Image-Assisted Property Damage Assessment via Machine Learning
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Solution Overview
Problem
There is a lack of consistency in identifying and calculating damages to property during rentals or leases, leading to trust issues between parties.
Innovation Solution
A method and system for image-assisted identification of property changes using a mobile device to capture images, identify items, tag them with descriptions, and compare conditions over time, determining unacceptable changes and associated costs through a backend program and potentially a blockchain, with the aid of a trained machine learning algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual damage identification and cost calculation methods are used, then flexibility in assessment is maintained, but consistency and trust between parties deteriorate
Solution Approach 1:
The patent replaces manual mechanical assessment processes with automated image processing and machine learning algorithms. The system captures images of property at different times, uses computer vision to automatically identify and compare items, and applies trained machine learning models to assess damage objectively, eliminating subjective human judgment and ensuring consistent evaluation across all assessments.
Solution Approach 2:
The patent creates digital copies of property through comprehensive image capture at the beginning and end of rental periods. These image copies serve as permanent records that can be automatically compared and analyzed, replacing physical inspection processes and providing an immutable reference for damage assessment that both parties can verify.
2Measurement precision
If automated image processing is implemented, then consistency and transparency are improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent develops a universal damage assessment system that can handle multiple types of property (vehicles, residences, personal belongings) through a single integrated platform. The machine learning model is trained on diverse datasets and can identify various item types and damage conditions, making the system broadly applicable across different rental scenarios without requiring separate specialized systems for each property type.
Solution Approach 2:
The patent performs preliminary actions by capturing comprehensive baseline images and descriptions of property conditions at the beginning of rental periods, and by pre-training machine learning models on extensive datasets of items and damage types. This preliminary data collection and model training enables rapid, accurate assessment at the end of rentals without requiring complex real-time analysis during the actual damage evaluation.
3Measurement precision
If comprehensive item identification and tracking are performed, then accuracy of cost determination is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary item identification and categorization during the initial image capture phase, creating a structured database of all property items with their descriptions and locations. This pre-processing enables rapid comparison and damage identification at the end of rentals, as the system only needs to detect changes rather than identify all items from scratch, significantly reducing assessment time.
Solution Approach 2:
The patent replaces time-consuming manual item-by-item inspection with automated image processing and comparison algorithms. The system uses computer vision to quickly analyze images, identify items, detect changes between time points, and calculate costs automatically, reducing the assessment process from potentially days of manual work to minutes or seconds of automated processing.
Data Source
AI summary
Systems and methods for determining estimated returns for image-assisted identification of property changes are disclosed. In one embodiment, a method for may include a computer program executed by mobile electronic device: (1) receiving a first image of a property captured by an image capture device at a first time; (2) identifying a plurality of first items in the first image; (3) tagging each of the plurality of first items with a first description by comparing each of the plurality of first items in the first image with a database of items and descriptions; (4) generating a list of the descriptions for the property; (5) receiving a first condition for each of the plurality of first items on the list; (6) communicating the list to a backend computer program; and (7) saving the list and descriptions.