Google llc (20240202796). Search with Machine-Learned Model-Generated Queries simplified abstract
Contents
Search with Machine-Learned Model-Generated Queries
Organization Name
Inventor(s)
Alice Au Quan of San Francisco CA (US)
Search with Machine-Learned Model-Generated Queries - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240202796 titled 'Search with Machine-Learned Model-Generated Queries
The abstract of the patent application describes systems and methods for searching using machine-learned model-generated outputs to generate theoretical datasets that can be matched to real-world examples.
- Selecting a plurality of terms to generate a prompt input for a dataset generation model.
- Processing the prompt input to generate a plurality of model-generated datasets.
- Receiving a selection of a particular model-generated database to query a real-world database.
Potential Applications: - Data analysis and research - Information retrieval and matching - Machine learning and artificial intelligence
Problems Solved: - Generating theoretical datasets for comparison - Improving search accuracy and efficiency - Enhancing data processing capabilities
Benefits: - Streamlining data analysis processes - Increasing search result relevance - Facilitating data-driven decision making
Commercial Applications: Title: Enhanced Data Analysis and Search Technology This technology can be used in various industries such as finance, healthcare, and e-commerce for improving data analysis, search capabilities, and decision-making processes. It can also be valuable for research institutions and academic organizations.
Questions about the technology: 1. How does this technology improve the accuracy of search results? 2. What are the potential limitations of using machine-learned model-generated outputs for dataset generation?
Original Abstract Submitted
systems and methods for searching using machine-learned model-generated outputs can provide a user with a medium for generating a theoretical dataset that can then be matched to a real world example. the systems and methods can include selecting a plurality of terms, which can be utilized to generate a prompt input that can be processed by a dataset generation model to generate a plurality of model-generated datasets. a selection can then be received that selects a particular model-generated database to utilize to query a database.
