Google llc (20240111572). DYNAMIC HETEROGENEOUS TASK PROCESSING simplified abstract
Contents
- 1 DYNAMIC HETEROGENEOUS TASK PROCESSING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DYNAMIC HETEROGENEOUS TASK PROCESSING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
DYNAMIC HETEROGENEOUS TASK PROCESSING
Organization Name
Inventor(s)
Jamie Menjay Lin of San Diego CA (US)
Chuo-Ling Chang of Mountain View CA (US)
DYNAMIC HETEROGENEOUS TASK PROCESSING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240111572 titled 'DYNAMIC HETEROGENEOUS TASK PROCESSING
Simplified Explanation
The method described in the patent application involves processing a stream of data by receiving a block of data, determining features associated with the block, selecting a task based on the features, and aligning the output of the selected task with the predicted output of another task processing a different block of data.
- The method involves processing data in a sequence of tasks.
- The features of a block of data are used to select the appropriate task for processing.
- If a second task is selected, the output is shifted in time to align with the predicted output of the first task processing a different block of data.
Potential Applications
This technology could be applied in various fields such as:
- Data processing and analysis
- Real-time signal processing
- Predictive modeling
Problems Solved
This technology helps in:
- Efficient processing of streaming data
- Aligning outputs from different tasks for accurate analysis
- Improving prediction accuracy
Benefits
The benefits of this technology include:
- Enhanced data processing capabilities
- Improved task selection based on data features
- Better alignment of task outputs for accurate predictions
Potential Commercial Applications
Potential commercial applications of this technology could include:
- Financial trading algorithms
- Medical data analysis systems
- Industrial process monitoring tools
Possible Prior Art
One possible prior art for this technology could be:
- Time series analysis techniques in data processing
Unanswered Questions
How does this technology handle large volumes of streaming data efficiently?
This technology likely employs optimized algorithms and parallel processing techniques to handle large volumes of streaming data efficiently.
What kind of features are typically used to determine the appropriate task for processing a block of data?
Features such as data complexity, signal strength, and frequency distribution could be used to determine the appropriate task for processing a block of data.
Original Abstract Submitted
a method including processing a stream of data in a sequence of tasks. the processing including receiving a first block of data of the stream of data, determining features associated with the first block of data, selecting, based on the features, one of a first a task to process the first block of data or a second task to process the first block of data and if the second task is selected, shift an output of the second task in time to align the output of the second task with a predicted output of the first task processing a second block of data of the stream of data.
