Dynamic Context Data Engine for Multi-Application Networks
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Solution Overview
Problem
In multi-application networks, users face inefficiencies in detecting and updating context data, leading to time constraints and reduced productivity due to the need to navigate multiple applications for computing operations, and there is a lack of tools that can automatically recognize data relationships and recommend operations.
Innovation Solution
A data engine is used to generate and update dynamic context data within a multi-application network by processing natural language inputs, extracting metadata, and linking it to computing operations, allowing for the recommendation of computing operations and reducing the need to navigate multiple interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually detect and update context data across multiple applications, then data accuracy may be maintained, but time consumption and productivity are reduced
Solution Approach 1:
The system enables self-service by automatically detecting and updating context data without requiring manual user intervention. The data engine continuously monitors digital request data objects and autonomously generates context data, allowing the system to serve itself rather than relying on users to manually maintain data accuracy across multiple applications.
Solution Approach 2:
The patent replaces the mechanical manual process of detecting and updating context data with an automated data engine that uses computational methods. This substitution eliminates the need for users to manually navigate multiple applications and update context data, thereby maintaining accuracy while significantly improving productivity.
2Adaptability or versatility
If users navigate multiple applications to find computing operations, then comprehensive tool access is achieved, but time constraints and cognitive load increase
Solution Approach 1:
The system implements universality by creating a unified interface that provides access to computing operations across multiple applications. The data engine aggregates and presents relevant computing operations in a single location, allowing users to access comprehensive tools without navigating between different applications, thus reducing time loss while maintaining versatility.
Solution Approach 2:
The data engine acts as an intermediary between users and multiple applications. It receives digital request data objects, processes them through the multi-application network, and returns relevant computing operations and context data. This intermediary layer consolidates access to diverse tools while eliminating the need for users to directly navigate multiple application interfaces.
3Device complexity
If manual context data management is used, then system complexity is reduced, but user training time and operational difficulty increase
Solution Approach 1:
The system performs self-service by automatically generating and maintaining context data without requiring users to understand or manage the underlying complexity. The data engine autonomously processes digital request data objects, extracts metadata, and updates context data, shielding users from system complexity while improving ease of operation.
Solution Approach 2:
The patent replaces manual context data management operations with automated computational processes. The data engine uses algorithms to detect, create, and update context data, substituting complex manual procedures with automated systems that are easier to operate despite their internal complexity.
4Productivity
If automated context data generation is implemented, then productivity is improved, but data engine complexity increases
Solution Approach 1:
The data engine is segmented into distinct functional modules that handle different aspects of context data generation. It separately processes digital request data objects, extracts metadata, determines profile data, and generates context data through specialized components. This segmentation manages the engine's complexity by organizing functions into manageable, independent units that collectively improve productivity.
Solution Approach 2:
The data engine serves as an intermediary layer between the multi-application network and users, absorbing its own internal complexity. It implements sophisticated algorithms for detecting and generating context data, but presents a simplified interface to users. The complexity is contained within the engine's automated processes while maintaining ease of use and improving workflow efficiency.
Data Source
AI summary
Disclosed are methods and apparatuses for generating or updating dynamic context data associated with a digital request data object in a multi-application network. receiving, a first input associated with the digital request data object; determining based on the first input, the digital request data object from the one or more databases associated with the multi-application network; determining profile data indicating one or more of: user data associated with first input, and trajectory data associated with one or more computing operations executed on the digital request data object; extracting metadata associated with the digital request data object; receiving a second input associated with the digital request data object; generating, based on the metadata, the profile data, and the second input, dynamic context data for the digital request data object; and storing the dynamic context data in the one or more databases associated with the multi-application network.