AI Component Selection for Brain-Guided Robotic Processes
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Solution Overview
Problem
Lending transactions face challenges such as opacity and asymmetry of information, moral hazard, complexity in application and negotiation processes, and regulatory burdens, making it difficult to determine the value of collateral and the reliability of entities involved.
Innovation Solution
A lending transaction enablement platform incorporating data-integrated microservices, including data collection and monitoring, blockchain, and smart contract services, which utilizes IoT, crowdsourcing, and social network analytics to monitor assets and entities, adjust interest rates, and automate debt restructuring, while ensuring compliance and reliability validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional lending processes are used, then operational simplicity is maintained, but transparency and information symmetry deteriorate
Solution Approach 1:
The system segments information collection from multiple sources (IoT devices, social networks, blockchain ledgers, crowdsourcing platforms) into distinct data streams that are processed independently before being integrated into a comprehensive entity profile. This segmentation allows each data source to maintain its own verification protocols while contributing to overall transparency.
Solution Approach 2:
The patent introduces an intermediary AI platform that acts as a mediator between various data sources (IoT sensors, social networks, blockchain) and lending decisions. This intermediary consolidates fragmented information, validates data through multiple channels, and presents synthesized intelligence to lenders, thereby improving information symmetry without requiring direct integration of all underlying systems.
2Productivity
If manual application and negotiation processes are used, then process complexity is reduced, but productivity and efficiency deteriorate
Solution Approach 1:
The system implements self-service capabilities where AI agents automatically perform application review, credit assessment, and loan term negotiation without human intervention. The AI platform autonomously collects data from multiple sources, evaluates risk profiles, and generates lending decisions, thereby dramatically improving productivity while the modular architecture manages complexity.
Solution Approach 2:
The patent applies preliminary action by pre-collecting and pre-analyzing data from IoT devices, social networks, and blockchain ledgers before the actual lending decision is required. This pre-processing of information enables faster decision-making and reduces the complexity of real-time processing during the actual lending transaction.
3Reliability
If comprehensive data collection is implemented, then entity reliability assessment is improved, but system complexity and regulatory burden increase
Solution Approach 1:
The patent creates a universal AI platform that handles multiple functions: data collection from diverse sources, validation across different data types, risk assessment, and compliance monitoring. This multi-functional system improves entity reliability assessment while managing complexity through a single integrated platform rather than separate systems for each function.
Solution Approach 2:
The system dynamically adjusts the parameters and depth of data collection based on the specific lending context, entity type, and risk profile. Rather than universally collecting all possible data, the AI platform selectively gathers information at appropriate levels of detail, improving reliability where needed while reducing unnecessary complexity in low-risk scenarios.
Data Source
AI summary
A method for selecting an AI solution for an automated robotic process including receiving at least one functional media including information indicative of brain activity by a human engaged in a task of interest, analyzing the functional media, identifying an activity level in at least one brain region, identifying a brain region parameter and an activity parameter; identifying an action parameter based in part on the brain region parameter or the activity parameter; and selecting a component of the AI solution in part on the brain region parameter, the activity parameter, or the action parameter.