20240020578. METHOD FOR EMBEDDING DATA AND SYSTEM THEREOF simplified abstract (SAMSUNG SDS CO., LTD.)
Contents
METHOD FOR EMBEDDING DATA AND SYSTEM THEREOF
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Inventor(s)
METHOD FOR EMBEDDING DATA AND SYSTEM THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240020578 titled 'METHOD FOR EMBEDDING DATA AND SYSTEM THEREOF
Simplified Explanation
The patent application describes methods and apparatuses for embedding data. The method involves the following steps:
- Acquiring a pretrained embedding model.
- Generating a prompt associated with a data sample using a lighter prompt encoder.
- Generating an embedding representation of the data sample by inputting the prompt and the data sample to the embedding model.
- Calculating a task loss by performing a predefined task using the embedding representation.
- Updating the prompt encoder based on the task loss.
Potential applications of this technology:
- Natural language processing: This technology can be used to embed textual data, enabling applications such as sentiment analysis, text classification, and language translation.
- Image recognition: By embedding image data, this technology can be applied to tasks like object detection, image classification, and image similarity matching.
- Recommender systems: Embedding data can help in building personalized recommendation systems by capturing user preferences and item characteristics.
Problems solved by this technology:
- Efficient embedding: The use of a lighter prompt encoder allows for faster generation of embedding representations, reducing computational resources required.
- Task-specific embedding: The calculated task loss helps in optimizing the embedding representation for specific tasks, improving performance and accuracy.
Benefits of this technology:
- Improved efficiency: The lighter prompt encoder reduces computational overhead, making embedding generation faster and more resource-efficient.
- Enhanced performance: The task loss optimization ensures that the embedding representation is tailored to specific tasks, leading to improved performance and accuracy in various applications.
- Versatility: This technology can be applied to different types of data, including text, images, and potentially other data types, making it versatile for a wide range of applications.
Original Abstract Submitted
methods and apparatuses for embedding data. the method for embedding data includes: acquiring a pretrained embedding model; generating a prompt associated with a data sample through a prompt encoder, the prompt encoder being lighter than the embedding model; generating an embedding representation of the data sample by inputting the prompt and the data sample to the embedding model; calculating a task loss by performing a predefined task by using the embedding representation; and updating the prompt encoder based on the task loss.
