Incremental Search Results With Parallel Partial Query Processing
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Solution Overview
Problem
Traditional natural language search tools face latency and inefficiencies when processing vast amounts of data, leading to wasted computational resources and limitations in utilizing cloud service providers due to load constraints, which are not traditionally used for search tasks.
Innovation Solution
A real-time search tool divides search tasks into sub-tasks that can be processed in parallel by cloud service provider resources, enabling partial results to be generated and displayed in real time, allowing users to iteratively refine their queries and halt incomplete searches, thereby optimizing computational resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional natural language search tools process vast amounts of data through large language models, then comprehensive search results are obtained, but latency increases to hours or days and computational resources are wasted
Solution Approach 1:
The patent divides a large search task into multiple smaller sub-tasks that can be processed independently and in parallel. Each sub-task processes a portion of the data through the large language model, allowing results to be generated incrementally rather than waiting for complete processing of all data. This segmentation enables early result delivery while maintaining comprehensive coverage options.
Solution Approach 2:
The system allows users to receive partial results from processed portions of data without requiring complete processing of the entire dataset. Users can halt the search process at any point after receiving sufficient results, avoiding unnecessary computational waste on data that won't be reviewed. This partial action approach balances result completeness with time efficiency.
2Adaptability or versatility
If cloud service providers process large search loads, then scalability and reliability are utilized, but load constraints reject requests exceeding cluster capacity
Solution Approach 1:
By breaking down large search tasks into smaller sub-tasks, the system ensures that each individual task falls within the load capacity limits of cloud service provider clusters. This segmentation allows the system to leverage cloud scalability and reliability without encountering rejection due to exceeding cluster load constraints.
Solution Approach 2:
The patent introduces a temporal dimension to search processing by delivering results incrementally over time rather than requiring complete processing beforehand. This allows the system to utilize cloud resources efficiently across multiple time steps, processing data in manageable batches that fit within cluster capacity while maintaining overall search effectiveness.
3Ease of operation
If data is extracted from data centers for processing, then search capability is enabled, but inefficiencies occur due to unused data transfer capacity and load limitations
Solution Approach 1:
The system performs preliminary processing of data in the data center before extraction, dividing it into pre-processed sub-tasks. This preliminary action reduces the amount of raw data that needs to be transferred to cloud services, optimizing data transfer capacity utilization and reducing energy waste associated with transferring unnecessary data volumes.
Data Source
AI summary
An application receives user input of a search query by way of a search interface. The application determines a task based on the search query, and divides the task into a plurality of sub-tasks, at least some of the plurality of sub-tasks divided for parallel processing by different compute components. The application receives publication of partial results from the different compute components as those partial results are completed by their respective compute components. The application inputs the partial results into a reducer to create an aggregate partial result, and generates for display the aggregate partial result within the search interface, where the aggregate partial result is updated in real time as further partial results are published.