computing for Real-time Preference Adaptation:

  • Enabling Description: The system is implemented on a neuromorphic computing architecture, such as Intel's Loihi or IBM's TrueNorth. This allows for real-time adaptation to changing party preferences by modeling the negotiation as a network of spiking neurons. Changes in a party's stated preferences or concessions are translated into synaptic weight adjustments, enabling the system to learn and predict emergent negotiation dynamics and suggest compromises that are more likely to be accepted.

• 1.1.3. Homomorphic Encryption for Secure Multi-Party Computation:

  • Enabling Description: The exchange of preference data between parties is secured using fully homomorphic encryption. Each party's preference data is encrypted before being transmitted to a central server or distributed ledger. The computational analysis of potential agreements is performed on the encrypted data without decryption, ensuring that no party, including the system administrator, has access to the raw preference data of any other party, thereby enhancing privacy and security in sensitive negotiations.

• 1.1.4. Decentralized Ledger Technology (Blockchain) for Verifiable Agreements:

  • Enabling Description: The process of reaching an agreement and the final terms of the agreement are recorded on a distributed ledger. Each step of the negotiation, including offers, counter-offers, and acceptances, is a transaction that is cryptographically signed by the participating parties and added to an immutable chain of blocks. This provides a verifiable and tamper-proof audit trail of the entire negotiation process.

• 1.1.5. Edge Computing for Latency-Sensitive Negotiations:

  • Enabling Description: For real-time, high-stakes negotiations, such as automated high-frequency trading or robotic swarm coordination, the core decision-making algorithm is deployed on edge computing devices. This minimizes latency by performing computations closer to the data source, eliminating the need for round-trip communication with a centralized cloud server.

Derivation 1.2: Operational Parameter Expansion

• 1.2.1. Nanoscale materials design:

  • Enabling Description: The patented method is applied to the design of novel metamaterials. Multiple research groups (parties) input their desired material properties (attributes) such as negative refractive index, specific tensile strength, or thermal conductivity. The system then explores a vast combinatorial space of nanoscale structures and chemical compositions to identify novel material designs that satisfy the multi-attribute requirements of the participating groups.

• 1.2.2. Industrial Scale Supply Chain Optimization:

  • Enabling Description: The system is scaled to manage the complex negotiations within a global supply chain. Thousands of suppliers, manufacturers, and distributors (parties) input their production capacities, costs, delivery times, and quality metrics (attributes). The system continuously calculates optimal sourcing and logistics arrangements in real-time to respond to disruptions, demand fluctuations, and geopolitical events.

• 1.2.3. Extreme Temperature Environments - Fusion Reactor Maintenance:

  • Enabling Description: In the context of remotely operated maintenance of a fusion reactor, multiple robotic arms (parties) must negotiate their actions to perform a complex repair in a high-temperature, high-radiation environment. Each robot has a set of operational constraints and objectives (attributes). The system facilitates a rapid, automated negotiation between the robots to determine a sequence of actions that accomplishes the repair task without collisions or damage to the reactor.

• 1.2.4. High-Frequency Spectrum Allocation:

  • Enabling Description: The system is used for dynamic, real-time allocation of radio frequency spectrum among multiple competing wireless communication providers (parties). The attributes for negotiation include bandwidth, latency, power levels, and geographic coverage. The system facilitates sub-second negotiations to reallocate spectrum based on real-time demand and network congestion, maximizing spectral efficiency.

• 1.2.5. High-Pressure Subterranean Resource Extraction:

  • Enabling Description: In deep-sea or subterranean mining operations, multiple autonomous drilling and extraction robots (parties) must negotiate their operational parameters. These attributes include drilling speed, extraction volume, power consumption, and risk of geological instability. The system enables the robots to collaboratively optimize the extraction process while minimizing the risk of catastrophic failure under high-pressure conditions.

Derivation 1.3: Cross-Domain Application

• 1.3.1. Aerospace - Satellite Constellation Resource Management:

  • Enabling Description: A constellation of satellites, operated by different entities (parties), uses the system to negotiate the allocation of limited resources, such as communication bandwidth, processing power, and earth observation time. Each satellite operator inputs their mission priorities and resource requests (attributes). The system facilitates a dynamic, autonomous negotiation to optimize the overall performance of the constellation for various tasks, such as disaster monitoring, climate research, and global communication.

• 1.3.2. AgTech - Precision Agriculture Cooperative Farming:

  • Enabling Description: A cooperative of farmers (parties) uses the system to manage shared resources, such as irrigation systems, harvesting equipment, and drone-based crop monitoring services. Each farmer inputs their crop types, planting schedules, and resource needs (attributes). The system optimizes the allocation of these resources across all farms in the cooperative to maximize collective yield and minimize water and energy consumption.

