Finance TRIZ Cases

Vehicle registration and insurance data are sent through the digital key sharing flow, cutting document handoff time while preserving authorization proof.

Ambient road images are analyzed without event triggers to predict driving risk more accurately while reducing processing load.

Multi-sensor event analysis combines camera and inertial data to identify traffic-event causes and reinforce safe driving in real time.

Pre-operation coverage checks let shared vehicles verify driver insurance or secure on-demand coverage before access, reducing owner liability.

Telematics data detects crashes and injury likelihood, then triggers emergency messages to speed assistance when drivers cannot call.

Telematics data detects crash severity and pre-generates insurance claims, cutting reporting delays and improving payout accuracy.

Telematics and device interaction data are classified across multiple phones to separate driver and passenger usage and protect rating accuracy.

Alerts drivers before unsafe autonomous route segments so they can switch to manual driving and support route-based risk assessment.

Real-time switching between grid power and on-site stored-energy generation cuts price volatility exposure and peak electricity costs.

Ontology-based translation of OEM feature terms links build, crash, usage, and software data to score safety feature updates accurately.

Aggregating data from relevant vehicle sensors helps verify event claims, build accurate timelines, and reduce reliance on operator narratives.

Comparative telematics feedback turns raw driving attributes into clear user displays that improve engagement and encourage safer driving.

Pre-aggregated and denormalized telematics attributes enable real-time driver scoring without long data collection delays.

Telematics-based crash severity analysis triggers emergency assistance requests with driver approval, cutting response delays and claim handling time.

Vehicle-speed-based display modes keep digital license plate data readable, limit power use, and support real-time updates and theft alerts.

GPS and telematics automate supervised driving logs, verify day and night hours, and support safe-driving rewards and premium discounts.

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

By combining vehicle telematics with phone mode data, this case shows how driver detection enables personalized safe-driving incentives.

A vehicle control computer builds and switches driver profiles from behavior data to adapt trips faster while improving safety.

Wavelet decomposition with rolling analysis detects seasonal energy products faster, improving margin accuracy while meeting calculation deadlines.

Standardized safety feature mapping links OEM build data with accident records to score real-world effectiveness across vehicles.

Adaptive accelerometer, gyroscope, and GPS sampling identifies trips in real time while reducing battery drain and processing load.

Performance data and update history are combined to detect autonomous vehicle state changes for maintenance, insurance, and operation decisions.

Tamper-resilient metering data, hashing, and timestamping enable accurate REC verification and trusted DIGIREC issuance.

Direct inverter data, ambient sensing, and cryptographic validation improve renewable energy production tracking and certificate issuance.

Trusted terminals, blockchain consensus, and smart contracts secure two-way electricity data and automate tamper-resistant settlement.

Voice biometrics are cross-checked with vehicle telemetry and sensor data to block fraud, interception, and recorded speech insertion.

Combines affixed and detachable vehicle device data into one dashboard value while validating consent and securing sharing through blockchain consensus.

Connection-status scoring lets aggregators reward EV users across home, workplace, and public charging to improve grid balancing participation.

Telematics data is filtered and calibrated into an ADAS risk score that predicts accident frequency and severity more precisely.

Driving behavior profiles are matched to routes and conditions so vehicle controls can adapt trip by trip and improve accident prevention.