Third-party Integration Query Response System
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Solution Overview
Problem
Computing systems face challenges in providing accurate query responses specific to a first party computing entity, particularly when integrating third party computing functionality, as conventional methods return generic and non-personalized data that do not account for the unique characteristics of the first party system, leading to delays and increased risks associated with data exposure.
Innovation Solution
A method that involves receiving queries from first party computing systems, identifying relevant third party entities, accessing integration data, determining integration delay predictions specific to the first party system, and taking actions based on these predictions, such as modifying rankings or initiating integration operations, by utilizing reference entities and anonymized tenant computing system integration data to generate tailored timing predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic query response methods are used for third party integration, then system complexity is reduced, but integration timing accuracy deteriorates
Solution Approach 1:
The patent segments the query response system into multiple specialized modules: a query routing module that directs queries to appropriate data sources, a data aggregation module that collects integration data from multiple tenants, and a prediction generation module that creates customized timing predictions. This segmentation allows each module to specialize in specific tasks, improving integration timing accuracy while keeping individual module complexities manageable.
Solution Approach 2:
The patent implements preliminary action by pre-collecting and storing integration data from multiple reference entities before queries are made. The system maintains a database of historical integration data, including integration delays, success rates, and timing information from various tenants. When a query is received, the system can immediately retrieve and analyze this pre-prepared data to generate accurate predictions without performing complex real-time analysis.
2Measurement precision
If first party computing system specific query responses are provided, then integration timing accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent introduces an intermediary integration management system that sits between the query source and the integration data sources. This intermediary system aggregates data from multiple reference entities, processes it through standardized algorithms, and returns customized predictions to the first party computing system. By acting as an intermediary, the system handles the complexity of data collection and analysis centrally, allowing individual systems to receive accurate predictions without implementing complex data processing themselves.
Solution Approach 2:
The patent uses copying by creating virtual representations of integration scenarios based on historical data from reference entities. Instead of requiring each first party computing system to analyze raw integration data independently, the system creates copied and processed versions of this data that contain pre-analyzed insights, patterns, and predictions. These copied datasets are tailored to match the specific characteristics of each querying system, providing accurate predictions with minimal processing complexity.
3Loss of time
If integration delay predictions are provided without customization, then response time is reduced, but prediction accuracy for specific systems deteriorates
Solution Approach 1:
The patent implements universality by designing a multi-functional query response system that can handle multiple types of queries simultaneously using the same infrastructure. The system can provide both quick generic predictions and more detailed customized predictions based on the specific needs and characteristics of each querying system. This universal platform serves multiple functions: data aggregation, pattern recognition, prediction generation, and result customization, all within a single integrated system that optimizes response time while maintaining accuracy.
Data Source
AI summary
A method, in various aspects, comprises: (1) receiving a query from a first party computing system related to integrating third party computing functionality into the first party computing system; (2) identifying a set of third party entities that provide the third party computing functionality; (3) accessing integration data; (4) identifying a set of reference entities, the set of reference entities including, for each respective third party entity, a respective reference entity that has previously integrated the third party computing into a respective reference entity computing system associated with the respective reference entity; (5) determining second integration data with respect to the set of reference entities integrating the third party computing functionality; (6) generating, based on the first integration data and the second integration data, data responsive to the query that is specific to the first party computing system; and (7) taking an action with respect to the data.