GPU-Based Query Generation for Streaming Content Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional streaming content is static and requires significant computational resources to generate user-specific customized content, making it time-consuming and inefficient, especially for large content libraries.
Innovation Solution
The use of learning engines, such as large language models (LLMs), to generate sophisticated user-specific customized content in real-time, combined with text-to-speech and text-to-video technologies, to create dynamic and personalized content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional streaming content is used, then content delivery is simple and stable, but content is static and cannot be customized to individual users
Solution Approach 1:
The patent introduces an intermediary system comprising a learning engine, graphics processing unit (GPU), and query generation module that mediates between the static content library and the user's personalized viewing preferences. This intermediary dynamically generates customized content recommendations and visual representations without requiring fundamental changes to the core streaming infrastructure, thus achieving adaptability while managing complexity through a dedicated intermediate layer.
Solution Approach 2:
The system segments the content delivery function into separate modular components: a content library, a learning engine for preference analysis, a GPU-based visual generation module, and a query processing system. This segmentation allows each component to specialize in specific tasks, enabling content customization through coordinated interaction of independent modules rather than requiring a monolithic complex system.
2Adaptability or versatility
If user-specific customized content is generated using conventional techniques, then content can be tailored to individual users, but it requires enormous computer resources and is very time consuming
Solution Approach 1:
The patent changes the computational parameters by utilizing graphics processing units (GPUs) instead of conventional CPUs for content generation tasks. GPUs are specifically optimized for parallel processing and can rapidly generate visual representations and process graphical data, significantly reducing the computational time and resource requirements for creating customized content recommendations while maintaining high adaptability to user preferences.
Solution Approach 2:
The learning engine performs preliminary analysis of user preferences and content characteristics in advance, generating queries and visual representations before the actual content delivery. This preliminary action pre-processes the customization logic and visual assets, reducing the computational burden during real-time content selection and delivery operations.
3Measurement precision
If sophisticated content recommendations are provided in real-time for large content libraries, then content recommendations become more accurate and relevant, but the process takes an inordinate amount of time and requires enormous computer resources
Solution Approach 1:
The system changes the processing parameter by employing graphics processing units (GPUs) which are designed for high-speed parallel computation. The GPU accelerates the generation of visual representations and the processing of graphical data related to content items, enabling sophisticated recommendation algorithms to execute much faster than on conventional processors, thus achieving high recommendation accuracy within acceptable time frames even for large content libraries.
Solution Approach 2:
The system performs preliminary generation of visual representations and query formulations in advance based on user profile data and content metadata. This pre-computation of visual assets and query structures reduces the processing time required during real-time recommendation generation, as the heavy computational tasks are completed beforehand when data is more readily available.
4Adaptability or versatility
If navigation interfaces require navigation through many screens to find desired content, then comprehensive content search is possible, but the navigation is non-intuitive and compute-intensive
Solution Approach 1:
The patent creates visual copies or representations of content items in an enhanced graphical user interface that directly displays customized content recommendations with visual representations. This copying approach allows users to access comprehensive content search capabilities through intuitive visual displays rather than navigating through multiple hierarchical screens, as the visual representations provide immediate contextual information and direct access to desired content.
Solution Approach 2:
The system transitions from traditional multi-screen text-based navigation to a two-dimensional graphical interface that presents content recommendations visually. By organizing content information in a graphical dimension with visual representations, the system maintains comprehensive search capability while dramatically improving navigation intuitiveness, as users can visually scan and interact with content in a single interface rather than progressing through sequential screens.
Data Source
AI summary
An aspect relates to determining if a request received from a user device is sufficiently similar to a cached request, wherein the request requesting an identification of streamable content meeting one or more criteria. The determination comprises comparing a vector corresponding to the received request with vectors of previously received, cached requests. If the vector corresponding to the received request is sufficiently similar to a first vector of a first previously received, cached request, then a previously generated response corresponding to the first previously received, cached request may be accessed and transmitted to the user device. If a sufficiently similar vector is not identified, then at least a portion of the received request may be transmitted, in association with an identification of content items in a library, to an artificial intelligence learning engine. The response from the artificial intelligence learning engine may be transmitted to the user device.