Operating System Macro Operations for Repetitive Task Automation
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Solution Overview
Problem
Users face challenges with repetitive operations on computers, leading to user fatigue and increased chances of errors due to the onerous nature of performing similar tasks daily, which existing systems do not adequately address.
Innovation Solution
A computer-implemented method that identifies atomic operations within user interactions and calculates correlation indexes to create macro operations, automatically supporting users by performing repetitive tasks, thereby reducing user burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users perform repetitive operations manually, then the system maintains simplicity and direct user control, but user fatigue increases and error rates rise
Solution Approach 1:
The system automatically performs repetitive operations without requiring continuous user intervention. The macro operation framework enables the system to self-execute sequences of atomic operations that have been identified and configured, reducing user fatigue and minimizing errors by eliminating manual repetition.
Solution Approach 2:
The system performs preliminary analysis to identify repetitive operation patterns and pre-configures macro operations before execution. By detecting sequences of atomic operations and calculating correlation indexes in advance, the system prepares automated workflows that can be triggered automatically, preventing user fatigue before it occurs.
2Productivity
If the system automates repetitive operations through macro operations, then user fatigue is reduced and productivity increases, but system complexity increases
Solution Approach 1:
The system segments complex user interactions into atomic operations, which are the smallest indivisible units of interaction. By breaking down user workflows into discrete atomic operations, the system can identify repetitive patterns and create macro operations that automate sequences without requiring the entire system to become complex. Each atomic operation remains simple and well-defined.
Solution Approach 2:
The macro operation framework serves multiple functions: it detects repetitive patterns, stores operation sequences, calculates correlation indexes, and executes automated workflows. This universal framework handles diverse types of repetitive operations across different applications and contexts, reducing the need for separate automation mechanisms for each scenario and thereby limiting overall system complexity.
3Adaptability or versatility
If the system monitors and analyzes user interactions to identify patterns, then automation opportunities are discovered, but processing overhead increases
Solution Approach 1:
The system applies partial monitoring by focusing only on identifying repetitive operation sequences rather than analyzing every aspect of user interactions. It calculates correlation indexes only for sequences that exhibit repetition patterns, not for all possible operation combinations. This selective approach enables pattern recognition while limiting processing overhead to only what is necessary for automation detection.
Data Source
AI summary
A computer-implemented method and computer processing system are provided. The method includes identifying, by a processor, atomic operations within a sequence of data elements and metadata associated with the atomic operations. The sequence of data elements is related to an interaction between a user and a user interface of a computer system that includes the processor. The method also includes calculating, by the processor, correlation indexes from the metadata. A respective correlation index is calculated for various atomic operation groups formed from the atomic operations. The method additionally includes identifying, by the processor, a macro operation from at least one of the correlation indexes. The macro operation includes multiple correlated atomic operations from among the atomic operations identified within the sequence.