Finance TRIZ Cases

Combines phone, vehicle, and environment sensor data to separate trip-level driver risk without relying on broad statistical pricing.

Real-time trip data and blockchain smart contracts trigger route, acceleration, and identity alerts to improve ride-hailing safety.

High-rate acceleration, speed, and GPS data are combined to filter noise and harsh braking for more reliable low-impact collision detection.

Aggregated zero-knowledge power transaction records cut blockchain processing load while preventing double counting of supply and demand.

Centralized platoon admission uses verified driver and vehicle data to organize compatible vehicles safely and efficiently.

Crowdsourced vehicle, mobile, and wearable sensors verify hyper-local weather events, improving trigger accuracy without permanent site sensors.

Base sensors screen for crash conditions, then trigger extra mobile sensors briefly to cut power use and improve detection accuracy.

Benchmarking and simulation turn operational, contextual, technical, legal, and cyber data into autonomous vehicle risk classes for coverage pricing.

Machine learning combines telematics, driver, vehicle, and usage data to generate dynamic fleet risk scores that adapt to changing trucks and drivers.

Face recognition unlocks a shared vehicle when an authorized user approaches, removing code scans and plate entry steps.

Adaptive risk models combine traffic, geography, surrounding, behavior, and security inputs to guide real-time autonomous vehicle responses.

Vehicle sensor data detects environmental and building conditions to trigger targeted insurance quotes and reduce blind sales outreach.

Pre-entry voice analysis infers driver irritation before boarding, enabling immediate in-vehicle navigation and sensory assistance.

Onboard video analysis extracts searchable metadata like license plates, while full footage is sent only when users request specific events.

Vehicle sensor data is used to detect local environmental conditions, infer building condition, and trigger more accurate insurance outreach.

Route difficulty scoring normalizes telematics data so driver performance and risk ratings stay comparable across different roads.

Joint price-volume modeling and DER pool energy exchange improve intraday bids while meeting network constraints and heterogeneous asset needs.

Base sensors monitor continuously while extra mobile sensors activate only after a crash condition, cutting power use and improving detection accuracy.

Environmental data from vehicle sensors identifies building conditions and supports targeted insurance quotes without blind sales calls.

Telematics, phone interaction events, and in-vehicle position data are combined to separate driver and passenger mobile use for fairer behavior attribution.

Joint price-volume modeling and iterative MILP scheduling help aggregated DERs trade intraday under network constraints and forecast updates.

A virtual grid coordinates renewable resources, wires, and power deals to deliver preferred low-cost electricity despite fluctuating output.

Camera and vehicle data classify driver gestures into behaviors, improving insurance risk assessment beyond self-reported driving data.

Real-time sensor data verifies emissions cuts or plastic recycling, enabling faster carbon and plastic credit certification with crypto rewards.

Weather-driven simulation links daily grid operation with long-term equipment investment risk and return for autonomous local energy systems.

Multiple vehicle sensors capture tailgating, speeding, and lane-change events to improve driver risk assessment beyond OBD-only monitoring.

Cryptographically secured ledger records let local generators and loads trade energy directly while preserving transaction integrity and transparency.

Unsupervised clustering separates driver and passenger phone use during driving events, cutting false positives in distracted driving assessment.

Driver-linked vehicle settings enforce speed, passenger, location, and time limits to curb risky driving and support more accurate insurance pricing.

Clustered telematics, booking, and maintenance data improve driver behavior scoring beyond incomplete feedback and manual record review.

Continuous driver response and alertness assessment helps autonomous vehicles judge takeover readiness before control shifts in extreme conditions.

Mobile and vehicle sensors classify driver gestures to replace self-reported data and improve insurance risk rating accuracy.