Machine Learning Social Network for Financial News
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Solution Overview
Problem
Current social networks lack optimization for specific areas of interest, particularly in finance and investing, failing to provide users with tailored news sharing and trading solutions that account for individual needs and preferences, leading to inadequate decision support for financial instrument selection.
Innovation Solution
A machine-learning-enhanced social network system utilizing virtual cards for financial news sharing and trading, optimized with a large language model trained in finance and investing, allowing users to share and discuss financial news, and make informed trading decisions through a dedicated platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general-purpose social networks are used, then broad user coverage is achieved, but optimization for specific areas of interest is lost
Solution Approach 1:
The patent segments the social network system into multiple specialized networks, each dedicated to a specific area of interest (e.g., finance, technology, healthcare). This segmentation allows each network to be optimized for its domain while the overall system provides diverse specialized services, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The patent introduces machine learning models as intermediaries that analyze user content, preferences, and behavior to automatically route users to appropriate specialized networks and personalize content delivery. This intermediary layer enables specialized optimization without requiring users to manually navigate complex system choices.
2Adaptability or versatility
If simple machine-learning algorithms are used, then system complexity is reduced, but personalization and optimization capabilities are insufficient
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models with extensive domain-specific data before deployment. These pre-trained models can then provide sophisticated personalization and content optimization with reduced computational overhead during runtime, balancing personalization capability with system complexity.
Solution Approach 2:
The system employs self-service mechanisms where machine learning models continuously learn from user interactions, content engagement patterns, and feedback without requiring manual reconfiguration. This automated learning enables progressive personalization while keeping the system architecture relatively simple.
3Adaptability or versatility
If free-form graphical user interface is used, then ease of operation is improved, but optimization for specific areas of interest is reduced
Solution Approach 1:
The patent implements dynamic user interfaces that automatically adapt their structure, content, and functionality based on the user's selected domain of interest and observed behavior. The interface transitions from static free-form design to dynamic domain-optimized layouts, providing both ease of operation and domain-specific optimization through adaptive reconfiguration.
4Reliability
If general social network algorithms are used, then system simplicity is maintained, but decision support for financial instrument selection is inadequate
Solution Approach 1:
The patent applies local quality by implementing specialized machine learning models and algorithms tailored to specific domains, particularly finance and investing. These domain-specific components provide enhanced decision support quality for financial instrument selection while maintaining simpler general-purpose algorithms for other areas, thus resolving the contradiction between reliability and overall system complexity.
Data Source
AI summary
A virtual card server comprising a machine intelligence engine such as a Large Language Model (LLM) neural network system. This virtual card server is configured to communicate with a plurality of user computerized devices, receive news feeds, communicate with third-party servers, and implement a virtual card-oriented social network over at least one domain of interest. The machine intelligence engine is trained over the domain(s) of interest and can work with the server to notify social network users about relevant news feed items, as well as to automatically select and promote relevant social network postings about the field of interest. Additionally, users can use the social network to send commands to third-party servers, such as financial and investment transaction servers.