AI Prediction System for Resource Allocation
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Solution Overview
Problem
Existing technologies struggle to accurately allocate customers across resources due to inefficiencies in algorithms for estimating current or predicted conditions, leading to inaccurate results and resource wastage.
Innovation Solution
An AI prediction system that converts images of contained spaces into numerical counts of objects of interest, using a time-series-based prediction model to forecast future object counts and recommend optimal resource allocation based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If appointment booking systems are used to allocate customers, then customer connection reliability is improved, but system complexity and user flexibility are worsened
Solution Approach 1:
The patent replaces traditional manual appointment booking systems with an AI-driven automated allocation system that uses computer vision, time-series prediction models, and natural language processing to automatically analyze conditions and allocate customers, eliminating the need for complex manual scheduling while maintaining reliability
Solution Approach 2:
The system enables self-service allocation where the AI autonomously monitors conditions, predicts future states, and assigns customers to resources without requiring users to manually book appointments, simplifying the interaction while improving connection reliability
2Productivity
If traditional algorithms are used for estimating customer allocation, then device complexity is reduced, but measurement precision and productivity are worsened
Solution Approach 1:
The patent divides the allocation system into distinct modular components: image capture modules, object detection modules, time-series prediction models, natural language processing modules, and allocation decision modules. This segmentation enables each component to specialize in specific tasks, improving overall measurement precision and productivity while managing complexity through modular architecture
Solution Approach 2:
The system transitions from traditional single-dimensional rule-based algorithms to multi-dimensional analysis by incorporating visual data from images, temporal patterns from time-series data, and contextual information from natural language processing, significantly enhancing measurement precision through holistic condition assessment
3Measurement precision
If real-time monitoring of contained spaces is implemented, then measurement precision is improved, but use of energy and device complexity are worsened
Solution Approach 1:
The system performs preliminary actions by capturing images and detecting objects in advance, then uses time-series prediction models to forecast future conditions. This allows the system to process data at lower intensity intervals rather than requiring continuous high-power computation, reducing energy consumption while maintaining measurement precision
Solution Approach 2:
The system creates digital copies of physical spaces through image capture and object detection, then analyzes these copies using prediction models. This eliminates the need for continuous physical monitoring and reduces computational energy requirements compared to real-time sensor-based tracking while preserving measurement accuracy
Data Source
AI summary
Systems and methods to convert one or more objects of interest from a respective image of each of at least two contained spaces into respective one or more numerical counts indicative of a number of the one or more objects of interest in the at least two contained spaces within a period of time. A prediction is generated of a future number of the one or more objects of interest in the at least two contained spaces within a future period of time after the period of time, and a recommendation is recommended to a prospective user one of the at least two contained spaces for the prospective user to select as a destination within the future period of time based on the prediction of the future number of one or more objects of interest in each of the at least two contained spaces.