Distributed Cognitive System for Digital Media Value Estimation
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Solution Overview
Problem
Existing solutions are inadequate for proactive analysis of digital media through machine learning, particularly in identifying small, innate objects and for authorization and security processing, and fail to accurately predict values of complex objects within digital media streams.
Innovation Solution
A distributed cognitive system is employed to analyze digital media streams, identifying parameters and generating predicted value estimations by invoking multi-level input collections and communicating reports to users, utilizing cognitive entities for dynamic information collection and machine learning analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a distributed cognitive system with multi-level input collections is used to analyze digital media streams, then the measurement precision of object parameters and value estimation is improved, but the device complexity increases
Solution Approach 1:
The system segments the cognitive analysis process into multiple independent cognitive entities, each responsible for specific tasks such as object detection, parameter identification, or value estimation. Each entity processes specific portions of the digital media stream independently, then results are aggregated to form the final prediction. This segmentation enables improved measurement precision through specialized processing while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces multi-level input collections that add an additional dimension to the data processing architecture. Instead of processing only the base digital media stream, the system collects information at multiple levels (e.g., raw data, processed features, contextual metadata) and feeds these through additional layers of cognitive entities. This dimensional expansion enables more comprehensive analysis and improved prediction accuracy.
2Adaptability or versatility
If multiple cognitive models are connected to create a distributed cognitive system, then the adaptability and versatility of the system is improved, but the device complexity increases
Solution Approach 1:
The distributed cognitive system employs multiple cognitive entities that can be configured to perform various functions depending on the analysis requirements. Each cognitive entity is designed with universal capabilities to process different types of digital media streams (video, audio, text) and can be adapted for different applications such as object detection, scene understanding, or value estimation. This multi-functionality enables high adaptability while the standardized entity design keeps complexity manageable.
Solution Approach 2:
The system dynamically configures and connects cognitive entities based on the specific analysis task and available data. The architecture allows flexible assembly of cognitive entities where the same entity can be instantiated multiple times with different parameters or connected in different configurations. This dynamic adaptability enables the system to respond to varying requirements without redesigning the entire system, thus managing complexity through reconfigurability rather than rigid design.
Data Source
AI summary
A neural network system for generating a value estimation is provided. A computing device analyzes one or more digital media streams. A computing device identifies one or more parameters of the object in one or more digital media streams. A computing device collects information of an object in one or more digital media streams. A computing device generates a precited value estimation of the object via invocation of additional multi-level input collection in a distributed cognitive system. A computing device communicates a report associated with the predicted value estimation of the object to a user of a computing device.