Real-time Multi-computer Data Exchange System with ML Negotiation
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Solution Overview
Problem
Current technologies face challenges in providing real-time, customized outputs to users leveraging advanced communication protocols like 5G, particularly in efficiently negotiating and presenting optimized offers based on user location and preferences.
Innovation Solution
A system utilizing 5G technologies to detect user location, identify nearby entities, initiate communication, and employ machine learning to analyze and generate optimized offers, enabling real-time negotiation and presentation of customized deals between user and entity systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If real-time multi-computer data exchange is implemented to provide customized outputs based on user location, then user customization capability is improved, but system complexity increases
Solution Approach 1:
The system is divided into multiple independent computing devices (user device, entity computing systems, negotiation system) that can operate autonomously. Each device handles specific functions (location detection, offer generation, negotiation) rather than a monolithic system, reducing overall complexity while enabling customization.
Solution Approach 2:
A negotiation system acts as an intermediary between the user device and entity computing systems. This mediator automates the complex negotiation process by generating counter-offers based on machine learning, simplifying the interaction model and reducing the complexity of direct multi-party coordination.
2Productivity
If automated negotiation with counter-offer generation is implemented, then negotiation efficiency is improved, but computational requirements increase
Solution Approach 1:
The negotiation system pre-generates counter-offers using machine learning models before actual negotiations occur. By preparing negotiation strategies and counter-offer templates in advance based on historical data and user preferences, the system reduces real-time computational burden while maintaining high negotiation efficiency.
Solution Approach 2:
The negotiation system autonomously generates counter-offers and manages negotiations without requiring continuous human intervention or complex real-time computations. The machine learning model self-adjusts negotiation strategies based on incoming data, reducing the need for intensive computational resources during active negotiation.
3Reliability
If multiple entities are monitored and contacted in real-time, then offer quality is improved, but communication overhead increases
Solution Approach 1:
The system identifies and contacts only those entities that are geographically proximate to the user's location and relevant to the user's needs. By filtering entities based on location data and user preferences before initiation of communication, the system reduces the number of communications required while maintaining offer quality.
Solution Approach 2:
The system performs preliminary filtering and ranking of entities based on location data, user preferences, and historical performance before initiating negotiations. This pre-screening process identifies the most promising entities to contact, reducing communication overhead while ensuring high-quality offers are obtained.
Data Source
AI summary
Arrangements for dynamic data exchange and control are provided. In some examples, user device location data may be detected by a system. The location data may be detected in real-time and one or more entities within a predefined proximity of the detected location may be identified. A communication session may be established with a first entity of the one or more entities identified and a request for a first offer may be transmitted to the first entity. In response, a first offer may be received from the first entity and evaluated to determine whether it is an optimized offer. If so, the offer may be presented to the user. If not, a counter offer may be generated using machine learning. The counter offer may be transmitted to the first entity for evaluation and acceptance or generation of another counter offer. Upon agreeing to a particular offer, the offer may be transmitted to the user device.