Robotic Process Configuration Using Biometric Reasoning Signals
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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, burdensome regulatory regimes, and difficulties in determining collateral value and financial health of entities.
Innovation Solution
A lending transaction enablement platform incorporating data-integrated microservices, including data collection and monitoring, blockchain, and smart contract services, with IoT, crowdsourcing, and social network analytics to automate processes like interest rate adjustments, debt restructuring, and loan 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 patent introduces a blockchain-based intermediary platform that mediates between lenders and borrowers. This platform uses smart contracts to automatically execute lending terms, IoT devices to monitor collateral conditions, and distributed ledgers to record all transactions transparently. The intermediary system resolves information asymmetry by providing all parties with access to the same verified data while maintaining operational simplicity through automation.
2Productivity
If manual application and negotiation processes are used, then process flexibility is maintained, but process complexity and time consumption increase
Solution Approach 1:
The patent implements self-service mechanisms where smart contracts automatically negotiate and execute lending terms based on pre-programmed conditions. IoT devices autonomously monitor collateral and trigger events without human intervention. The system allows parties to input their requirements and have the blockchain network automatically match them with suitable counterparties, eliminating manual application processes while maintaining flexibility through programmable logic.
Solution Approach 2:
The patent employs preliminary action by pre-configuring smart contracts with all necessary lending terms, conditions, and automation logic before transactions occur. Collateral monitoring parameters are pre-set, and automated enforcement mechanisms are pre-established. This allows the system to rapidly process transactions without real-time human intervention, significantly increasing productivity while the initial setup complexity is managed through standardized templates.
3Measurement precision
If traditional collateral valuation methods are used, then assessment simplicity is maintained, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent replaces manual collateral valuation methods with automated sensor-based monitoring systems. IoT devices physically attached to collateral items continuously measure conditions such as location, temperature, humidity, and operational status. This substitution of mechanical human assessment with automated electronic sensing dramatically improves measurement precision and reliability while the data is automatically fed into the blockchain ledger for transparent recording.
4Reliability
If extensive regulatory compliance checks are performed manually, then compliance thoroughness is maintained, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements continuous feedback mechanisms where smart contracts automatically monitor transaction conditions against pre-programmed regulatory requirements. The system provides real-time feedback on compliance status to all parties and automatically triggers corrective actions or alerts when deviations occur. This continuous automated monitoring ensures thorough compliance verification while reducing processing time compared to periodic manual audits.
Data Source
AI summary
A method for selection and configuration of an automated robotic process includes receiving a temporal biometric measurement of a worker performing a task, receiving a spatial-temporal environmental input provided to the worker, identifying a type of reasoning used when performing the task partially based on the temporal biometric measurement of the worker, selecting a component of an AI solution to replicate the type of reasoning, and configuring the component of the AI solution based on the spatial-temporal environmental input. The temporal biometric measurement includes a set of spatial-temporal imaging data of a brain of the worker and identifying the type of reasoning includes identifying a set of spatial-temporal neocortical activity patterns of the worker, identifying an active area of a neocortex of the worker; and selecting the component of the AI solution partially based on the identified active area of the neocortex.