Relevancy Matching Server for Networked Device Data
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Solution Overview
Problem
Users face challenges in configuring networked devices to share information effectively, leading to irrelevant content presentation and missed revenue opportunities due to the complexity of configuration protocols and the inability to automatically establish bidirectional communication between devices.
Innovation Solution
A system that includes a client device associated with multiple networked devices through a computer network, processing embedded objects within a security sandbox, and utilizing a relevancy-matching server to receive and match primary data from networked devices, ensuring targeted and relevant content is rendered to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration protocols are implemented for networked devices to share information, then information sharing capability is improved, but user operation complexity and time consumption increase significantly
Solution Approach 1:
The system enables devices to automatically discover and share information without manual user configuration. The networked device autonomously transmits information to the client device, eliminating the need for users to read manuals or understand configuration protocols, while still achieving reliable information sharing.
Solution Approach 2:
The patent introduces an information sharing mechanism that acts as an intermediary between networked devices and client devices. This mediator facilitates automatic information transmission and matching, resolving the complexity of direct device-to-device configuration while ensuring reliable information exchange.
2Device complexity
If generic content is presented to all users, then system simplicity is maintained, but content relevance and user engagement decrease
Solution Approach 1:
The system applies local quality by tailoring content presentation to individual users based on information gathered from their networked devices. Instead of uniform generic content, each user receives customized content matching their specific context, behaviors, and device data, thereby improving relevance without significantly increasing overall system complexity.
Solution Approach 2:
The patent utilizes parameter changes by adjusting content delivery based on varying user parameters extracted from networked device information. The system dynamically modifies content relevance parameters according to user-specific data such as viewing habits, device type, and interaction patterns, transforming generic content into personalized experiences.
3Productivity
If automatic bidirectional communication is implemented between devices, then content relevance and user experience improve, but system complexity and security requirements increase
Solution Approach 1:
The system segments the communication architecture into distinct functional components: information gathering from networked devices, data transmission, information matching, and content delivery. This segmentation allows automatic bidirectional communication to achieve high content relevance while managing complexity through modular design, where each component handles specific tasks independently.
Data Source
AI summary
A system includes a client device capable of being associated with a number of networked devices through a computer network to: process an embedded object, constrain an executable environment in a security sandbox, and execute a sandboxed application in the executable environment. The embedded object is processed through the sandboxed application. The system also includes a relevancy-matching server to: receive primary data generated from fingerprint data of each of the number of networked devices, match the primary data with targeted data based on a relevancy factor, search a storage for the targeted data, and cause rendering of the targeted data through the embedded object processed through the sandboxed application of the client device. The primary data is any one of a content identification data and a content identification history.