Discounts/Incentives TRIZ Cases

See how image capture and user account linking enable multi-user laundry appliances to identify

See how NFC-tagged cups with automated bin recognition, mobile tracking, and reward incentives

See how a coin laundry system uses QR-code displays at store terminals and washing machines to

See how a usage monitor and server track personal care device records, determine reward thresho

See how a single camera with mirrors captures multi-angle views to calculate item volume and we

Automatic compartment dispensing lets an autonomous vehicle leave goods in a secure drop-off area for unattended, accessible retrieval.

Unique coded reward tokens use database verification to stop double redemption and magnetically combine into larger trophies.

Sensor-based scoring of charging, braking, and energy use helps electric two-wheeler riders improve efficiency and battery health.

Surrounding vehicles dock and supply extinguishing water to battery fire vehicles, speeding suppression and reducing damage.

Driving data is compared with past ADAS-aided events to estimate missed alerts and update driver profiles when safety features are deactivated.

A battery collection intermediary matches base collection status with user battery data to target replacement recommendations and lower collection burden.

Multi-agent reinforcement learning positions mobile EV chargers in high-demand zones to improve coverage while limiting movement cost and coordination load.

AI orchestration coordinates flexible loads in real time to mask data center demand and unlock grid capacity without upgrades.

Tracks renewable and grid charging shares to calculate EV travel CO2 emissions more accurately with source-specific emission factors.

Combining tire sensor data, tire type, and weather conditions, this case shows how vehicles warn drivers of hydroplaning and accident risk.

Non-invasive substance sensors and user driving profiles extend impaired-driving detection beyond conventional marijuana test windows.

A unified EV ecosystem coordinates charging, power supply, E-point payments, and carbon rights to manage growing EV infrastructure demands.

Personalized incentives for EV drivers and passengers improve demand response participation and support more reliable power grid balancing.

Vehicle sensor and mobile data replace self-reported inputs to generate faster insurance quotes with more accurate risk assessment.

Customizable telematics alerts and segmented access rights enable real-time driving monitoring without overwhelming parents with excess data.

Compares deactivated ADAS events with historical driving data to estimate missed alerts and flag unsafe driver reliance patterns.

Digital content rewards motivate EV charging power control, including users who do not shop near the station, while preserving contribution-based access.

Lane images and localized incentive icons guide drivers toward safe paths and speed compliance without overwhelming attention.

Zone-based demand response cuts facility grid load during events while preserving occupant comfort and productivity through selective control.

Biometric and vehicle data are combined to detect phone use during trips, enabling real-time driver rewards and credential access control.

Automatically generated sightseeing plans use trip route data and desired stop-by spots to cut planning effort and simplify registration.

Captured destination images verify user-reported location cues, helping dispatch control distinguish genuine delays from intentional billing misuse.

Passenger speech and in-car video cues are matched with POI data to deliver more relevant promotions and local content inside the vehicle.

Priority-based load scheduling cuts facility demand during grid events while limiting disruption to occupant comfort and productivity.

Cloud processing filters nearby connected objects by vehicle heading and location to deliver relevant alerts without excess data load.

Machine learning links retrofit financing, personalized energy plans, and demand response to cut costs while supporting grid stability.

Cloud processing uses vehicle location and heading to filter nearby connected objects and deliver timely traffic signal alerts.

Captures tire state data when vehicles enter a service-station wireless range, enabling abnormality diagnosis, user alerts, and maintenance coupons.

Vague OBD codes are translated into specific vehicle issues, repair actions, and service timing to help operators act without a shop visit.

Privilege signals sent after EV charging conditions are met help amusement parks increase user participation in on-site power control.

Road segment verification rates and guide marks help drivers collect missing sensor data and expand ADS ODD coverage faster.

An identity-managed digital twin customizes vehicle operating-state views while supporting AI updates to drive train parameters.

Discounts for pickup or drop-off near EV chargers help taxi EVs secure charging time during service and improve vehicle availability.

Matched driver tournaments use telematics and mobile data to reward measurable improvement in braking, cornering, and phone use.

Neural networks and real-time vehicle data personalize route, speed, and media settings to improve passenger satisfaction through a digital twin interface.

A prediction model converts vehicle telematics between incompatible system formats, reducing integration errors and enabling agnostic use.

Neural-network digital twin views adapt vehicle settings by user role and satisfaction state to simplify configuration and improve driving experience.

Predicted charging demand and user incentives shift vehicle charging away from peak slots to prevent community electricity shortages.

Wireless monitoring tracks device-level electrical signatures to spot abnormal activity and trigger actions that help prevent home fire damage.

Real-time desirability scores and compliance feedback encourage drivers to follow speed and route suggestions, easing congestion and fuel loss.

Vehicle sensors and telematics turn everyday driving into short safe-driving challenges with automated feedback and randomized rewards.

Neural-network satisfaction detection lets a vehicle digital twin adjust route, speed, audio, and other settings for personalized real-time operation.

When ADAS features are turned off, historical driving data is used to estimate missed alerts and refine driver risk profiles.

Voltage and current monitoring identifies connected devices in rented portable power units, enabling user treatment that discourages battery misuse.

Battery temperature is used to predict EV charging time and fee more accurately, then quantify the savings from battery heating.

Adaptive in-cabin digital twin controls update vehicle commands from trigger conditions, improving personalization and state awareness.

Vehicle alert response and software update status are combined with telematics data to build more complete driver profiles for third-party assessment.

Tracked vehicle and smartphone usage data replace self-reports to generate faster, more accurate auto insurance quotes.

Home-station incentives make battery swap demand more predictable, helping balance registrations and reduce congestion across stations.

Home-station incentives make battery swap demand more predictable, helping balance station load and reduce congestion across EV networks.

During reserved EV charging periods, a server recommends usable nearby facilities based on stay, wait, and travel time while managing charging.

Incentive-based household power sharing with a shared battery and management server helps prevent persistent shortages in off-grid communities.

In-vehicle challenges and sensor-based feedback turn routine driving into continuous safe-driver training with low time demand and reward-based engagement.

Proactive coupon checks match machine-readable data to positive databases to catch invalid or expired coupons before checkout.

Automated private tour grouping uses entity notifications, participation thresholds, and refunds to cut per-person cost and expand reach.

Points are calculated from printed label length and tape value, aligning rewards with actual consumable use in label data sharing.

Verified face matches across multiple locations are merged into one visit record image, improving visit proof without fragmented user records.