Therapies TRIZ Cases

See how a cooking control system adjusts temperature, time, and pressure parameters to alter ch

See how a bathtub system merges objective sensor data with subjective comfort input to build ba

Bringing dental care to schools and other sites cuts travel disruption while route planning, scheduling, and notifications keep visits coordinated.

Sensor data from evacuation vehicles drives personalized health actions that help disaster victims avoid deterioration during car-based sheltering.

Virtual articulator modeling and CAD-CAM create customized dental appliances that guide mandibular movement with less manual fitting time.

Two glucose readings and a linear rise-rate model predict prolonged postprandial hyperglycemia early enough to adjust insulin delivery.

Real-time camera tracking of gaze, head pose, and facial cues helps detect disengagement and improve adherence in home digital therapy.

Visual IoT sensors extract skeletal data to classify sedentary behavior more accurately than wearables or passive sensors, enabling continuous wellbeing monitoring.

Measures selected mRNA and protein biomarkers to detect early inflammatory disease signals and track treatment response more accurately.

Automated risk scoring, care scheduling, and reminders replace handwritten records to maintain continuous pressure ulcer prevention across staff changes.

Generative AI creates custom goal images that update with user progress, improving motivation while limiting image-generation complexity.

Eye-tracking in VR adjusts visual tasks in real time to diagnose disorders and personalize therapy under realistic viewing conditions.

Neural-network analysis links user activity and pain scores to personalized recommendations that support chronic pain self-management.

Real-time code clock tracking records actions, personnel, and timestamps during medical emergencies to reduce omissions and documentation errors.

Segments and aligns diabetes therapy data from multiple devices across time changes, preserving user-relevant event timing for clearer analysis.

Layered 3D body visualization links health assessment data to personalized anatomy, reducing occlusion and improving user understanding.

Biometric signatures from medical device use verify treatment adherence during telemedicine, improving claim adjudication and fraud detection.

Tree-based regression models combine trial and real-world patient data to predict individual treatment outcomes and guide treatment choice.

Terminal logs, user attributes, and questionnaires are combined into affinity scores to recommend health behaviors users can actually follow.

Witness rate and action rate are combined into rescue action conversion efficiency to assess medical rescue organizations more accurately.

Continuous temperature and physiological monitoring detects baseline deviations to predict labor onset earlier and alert users or clinicians.

Wireless proximity detection verifies patient care protocol completion and updates health records without manual charting.

Activity-aware sensor analysis switches between exercise and rest modes to improve dehydration prediction and water intake guidance.

An AI chatbot triages home dialysis questions using device and EMR data, cutting nurse interruptions while escalating urgent issues promptly.

Non-invasive saliva testing of SMARCA2 and NOTCH1 markers with machine learning improves early TMJ osteoarthritis diagnosis and prognosis.

Combining EHR, non-invasive, and invasive physiological metrics, this index predicts renal denervation suitability and confirms therapy completeness.

Mobile sensor, travel, and driving data are analyzed with machine learning to automate risk profiling and improve quote accuracy.

Machine learning turns patient and task data into PCNL planning recommendations that improve consistency, staffing, and instrument selection.

Nested storage and specialized repositories link real-time inquiries with entity records to deliver timely, context-aware responses.

Camera scanning and deep learning automate medical device data capture, patient verification, and timely transfer to healthcare providers.

Blood flow data from unstable toilet-use periods is excluded so health indices are calculated only during stable fluctuation states.

LOH region analysis links SNP array data to HDR deficiency, helping predict response to PARP inhibitors, radiation, and DNA-damaging therapy.

Modular rescue robots use drones, self-assembly, and adaptive treatment planning to deliver care reliably in hazardous, unstructured environments.

Product image recognition and user risk modeling simplify oral care selection by matching products and routines to individual needs.

Machine learning predicts provisional FIM scores for therapist correction, reducing assessment time while keeping rehab plans current.

Machine learning predicts provisional FIM scores for therapist review, keeping rehabilitation assessments current without full manual rescoring.

Blockchain storage and patient key verification protect medical questionnaire data while enabling trusted, privacy-preserving access.

Derivative-based distal pressure curve geometry assesses coronary stenosis severity at rest or hyperemia without vasodilating agents.

Inline visual highlighting of medically similar orders helps clinicians catch duplicates in real time without ignored pop-up warnings.

Multi-device home monitoring uses machine learning to detect cognitive or physical decline early and trigger assistance for elderly people living alone.

A frequency-ratio model classifies cfDNA variants as tumor or non-tumor using plasma and reference blood datasets, avoiding patient WBC sequencing.

An AI engine turns condition data into action instructions, medical-record views, and curated education within one patient viewer.

Patient and tumor data determine electrode count, placement, and field strength to target tumors while limiting normal-cell damage.

Thermal imaging tracks body temperature and sleep stages without physical contact, helping assess sleep quality and quantity.

Removing visual, audio, and tactile alerts keeps workers focused while remote monitors track abnormal vital signs.

Event handlers update only affected visual elements, helping a GUI reflect ongoing execution data without costly continuous processing.

Combining liver blood markers with logistic risk scoring identifies high-risk chronic liver disease patients for targeted surveillance.

Tracking staff and equipment against procedure steps gives patients real-time updates that reduce anxiety without constant staff intervention.

Predicted FIM values let therapists review and correct assessments faster, keeping rehabilitation plans aligned with a patient’s latest condition.

Automated reminders and device transmission address missed patient measurements while reducing routine provider follow-up work.

TTL jobs schedule patient communications at care-plan intervals while response tracking helps providers monitor adherence outside medical facilities.

Automatic DICOM export and encrypted USB storage keep patient treatment plans available when record-and-verify networks fail.