AI Image Analysis for Asset Allocation and Data Routing
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Solution Overview
Problem
Existing data routing systems face challenges in securely, accurately, and efficiently routing data packets associated with assets to different computing entities with varying formats and protocols, while maintaining stateful awareness and compliance with time-sensitive information, especially in asset disposition workflows involving properties.
Innovation Solution
An asset allocation system using a trained vision language model to identify impediments in electronic images, generate text outputs, and allocate assets to disposition channels based on evaluation data, integrating with large language models for text generation and API connectivity to manage tasks and comply with service-level agreements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of electronic images is used to identify asset deficiencies, then accuracy can be maintained, but time consumption and processing speed deteriorate
Solution Approach 1:
The patent replaces manual mechanical evaluation of electronic images with an automated machine learning model that processes images to identify asset deficiencies. The model generates text outputs describing impediments and allocates assets to disposition channels automatically, eliminating the need for human reviewers while maintaining identification accuracy and significantly reducing processing time.
2Adaptability or versatility
If multiple computing entities with varying formats and protocols are integrated, then system versatility improves, but system complexity and routing difficulty worsen
Solution Approach 1:
The patent implements a universal asset allocation system that can interface with multiple different computing entities through standardized protocols. The machine learning model serves as a universal processor that handles various asset types and disposition channels, while the system automatically adapts to different partner system formats and requirements, reducing the complexity of integrating diverse systems.
Solution Approach 2:
The patent introduces an intermediary layer between the asset evaluation system and partner computing entities. This intermediary handles format conversion, protocol adaptation, and data standardization, allowing the core machine learning model to remain simple while managing the complexity of multiple integrated systems through a unified interface.
3Measurement precision
If comprehensive evaluation data is collected from multiple sources, then allocation accuracy improves, but data processing complexity and time requirements worsen
Solution Approach 1:
The patent merges multiple data sources including electronic images, asset attributes, and evaluation criteria into a unified processing framework. The machine learning model consolidates these diverse inputs and generates comprehensive evaluation data that feeds into the allocation determination, simplifying the handling of multi-source data while maintaining high allocation accuracy.
Solution Approach 2:
The patent performs preliminary processing of evaluation data before final allocation determination. The machine learning model pre-processes images and generates impedance descriptions in advance, organizing data in a standardized format that simplifies subsequent allocation decisions and reduces the complexity of real-time processing.
4Productivity
If automated routing of data packets is implemented, then processing speed improves, but routing accuracy and compliance reliability worsen
Solution Approach 1:
The patent implements feedback mechanisms in the automated routing system where the machine learning model continuously evaluates routing decisions and adjusts based on outcomes. The system monitors allocation accuracy and compliance metrics, using this feedback to improve routing precision while maintaining high processing speeds through automated decision-making.
Data Source
AI summary
Disclosed herein are methods and systems that use artificial intelligence techniques for determining an allocation of an asset based, at least in part, on analyzing electronic images of the asset. In an embodiment, an AI model can be executed on a received data packet comprising a set of electronic images to generate a set of attributes. In an embodiment, the AI model can identify a segment of the asset associated with the electronic image, generate an attribute comprising a description of quality of the segment of the asset, and determine whether the electronic image is associated a subset of the electronic images. In an embodiment, the set of attributes and an indication of the subset with user input about the asset can be aggregated to generate evaluation data. In an embodiment, a computer model can be executed to determine an allocation of the asset to a class.