Snap inc. (20240354641). RECOMMENDING CONTENT USING MULTIMODAL MEMORY EMBEDDINGS simplified abstract
Contents
RECOMMENDING CONTENT USING MULTIMODAL MEMORY EMBEDDINGS
Organization Name
Inventor(s)
William Miles Miller of San Francisco CA (US)
Aleksei Stoliar of Marina del Rey CA (US)
RECOMMENDING CONTENT USING MULTIMODAL MEMORY EMBEDDINGS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240354641 titled 'RECOMMENDING CONTENT USING MULTIMODAL MEMORY EMBEDDINGS
- Simplified Explanation:**
The patent application describes a system that collects interaction data from a user using various functions, processes this data using machine learning models, and generates recommended content for the user based on this data.
- Key Features and Innovation:**
- System gathers interaction data from user activities.
- Utilizes machine learning models to create a multimodal memory of the data.
- Identifies prompts for the user and generates recommended content based on this and the multimodal memory.
- Applies recommended content to the user's interaction client.
- Potential Applications:**
This technology could be used in personalized content recommendations, user experience enhancement, and targeted advertising.
- Problems Solved:**
The system addresses the challenge of providing relevant and personalized content to users based on their interactions and preferences.
- Benefits:**
- Improved user engagement and satisfaction.
- Enhanced user experience through personalized recommendations.
- Efficient content delivery based on user behavior.
- Commercial Applications:**
This technology could be applied in digital marketing, e-commerce platforms, social media networks, and content streaming services to enhance user engagement and drive conversions.
- Questions about the Technology:**
1. How does the system differentiate between different modalities of interaction data? 2. What are the potential privacy concerns associated with collecting and processing user data in this manner?
- Frequently Updated Research:**
Ongoing research in machine learning algorithms and natural language processing could further enhance the capabilities of this system in generating personalized content recommendations for users.
Original Abstract Submitted
described is a system for gathering interaction data from use of one or more interaction functions by a first user, wherein the interaction data includes data in different modalities and generating a multimodal memory for the interaction data by applying the interaction data to a first machine learning model. the system also identifies a prompt for the first user and processes a combination of data associated with the prompt and the multimodal memory using a second machine learning model to generate recommended content for the first user. the system then proceeds to apply the recommended content to a first interaction client of the first user.
