Autonomous Disparate Asset Tracking for Lifecycle Valuation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a lack of effective systems for monitoring and managing the lifecycle of assets to optimize their replacement, leading to inefficiencies in environmental impact, health risks, and inaccuracies in insurance claims, with existing approaches being rudimentary and susceptible to fraud.
Innovation Solution
A system comprising communications, logic, and interface circuitry that tracks asset usage, generates lifecycle-related valuation scores, and provides feedback on ideal replacement times using AI/ML algorithms, integrating with databases and external data sources for accurate reporting and recommendation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rudimentary tracking approaches are used for asset monitoring, then device complexity is reduced, but measurement precision and reliability of asset lifecycle data deteriorate
Solution Approach 1:
The patent introduces a centralized tracking system that acts as an intermediary between disparate assets and various data sources. This system collects, validates, and standardizes lifecycle data from multiple sources including sensors, databases, and external APIs, thereby improving measurement precision without requiring each individual asset to have complex built-in tracking capabilities.
Solution Approach 2:
The tracking system is designed to handle multiple types of assets (electrical products, appliances, vehicles) and data sources through a universal interface. It can process different data formats, access various databases, and integrate with external systems, providing precise tracking across diverse asset categories without requiring asset-specific complex solutions.
2Reliability
If comprehensive asset monitoring is implemented, then reliability of asset data improves, but device complexity and cost increase
Solution Approach 1:
Assets equipped with tracking capabilities autonomously report their own lifecycle status, usage data, and operational parameters to the centralized system. This self-service approach improves data reliability by eliminating manual reporting errors while avoiding the complexity of active monitoring and verification systems, as assets themselves maintain and transmit their own data.
Solution Approach 2:
The system implements feedback mechanisms where tracked assets receive updates based on their performance data, enabling them to adjust operational parameters or trigger maintenance alerts. This feedback loop improves data reliability by continuously validating asset status against established criteria while maintaining relatively simple system architecture through automated responses.
3Device complexity
If manual asset tracking methods are used, then device complexity is minimized, but loss of information and fraud susceptibility increase
Solution Approach 1:
The patent replaces manual tracking methods with automated electronic systems that digitally record and transmit asset lifecycle data. This substitution eliminates information loss associated with manual processes while keeping device complexity manageable by using standard communication protocols and databases rather than complex custom systems.
Solution Approach 2:
The system creates and maintains digital copies of asset data from multiple sources, including sensor readings, database records, and external references. These copies are stored in a centralized repository that can be queried and validated, preventing information loss and fraud by providing immutable records that can be cross-referenced without requiring complex verification mechanisms.
4Measurement precision
If AI/ML algorithms are integrated for valuation scoring, then measurement precision of lifecycle valuation improves, but device complexity and computational requirements increase
Solution Approach 1:
The system pre-processes and prepares data from multiple sources before it is needed for valuation scoring. Historical data is cleaned, standardized, and organized in advance, allowing AI/ML algorithms to operate on ready-to-process information. This preliminary action improves valuation precision by ensuring high-quality input data while reducing the computational complexity during actual scoring operations.
Solution Approach 2:
The valuation scoring process is divided into separate modules: data collection, data processing, algorithm execution, and result generation. Each segment can be independently optimized and scaled, allowing precise AI/ML valuation without requiring the entire system to be complex. The segmentation enables selective application of computational resources only where needed for algorithm processing.
Data Source
AI summary
One exemplary aspect concerns a system that includes a communications circuit, logic circuitry and an interface circuit working together to aggregate and provide feedback on indications of ongoing use of various assets such as in use in and around a residence. For each asset, the communications circuit and logic circuitry are used to receive and aggregate ongoing-use indications, access a profile indicating an asset-related lifecycle, and in response execute an algorithm to adjust a valuation score of the asset. The interface circuit may report on the ongoing use of said at least one of the plurality of disparate assets. In a more specific example, the valuation score is associated with one or more environmental-related components that appreciate and/or depreciate with ongoing use of the asset over portions of the lifecycle, and the assets may be residence-related such as including solar panels, IoTs, battery-assisted vehicles, and HVAC appliances.