VIN Decoding for Automated Vehicle Feature Text Generation
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Solution Overview
Problem
Car dealerships face challenges in providing accurate and comprehensive textual information about vehicles on their lots, leading to inconsistencies and inefficiencies in online listings, which can deter potential customers due to the lack of coordination between vehicle images and textual descriptions, and the manual effort required to maintain SEO-friendly content is resource-intensive.
Innovation Solution
An automated system that decodes Vehicle Identification Numbers (VINs) to determine vehicle features, prioritizes notable features, and generates comprehensive textual descriptions, ensuring accuracy and efficiency by utilizing build data, industry-standard codes, and predetermined rule sets, while also correlating textual information with vehicle images for precise representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual methods are used to determine and write vehicle features for website listings, then textual information can be customized and coordinated with images, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system enables automated generation of vehicle feature descriptions by having the vehicle data itself (through VIN decoding) serve as the source material. The computer system automatically extracts, maps, and generates feature text without requiring manual intervention, allowing the data to 'write itself' through automated processing pipelines.
Solution Approach 2:
The patent replaces the manual mechanical process of determining and writing vehicle features with an automated computer-based system. The mechanical workflow of human analysts reviewing vehicle specifications and writing descriptions is substituted with electronic data processing, VIN decoding, and automated text generation algorithms.
2Ease of manufacture
If stock images are used on dealership websites, then generic vehicle information can be displayed, but specific vehicle features and angles cannot be shown
Solution Approach 1:
The system uses lot images (copies of actual vehicle photographs) instead of stock images. These images are obtained from third-party photographers who capture specific vehicles on dealership lots, creating accurate visual copies that represent the actual inventory available for purchase.
Solution Approach 2:
The patent introduces an intermediary system that coordinates between images and textual feature descriptions. This intermediary process ensures that the automatically generated feature text corresponds to the vehicles depicted in the lot images, creating a coherent representation of specific vehicles rather than generic stock content.
3Loss of information
If lot images from third-party photographers are used, then specific vehicle images can be displayed, but there is no coordination between images and textual feature information
Solution Approach 1:
The system implements feedback loops where the automatically generated feature descriptions are validated against the source vehicle data and coordinated with the corresponding lot images. This feedback mechanism ensures that the text accurately reflects the vehicles shown in the images by cross-referencing vehicle identification numbers and feature sets.
Solution Approach 2:
The patent creates a universal system that handles both image selection and text generation through a single automated process. The same VIN decoding and feature mapping that generates the textual description also identifies the corresponding vehicle in the image database, ensuring coordinated accuracy without requiring separate manual processes.
4Reliability
If comprehensive textual information is provided for all vehicles, then consumer understanding is enhanced, but the manual effort and resources required are excessive
Solution Approach 1:
The system enables continuous automated generation of comprehensive vehicle feature descriptions for entire dealership inventories. Once the VIN decoding and feature mapping are established, the system can continuously process multiple vehicles without interruption, generating complete feature information for all vehicles in the lot simultaneously rather than sequentially through manual processes.
Solution Approach 2:
The patent performs preliminary VIN decoding and feature extraction in advance, creating a structured database of vehicle features that can be quickly retrieved and presented. By pre-processing the vehicle data and establishing the feature mappings before customer inquiries, the system ensures comprehensive information is ready immediately without requiring manual assembly at the time of display.
Data Source
AI summary
A system for automatically generating textual information describing a vehicle being advertised for purchase is disclosed. The system comprises a processor and a memory communicatively coupled to the processor, the memory including: a vehicle identification number (VIN) decoder logic to decode a VIN to determine a vehicle represented by the VIN, a feature determination logic configured to determine a normalized list of features associated with the vehicle, and a vehicle text generation logic configured to generate textual information describing the vehicle. The memory further includes a feature prioritization logic configured to perform prioritization operations to determine priority values associated with one or more features of the normalized list of features. In some instances, decoding the VIN includes (i) determining attributes of the vehicle including make, model and year, and (ii) retrieving vehicle option codes from a first data store, wherein the vehicle option codes are associated with the VIN.