IoT Sensor Metadata Appending for Content Recreation
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Solution Overview
Problem
Contemporary electronic devices can only capture images and audio, lacking information about environmental conditions, making it difficult for users to recreate the settings of generated content.
Innovation Solution
A computer-based process determines environmental parameters using IoT sensors and appends this information as metadata to the content, allowing users to recreate the conditions by selecting relevant parameters based on context, such as location and network proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If contemporary electronic devices capture images and audio, then content generation is easy and quick, but environmental condition information is insufficient
Solution Approach 1:
The patent combines multiple data sources (IoT sensors, device sensors, and content metadata) into a unified environmental parameter dataset that accompanies the generated content. This merging allows the system to maintain quick content generation while enriching it with comprehensive environmental information from various sources.
Solution Approach 2:
The system performs preliminary actions by pre-configuring IoT sensors to monitor and record environmental parameters before content generation occurs. This advance preparation ensures that when content is captured, the corresponding environmental data is already available and associated, eliminating the need for post-processing data collection.
2Loss of information
If all device parameters are sent with content, then complete information is provided, but excessive irrelevant information is sent to users
Solution Approach 1:
The patent applies local quality by selectively including only those environmental parameters that are relevant to the specific content type and user context. For example, temperature data is included for food-related content but excluded from unrelated content, optimizing the data transmission by making each dataset locally optimized for its purpose.
Solution Approach 2:
The system uses partial action by retrieving and transmitting only a subset of available sensor data that is deemed relevant to the content. Rather than transmitting all possible parameters, the system selectively includes necessary information, reducing data volume while maintaining completeness for the specific use case.
3Loss of information
If machine learning models determine content context, then relevant parameters are selected, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models to quickly classify content types and predict relevant parameters. This advance training allows the models to make rapid decisions during content generation, reducing real-time processing requirements while maintaining high accuracy in parameter selection.
Solution Approach 2:
The patent applies parameter changes by adjusting the complexity and depth of machine learning analysis based on content type and urgency. For time-sensitive content, simpler parameter selection rules are applied, while for less time-critical content, more comprehensive ML analysis is performed, dynamically changing the processing parameters to balance accuracy and speed.
Data Source
AI summary
Systems and methods for determining parameters of devices that may have influenced generated content, and appending values of these parameters to the generated content for the benefit of other users. Devices near the location at which the content was generated may be selected, and parameters of these devices may be retrieved. These device parameters are often relevant to the generated content. Accordingly, the retrieved parameter values may be appended to the generated content for transmission along with the content. In this manner, other users may view both the content and the parameters of nearby devices that may have influenced the setting of the content, assisting users in, for example, recreating the content or its subject matter for themselves.