Salesforce, inc. (20240201958). IDENTIFYING METHOD FOOTPRINTS USING VECTOR EMBEDDINGS simplified abstract
Contents
IDENTIFYING METHOD FOOTPRINTS USING VECTOR EMBEDDINGS
Organization Name
Inventor(s)
Ajay Krishna Borra of San Francisco CA (US)
Ravi Sankar Pulle of San Francisco CA (US)
IDENTIFYING METHOD FOOTPRINTS USING VECTOR EMBEDDINGS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240201958 titled 'IDENTIFYING METHOD FOOTPRINTS USING VECTOR EMBEDDINGS
Simplified Explanation:
The patent application describes a system that collects profiling data related to computational resource consumption of code implementations, converts this data into vector spaces using vector embedding translation, and generates a model representation of resource consumption. Real-time profiling data can be compared to the model representation to identify deviations.
Key Features and Innovation:
- Collection of profiling data on computational resource consumption
- Conversion of data into vector spaces using vector embedding translation
- Generation of a model representation of resource consumption
- Comparison of real-time profiling data to the model representation
Potential Applications: This technology could be applied in software development, performance optimization, and resource management in various industries.
Problems Solved: This technology addresses the need for efficient monitoring and analysis of computational resource consumption in code implementations.
Benefits:
- Improved understanding of resource consumption patterns
- Early detection of deviations in resource consumption
- Enhanced performance optimization in software development
Commercial Applications: The technology could be utilized in cloud computing, IoT devices, and large-scale software systems to optimize resource usage and improve overall performance.
Prior Art: Readers interested in prior art related to this technology could explore research papers on code profiling, resource optimization, and machine learning in software development.
Frequently Updated Research: Stay updated on advancements in code profiling techniques, resource optimization algorithms, and machine learning models for software performance analysis.
Questions about Computational Resource Consumption: 1. How does the system convert profiling data into vector spaces? 2. What are the potential real-time applications of this technology in software development?
Original Abstract Submitted
methods, systems, apparatuses, devices, and computer program products are described. a system may collect a first set of profiling data associated with computational resource consumption of one or more code implementations or methods. the system may use a vector embedding translation to convert the profiling data into one or more vector spaces. each vector space may include a set of vectors, and each vector may correspond to an execution of a code implementation or method. the system may use the vector spaces to generate a model representation of the computational resource consumption of the one or more code implementations. in some cases, the system may collect and convert a second set of real-time profiling data into vector spaces, which the system may compare to the model representation such that users may identify deviations from resource consumption footprints.
