Shared Execution Caching for Speech Context Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current speech processing systems face inefficiencies in handling redundant requests for contextual data, leading to increased computational load and latency, as they often generate multiple executable processes to retrieve the same data, which can be costly in terms of resources and user experience.
Innovation Solution
Implementing a shared execution caching architecture that routes contextual data requests with the same partition key to the same host, allowing for the caching of retrieved data and reducing the need for redundant processes, while also using prefetching logic to anticipate and cache likely future requests based on historical probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple executable processes are generated to retrieve contextual data, then the system can handle different requests, but the computational load and resource consumption increase
Solution Approach 1:
The patent merges multiple redundant requests for the same contextual data into a single executable process by identifying common partition keys. When multiple requests target the same host (determined by partition key), the system consolidates them into one process that retrieves the data once, then serves all requesting components, thereby reducing computational load while maintaining request handling capability.
Solution Approach 2:
The patent creates a universal caching mechanism that serves multiple functions: it stores retrieved contextual data and provides it to any component that needs it. This single cache structure replaces multiple separate retrieval operations, allowing the system to handle diverse requests efficiently through a unified data access point.
2Adaptability or versatility
If multiple executable processes are generated to retrieve contextual data, then different requests can be processed, but the latency increases
Solution Approach 1:
The patent implements prefetching logic that anticipates future requests for contextual data and retrieves the data in advance, caching it before it is actually needed. This preliminary action eliminates waiting time when requests arrive, as the data is already available in the cache, thereby reducing latency while maintaining the ability to handle multiple request types.
Solution Approach 2:
By consolidating multiple requests into a single executable process that retrieves data once and serves all consumers, the system eliminates redundant retrieval operations. This merging reduces the total time required to service multiple requests compared to handling them separately, thereby reducing latency while preserving request processing capability.
3Reliability
If redundant retrieval processes are executed, then data can be obtained, but the resource usage increases
Solution Approach 1:
The patent creates a cached copy of contextual data after the first retrieval. Subsequent requests receive this cached copy instead of triggering new retrieval processes. This copying mechanism ensures data availability (reliability) while avoiding the resource consumption of repeated retrieval operations, as the cache serves as a lightweight replica of the original data.
Solution Approach 2:
The system merges multiple retrieval requests into a single execution process by identifying that they target the same host via partition key. This consolidation ensures that data is retrieved reliably once and then distributed to all requesting components, eliminating redundant resource usage while maintaining data retrieval reliability.
4Loss of energy
If a shared execution caching architecture is implemented, then resource usage is optimized, but the system complexity increases
Solution Approach 1:
The patent introduces a cache as an intermediary layer between the contextual data storage and the requesting components. This intermediary receives requests, checks for cached data, and either serves from cache or retrieves and caches the data. While this adds a component, it simplifies the overall system by providing a standardized interface that handles all retrieval logic centrally, making the architecture more manageable despite the added layer.
Solution Approach 2:
The caching system is designed to be self-managing, automatically determining whether data exists in cache or needs to be retrieved, and handling the entire process without external intervention. This self-service capability reduces the operational complexity of managing the cache, as the system autonomously optimizes resource usage through intelligent request routing and data caching decisions.
Data Source
AI summary
Devices and techniques are generally described for shared execution caching of contextual data. A first request to retrieve a first value from a first field of a database may be received. First entity identifier data associated with the first request may be determined. A second request to retrieve the first value from the first field may be received, the second request being associated with second entity identifier data. A determination may be made that a first host device is associated with the first entity identifier data and the second entity identifier data. In some examples, a first executable instruction effective to retrieve the first value from the first field of the database when executed by the first host device may be generated. In some examples, the first value may be retrieved from the first field of the database and sent to the first speech processing component.