Context-Aware Digital Command Registration Across Applications
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Solution Overview
Problem
Existing systems struggle to efficiently integrate and leverage data from multiple applications within a multi-application network, necessitating improved methods for registering, activating, and deactivating commands based on user-specific and contextual data to ensure data isolation and access to relevant information.
Innovation Solution
A digital assistant powered by artificial intelligence is implemented to analyze user inputs, recognize relationships among disparate data, and curate operations within a multi-application network, using semantic and syntactic data analysis to execute commands and manage exception events, while controlling access to applications based on user and device profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data from multiple applications is integrated and leveraged, then data accessibility and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent introduces a digital assistant as an intermediary layer between users and multiple applications. The digital assistant receives natural language inputs, analyzes them using semantic and syntactic processing, and translates them into appropriate commands for the target applications. This mediator handles the complexity of integrating multiple applications, allowing users to access data across applications without directly managing the underlying system complexity.
2Ease of operation
If commands are customized based on user-specific and contextual data, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing user inputs before executing commands. The digital assistant analyzes the natural language input, determines the user's intent, identifies relevant contextual data, and prepares the appropriate command structure in advance. This preliminary analysis phase separates the complexity of command customization from the execution phase, making the system easier to operate while managing complexity through structured preprocessing.
3Measurement precision
If semantic and syntactic analysis is performed on user inputs, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The digital assistant applies partial analysis based on the context and requirements of each input. Rather than performing complete semantic and syntactic analysis on every input uniformly, the system adjusts the depth of analysis based on factors such as input clarity, user profile, and contextual information. This selective approach maintains high measurement precision for critical inputs while reducing processing time for routine or clear commands, balancing accuracy with efficiency.
Data Source
AI summary
This disclosure relates to methods for generating one or more digital records associated with a multi-application network. The methods include: receiving a first input associated with a digital data object; determining, based on the first input, context data associated with the digital data object; executing a first computing operation and a second computing operation; generating a first digital record associated with at least one of the first computing operation or the second computing operation; updating the context data, thereby generating updated context data; receiving a second input associated with the digital data object; executing a third computing operation and a fourth computing operation; generating a second digital record associated with at least one of the third computing operation or the fourth computing operation; and updating the context data or the updated context data.