Pedestrian Attribute Analytics for Real-Time Economic Indicators
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Solution Overview
Problem
Current methods for predicting economic activity rely on lagging indicators and are unsatisfactory due to their reliance on intuition, heuristics, and traditional data analysis, which are inadequate for real-time decision-making in volatile markets, and the complexity of data exceeds human processing capabilities.
Innovation Solution
Utilize pedestrian attributes through predictive analytics, machine learning, and deep learning to identify correlations and generate leading indicators of economic activity by analyzing data from various sources, including public and private datasets, and employing supervised and unsupervised learning techniques to refine models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lagging indicators and human analysis methods are used to predict economic activity, then the analysis process is simple and intuitive, but the predictive accuracy and timeliness are insufficient due to the complexity of market forces and vast amounts of disparate data streams
Solution Approach 1:
The patent replaces traditional human analytical methods with automated machine learning models and artificial intelligence systems. These systems process vast amounts of disparate data streams including satellite imagery, social media data, credit card transactions, and other alternative data sources to generate predictive economic indicators, thereby achieving higher predictive accuracy without relying on human capacity to process complex data relationships
Solution Approach 2:
The patent transforms traditional economic indicator generation by changing the data parameters and sources used. Instead of relying on traditional lagging indicators like retail sales reports or industrial production data, the system uses real-time alternative data parameters such as satellite-observed parking lot vehicle counts, social media sentiment analysis, and mobile device location data to create leading indicators that predict economic activity before traditional metrics reflect changes
2Measurement precision
If multiple disparate data streams are collected to improve prediction accuracy, then the predictive capability is enhanced, but the difficulty of processing and analyzing the data increases beyond human capacity
Solution Approach 1:
The patent employs machine learning algorithms and automated data processing systems to handle the complexity of multiple disparate data streams. These systems automatically collect, clean, integrate, and analyze data from diverse sources including satellite imagery, social media platforms, financial transactions, and sensor networks, eliminating the need for human analysts to manually process such vast and varied data quantities
Solution Approach 2:
The patent creates a universal data processing platform that can handle multiple types of disparate data streams through a single integrated system. The machine learning infrastructure is designed to process structured and unstructured data from various sources using common analytical frameworks, enabling the system to simultaneously analyze satellite imagery, social media text, financial transactions, and other data types through unified multi-functional processing capabilities
3Loss of time
If real-time data processing is implemented to provide timely economic insights, then the timeliness of predictions is improved, but the computational resources and processing power required increase significantly
Solution Approach 1:
The patent implements preliminary data processing and model training in advance to reduce real-time computational requirements. Historical data is pre-processed, features are pre-engineered, and machine learning models are trained beforehand on extensive historical datasets. This preliminary action enables the system to generate rapid predictions when new data arrives, as the heavy computational lifting has already been completed in advance rather than in real-time
Solution Approach 2:
The patent divides the data processing workflow into segmented stages that can be processed independently and in parallel. The system segments data collection, cleaning, feature extraction, model inference, and result generation into separate modular components. This segmentation allows different stages to be processed simultaneously using distributed computing resources, reducing overall processing time and enabling real-time predictions without requiring all computations to occur sequentially at full intensity
Data Source
AI summary
Predictive analytics techniques are provided for produce leading indicators of economic activity based on observed pedestrian attributes—e.g., appearance and behavior—and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.