Trained Process for Dynamic API Selection in Query Responses
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Solution Overview
Problem
Existing systems face challenges in providing optimized responses to queries from diverse devices with varying capabilities and communication conditions, leading to resource wastage and suboptimal user experiences.
Innovation Solution
A computing device employs a trained process to determine an execution plan using selected APIs, optimizing responses based on query and context data, including device and communication factors, to ensure efficient resource utilization and enhanced user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the responding device uses a uniform response strategy for all queries, then the system is simple to implement, but resources are wasted and user experience is not maximized
Solution Approach 1:
The patent implements dynamic API selection by training a machine learning process that adapts API choices based on device capabilities and contextual factors. The system transitions from static uniform responses to dynamic personalized responses, selecting different API combinations optimized for each requesting device's storage, processing power, and network conditions.
Solution Approach 2:
The system changes parameters by using training data to learn optimal API selection strategies under varying conditions. The trained process adjusts which APIs are invoked based on device-specific parameters such as storage capacity, processing power, and communication bandwidth, thereby optimizing resource utilization without sacrificing simplicity.
2Adaptability or versatility
If the responding device selects multiple APIs to answer queries, then the response can be optimized for different device capabilities, but the complexity of determining which APIs to select increases
Solution Approach 1:
The patent applies self-service by training the system to automatically determine optimal API selections without requiring manual configuration or complex real-time decision logic. The trained process autonomously evaluates device capabilities and contextual factors to select appropriate APIs, reducing the operational complexity despite the versatility achieved.
Solution Approach 2:
The system performs preliminary action by pre-training the API selection process using training datasets that include device capabilities and contextual factors. This pre-computed knowledge is stored and reused during query processing, eliminating the need for complex real-time calculations and simplifying the runtime decision-making process while maintaining high adaptability.
3Productivity
If the system trains a process to optimize API selection, then resource utilization improves, but the training process requires additional time and computational resources
Solution Approach 1:
The patent performs the computationally intensive training process in advance, before the system needs to respond to queries. The trained process stores learned patterns for API selection based on device capabilities and contextual factors, allowing rapid inference during query processing without repeating the expensive training computations, thus improving productivity while minimizing time loss.
4Adaptability or versatility
If the system uses a trained process to determine execution plans, then the response is optimized for each device, but the system requires more sophisticated infrastructure
Solution Approach 1:
The system achieves device-specific optimization through self-service mechanisms where the trained process automatically adapts to different device capabilities without requiring manual intervention or complex infrastructure configuration. The trained model autonomously selects appropriate APIs based on device characteristics, reducing the burden on system infrastructure while maintaining high adaptability.
Data Source
AI summary
Systems and methods are provided that use a trained process to reply to a request comprising query data defining a query and context data defining contextual factors for the query from a device. The query is answered by one or more selected APIs of a plurality of APIs that invoke respective services to prepare a response. The trained process determines an execution plan responsive to the query data and the context data and is configured using training to define execution plans comprising selected APIs where a particular API is selected for the plan if it answers at least a portion of the query and the selected APIs together prepare the response optimized for the device according to the context data. The plan is provided to an execution component to execute the plan using the selected APIs and send the response to the requesting device.