• 1.3.3. Consumer Electronics - Smart Home Device Interoperability:

  • Enabling Description: Within a smart home environment, multiple devices from different manufacturers (parties), such as lighting systems, HVAC, and security cameras, use the system to negotiate their operational parameters to achieve a user's desired outcome (e.g., "movie mode"). The attributes for negotiation could include lighting levels, room temperature, and power consumption. The system enables these heterogeneous devices to collaboratively create a seamless and personalized user experience.

Derivation 1.4: Integration with Emerging Tech

• 1.4.1. AI-driven Predictive Negotiation:

  • Enabling Description: The system integrates a machine learning model, trained on historical negotiation data, to predict the likely outcomes of a negotiation and suggest optimal strategies. The AI analyzes the stated preferences and past behaviors of the negotiating parties to identify potential areas of compromise and warn of potential deadlocks before they occur.

• 1.4.2. IoT Sensors for Real-time Data-driven Negotiation:

  • Enabling Description: In a manufacturing setting, IoT sensors on production machinery provide real-time data on machine health, production output, and energy consumption. This data is fed directly into the decision-making system, allowing for dynamic rescheduling of production runs and maintenance activities based on the real-time state of the factory floor.

• 1.4.3. Blockchain for Smart Contract Execution:

  • Enabling Description: Once an agreement is reached through the negotiation process, the terms are encoded into a self-executing smart contract on a blockchain. The smart contract automatically enforces the terms of the agreement, such as triggering payments upon delivery of goods or services, without the need for intermediaries.

Derivation 1.5: The "Inverse" or Failure Mode

• 1.5.1. Graceful Degradation in Networked Systems:

  • Enabling Description: In a telecommunications network, in the event of a partial network outage, the system facilitates a negotiation between network nodes to operate in a "limited-functionality" mode. The nodes negotiate to prioritize critical traffic (e.g., emergency services communication) while gracefully degrading non-essential services, ensuring the continued operation of the most critical network functions.

• 1.5.2. Safe Failure Modes in Autonomous Vehicle Platoons:

  • Enabling Description: For a platoon of autonomous trucks, if a critical sensor on one vehicle fails, the system initiates a negotiation between the vehicles to establish a safe failure mode. This could involve increasing the following distance between trucks, reducing the platoon's speed, or having a "healthy" truck escort the malfunctioning vehicle to a safe stopping location.

• 1.5.3. Low-Power Mode for Off-Grid IoT Deployments:

  • Enabling Description: A network of battery-powered environmental sensors uses the system to negotiate their data transmission schedules to maximize the collective lifespan of the network. When battery levels are low, the sensors negotiate to enter a low-power mode, reducing the frequency of data transmission and prioritizing the collection of the most critical environmental data.

• 1.5.4. Conflict De-escalation in Social Robotics:

  • Enabling Description: In a scenario with multiple service robots operating in a public space, if the robots' intended paths conflict, the system facilitates a negotiation to de-escalate the potential for physical collision or deadlock. The robots negotiate a set of non-verbal cues (e.g., specific movements or light patterns) to signal their intentions and yield to one another in a socially acceptable manner.

• 1.5.5. Failsafe Resource Allocation in Critical Infrastructure:

  • Enabling Description: In the event of a power grid emergency, the system is designed to facilitate a rapid, automated negotiation between different sectors of the grid (e.g., industrial, residential, and emergency services) to implement a load-shedding protocol. The system is pre-configured with "failsafe" allocations that prioritize the delivery of power to critical infrastructure, such as hospitals and emergency response centers, even if a complete negotiation is not possible due to time constraints or communication failures.

II. Combination Prior Art Scenarios

• 2.1. Integration with W3C's Web of Things (WoT) Standard:

  • Enabling Description: The patented method is combined with the W3C WoT Thing Description standard to enable seamless negotiation between IoT devices from different manufacturers. Each device's capabilities, properties, and actions are described using a standardized JSON-LD format. This allows the decision-making system to automatically discover and interact with heterogeneous devices, facilitating complex, cross-platform negotiations for resource allocation and collaborative task execution in smart environments.

• 2.2. Combination with the OPC Unified Architecture (OPC-UA) for Industrial Automation:

  • Enabling Description: The system for multi-attribute value exchange is integrated with the OPC-UA communication protocol for industrial automation. This enables different machines and control systems on a factory floor to securely and reliably exchange data and negotiate production parameters in real-time. For example, a CNC machine, a robotic arm, and a quality control sensor could negotiate their operational speeds and tolerances to optimize the production of a complex part, with all communication and data exchange conforming to the OPC-UA standard.

• 2.3. Use with the HL7 FHIR Standard for Healthcare Interoperability:

  • Enabling Description: The patented method is applied to the coordination of patient care across different healthcare providers, utilizing the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. A patient's care team, including a primary care physician, a specialist, and a hospital (parties), can negotiate a treatment plan (a multi-attribute value exchange) by securely exchanging patient data formatted according to the FHIR standard. This ensures that all parties have a consistent and up-to-date view of the patient's medical history and treatment plan, leading to better-coordinated and more effective care.