Digital Assistant Interface for Multi-Application Command Orchestration
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Solution Overview
Problem
Existing systems struggle to efficiently integrate and leverage data across multiple applications within a multi-application network, leading to inefficiencies in process optimizations, data cataloging, exception event handling, and data storage, particularly in managing digital commands and user-specific data.
Innovation Solution
A digital assistant is implemented within a multi-application network to analyze user inputs, recognize data relationships, and recommend operations, using a data engine to encapsulate commands, manage exception events, and facilitate communication between native and non-native applications through a unified interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data from multiple applications is integrated and leveraged, then process optimizations and data tracking efficiency are improved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent introduces a digital assistant as an intermediary component that mediates between multiple applications and the user. This digital assistant receives natural language inputs, analyzes them using semantic and syntactic processing, and executes appropriate commands across different applications. By centralizing the interaction logic in this intermediary layer, the system achieves efficient multi-application coordination without requiring complex direct integrations between all application pairs, thus resolving the contradiction between productivity improvement and system complexity.
2Productivity
If computational tools are developed to register and activate commands dynamically, then command execution efficiency is improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent implements dynamic command registration and activation mechanisms where the digital assistant can register new commands, update existing commands, and deactivate commands based on runtime conditions. The system maintains a dynamic command registry that allows commands to be added, modified, or removed without system reconfiguration. This dynamic approach enables efficient command execution by having the digital assistant interpret natural language inputs and map them to appropriate registered commands, while avoiding the need for static, pre-configured command structures that would reduce flexibility and execution efficiency.
3Ease of operation
If natural language processing is implemented for user inputs, then user interface ease of use is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and analyzing natural language inputs to extract semantic and syntactic features before command execution. The digital assistant performs semantic analysis, syntax parsing, and intent recognition in advance, creating structured representations of user inputs that can be quickly matched against registered commands. This preliminary processing reduces the computational burden during actual command execution and minimizes perceived processing time for users, as the heavy lifting of natural language understanding is completed before the command needs to be executed.
Data Source
AI summary
Disclosed are methods and apparatuses for implementing a digital assistant computing operation in a multi-application network. The methods include: receiving an input command from a computing device; analyzing the input command; determining based on the analysis of the input command, a digital request data object associated with the input command; determining, based on the digital request data object, intent data, and generating based on the intent data, a first set of operation recommendations comprising a first operation recommendation. The methods also include: determining a first application function associated with the first operation recommendation; determining or accessing an application programming interface (API) associated with the first application function; connecting the API to the first application function; executing the first operation recommendation to generate a computing operation result; and rendering the computing operation result on a first graphical interface.


