Event Data Classification and Transmission System
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Solution Overview
Problem
Current systems for classifying and transmitting data from events, such as sporting events, fail to recognize the value of movable data collection devices and do not effectively distinguish between high and low value content, leading to unnecessary transmission of undesired data and lack of indication for users about available content.
Innovation Solution
A system and method that utilize a movable data collection device to classify data as high or low value, transmitting only high value portions with indicators, and providing replacement data during low value periods, allowing users to automatically return to high value content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous data stream is transmitted without distinction between desired and undesired content, then all content is available to users, but users receive low value data that is not desired
Solution Approach 1:
The patent segments the continuous data stream into distinct high value and low value content portions by introducing markers that delineate different content types. This allows the system to identify and separate desired content (live action, key events) from undesired content (advertisements, timeouts) without losing either, resolving the contradiction between complete availability and selective transmission.
Solution Approach 2:
The patent introduces markers as intermediary elements that mediate between the data stream and the user. These markers serve as indicators that inform users about the nature of upcoming content, allowing users to make informed decisions about whether to remain engaged with the stream, thus preventing unwanted content from consuming user attention while maintaining full stream availability.
2Ease of operation
If alternate data streams are provided for different content types, then users can select desired content, but users must manually switch between streams
Solution Approach 1:
The patent merges multiple content types (high value and low value content) into a single unified data stream while using markers to distinguish between them. This approach maintains the simplicity of a single stream for users while still providing the functionality of multiple streams through marker-based content identification, eliminating the need for manual switching.
Solution Approach 2:
The patent implements feedback by providing markers that continuously inform users about the type of content they are about to receive. This feedback mechanism allows users to passively select desired content by simply remaining engaged with the stream when markers indicate high value content, eliminating the need for active manual switching between streams.
3Loss of information
If users are notified of high value content portions, then users can return to desired content, but additional processing and transmission overhead is required
Solution Approach 1:
The patent extracts only the essential information needed for content identification (markers indicating high value portions) from the complete data stream, rather than processing or transmitting entire alternate streams. This extraction approach minimizes the processing and transmission overhead while still providing users with the information needed to access high value content effectively.
4Reliability
If all data portions are transmitted to users, then complete event coverage is provided, but bandwidth is wasted on low value portions
Solution Approach 1:
The patent applies partial action by transmitting markers that indicate the presence and timing of high value content portions without continuously transmitting all data portions with the same level of detail. This allows the system to maintain complete event coverage through the unified stream while reducing bandwidth consumption by using concise marker signals rather than full-data transmission for all portions.
Data Source
AI summary
A device and method for collecting data from an event and retransmitting same with high value portions of the data identified. Low value portions of the data from the event are identified so, if desired, they may be removed and replaced by other data, such as stats from the event, stats from other events, and trivia relevant to the event. Indications alert end users that the data signal is returning to high value portions.