AI Feedback Loop for Geolocation-Based Predictive Feature Refinement
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Solution Overview
Problem
Training artificial intelligence engines to accurately predict human behavior is challenging due to insufficient or irrelevant data, especially in large volumes of streaming video from cameras, which contains irrelevant information and lacks geographic relevance, making it difficult to determine predictive features for timely interventions.
Innovation Solution
Establishing a feedback loop that utilizes crowdsourced audiovisual content from a defined group of users to continuously refine predictive features, ensuring relevance through incentives and rewards for uploading topic-specific content, and using predictive features to educate users on capturing relevant data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If streaming video from cameras is used to train AI engines, then large volumes of data are available for analysis, but the data contains irrelevant information and lacks geographic relevance, reducing prediction accuracy
Solution Approach 1:
The patent extracts only the relevant portions of video data by using geographic coordinates to identify and isolate video segments that contain subjects of interest within specific geographic zones. This extraction process removes irrelevant information from the large volume of streaming video, retaining only the data needed for accurate prediction.
Solution Approach 2:
The patent applies local quality by focusing analysis on specific geographic locations rather than treating all video data uniformly. Different geographic zones are identified and analyzed with location-specific criteria, ensuring that each region's unique characteristics are captured while maintaining overall system accuracy.
2Reliability
If continuous streaming video is captured to ensure comprehensive data coverage, then all potential events are recorded, but irrelevant information increases and processing complexity rises
Solution Approach 1:
The patent performs preliminary action by pre-defining geographic zones and coordinates before video analysis begins. This preparation allows the system to immediately filter and focus on relevant areas when video streams are received, avoiding the need to process entire video feeds and reducing computational complexity while maintaining data completeness.
3Loss of information
If historical data is used to train AI engines, then predictive features can be determined, but the data may not be relevant to the specific topic being studied
Solution Approach 1:
The patent changes parameters by using geographic coordinates as a key filtering parameter to select historical video data. Instead of using all available historical data, the system filters data based on geographic relevance, ensuring that only historically significant and location-appropriate data is used for training, thereby improving both completeness and relevance.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for utilizing a feedback loop to continuously improve an artificial intelligence (AI) engine's determination of predictive features associated with a topic. An embodiment operates by training an AI engine for a topic using data from a data source, wherein the topic is associated with a geolocation. The embodiments first receives a set of predictive features for the topic from the trained AI engine. The embodiment transmits the set of predictive features for the topic to a set of electronic devices. The embodiment second receives a set of audiovisual content captured by the set of electronic devices. The set of electronic devices capture the set of audiovisual content based on the set of predictive features for the topic. The embodiment finally retrains the AI engine based on the first set of audiovisual content.