Adaptive Object Identification via Dynamic Attribute Sampling
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Solution Overview
Problem
Traditional object identification techniques in web applications face challenges due to dynamic attributes changing over time, making it difficult for automated tools to reliably replay scripts and identify objects, especially when manual intervention is required for updating object descriptions.
Innovation Solution
Adaptive object identification mechanisms that employ multiple sampling of attributes at 'interesting' points in an object's lifetime, using importance scores to distinguish static from dynamic attributes and create a robust object description, which can be refined during script replay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object identification techniques are used with single observation, then the identification process is simple, but the reliability of object identification deteriorates when attributes change over time
Solution Approach 1:
The patent applies dynamics by making the object description adaptive rather than static. The identification mechanism continuously updates the object description based on multiple observations over time, allowing it to adapt to changing attributes. This dynamic approach resolves the contradiction by maintaining high reliability through continuous adaptation while managing complexity through automated updating processes.
Solution Approach 2:
The patent employs preliminary action by performing multiple observations and attribute sampling before final object identification is needed. By collecting and analyzing attribute data in advance across multiple time points, the system builds a robust object description that can withstand attribute changes, thereby improving reliability without requiring complex real-time analysis.
2Productivity
If manual intervention is used to update object descriptions, then the identification accuracy can be maintained, but the productivity and efficiency deteriorate
Solution Approach 1:
The patent implements self-service by enabling the identification mechanism to automatically update object descriptions without manual intervention. The system autonomously performs multiple observations, analyzes attribute changes, and adjusts the object description accordingly. This self-updating capability maintains identification accuracy while dramatically improving productivity by eliminating manual updating requirements.
Solution Approach 2:
The patent uses feedback mechanisms where the system continuously monitors attribute changes and uses this information to refine object descriptions. By incorporating feedback from multiple observations and attribute sampling, the system automatically adjusts its identification criteria to maintain accuracy while operating autonomously, thus resolving the contradiction between productivity and precision.
3Reliability
If multiple sampling of attributes is performed to distinguish static from dynamic attributes, then the object description robustness improves, but the time and resources required increase
Solution Approach 1:
The patent applies partial action by performing multiple observations but not necessarily sampling all attributes at every observation point. Instead, it strategically samples attributes at key moments and uses importance weighting to focus on the most critical attributes. This approach builds robust object descriptions while minimizing the time and resources required compared to exhaustive sampling of all attributes at all times.
Data Source
AI summary
A adaptive object identification mechanism provides an object description of an object of an application that is executed by a processor, where the object description is based on attributes associated with the object. When an operation on the object is detected, the attributes of the object are sampled at the time of the operation and compared with the object description to assess whether the attributes have changed. The object description is then adjusted based on the assessment.