Image-Based Data Record Matching for Cross-Device Synchronization
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Solution Overview
Problem
Providing synchronized information efficiently across computing systems with diverse devices via computer networks is challenging.
Innovation Solution
A system and method that utilize image data processing, including machine-learning models like optical character recognition and object detection, to generate values from pixel data, filter and search for relevant content items, and update graphical user interfaces for synchronized information presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional information sharing protocols are used across heterogeneous computing devices, then compatibility is maintained, but synchronization efficiency deteriorates
Solution Approach 1:
The patent introduces an intermediary translation layer that converts between different device formats and a standardized internal representation. The system captures image data from diverse peripherals (cameras, sensors), processes it through a unified machine learning pipeline, and generates standardized data structures that can be efficiently synchronized across the network, thus maintaining compatibility while improving synchronization efficiency.
Solution Approach 2:
The system dynamically adjusts processing parameters based on device capabilities and data types. By modifying image processing parameters, machine learning model selection, and data structure formats according to the specific peripheral device and computing system characteristics, the system achieves both high synchronization efficiency and broad device compatibility.
2Loss of time
If image data from capture devices is processed in real-time, then information freshness is improved, but processing complexity increases
Solution Approach 1:
The patent divides the image processing pipeline into distinct modular stages: image capture from peripherals, pre-processing and normalization, machine learning inference, data structure generation, and network transmission. Each stage can be independently optimized and executed, enabling real-time processing while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by pre-processing image data (normalization, feature extraction) and pre-loading appropriate machine learning models before actual analysis is needed. This preparation work reduces the computational burden during real-time operation, maintaining information freshness while controlling processing complexity.
3Measurement precision
If machine learning models are used to process image data, then information accuracy is improved, but computational resources consumed increase
Solution Approach 1:
The patent applies partial processing by selecting and executing only the necessary machine learning models based on the specific data capture scenario. Rather than running all available models on every image, the system chooses the minimal set required for accurate interpretation, thus maintaining information accuracy while reducing computational resource consumption.
Data Source
AI summary
Systems and methods for generating network data structures from local peripheral signals are disclosed. A system can receive image data from a capture device, where the image data includes a plurality of pixels. The system can determine that the plurality of pixels depict a visual representation of a first data record satisfying a predetermined criterion, the first data record corresponding to a set of data record parameters. The system can generate a set of data from the first data record using a subset of the plurality of pixels of the image data, and can transmit the set of data to at least one server, causing the server to identify a second data record based on the set of data record parameters. The system can present, via a graphical user interface, a content item representative of the second data record received from the server.