Automated eDiscovery Staging Path for Data Collection
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Solution Overview
Problem
Conventional systems for electronic discovery in business environments are inefficient and resource-intensive due to the need to navigate disparate data sources, making it time-consuming and tedious to collect responsive data, especially for legal or arbitration purposes.
Innovation Solution
The implementation of robotic systems and automation to automatically generate a staging path, capture criteria and encrypted export keys, and export responsive data collections from cloud-based services, compressing the data into forensic containers while tracking progress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional systems manually navigate disparate data sources to collect data, then data collection can be performed, but it is time-consuming and tedious, diverting significant resources from business activity
Solution Approach 1:
The system enables self-service electronic discovery by allowing the eDiscovery platform to automatically collect, process, and manage data from disparate sources without requiring manual intervention. The platform autonomously navigates interfaces, retrieves data, and performs processing tasks that would otherwise require human operators to traverse multiple systems manually.
Solution Approach 2:
Manual mechanical navigation of interfaces is replaced with automated electronic processes. The system uses software bots and automated scripts to traverse data sources, retrieve information, and transfer data between systems, substituting human manual operations with automated computational processes that significantly reduce time and resource requirements.
2Adaptability or versatility
If multiple unique interfaces for different services are used to obtain underlying data, then comprehensive data can be collected, but the process becomes increasingly complex and tedious
Solution Approach 1:
The eDiscovery platform implements a universal interface layer that can interact with multiple different data sources through standardized protocols. Rather than requiring separate manual processes for each service, the platform uses a unified automated approach that can adapt to various data sources including cloud-based SaaS products, on-premises systems, and hybrid environments through common authentication and data retrieval mechanisms.
3Productivity
If manual processes are used for electronic discovery, then data can be collected, but significant resources are diverted from business activity
Solution Approach 1:
The system enables self-service electronic discovery by allowing the eDiscovery platform to automatically collect, process, and manage data from disparate sources without requiring manual intervention. The platform autonomously navigates interfaces, retrieves data, and performs processing tasks that would otherwise require human operators to traverse multiple systems manually.
Data Source
AI summary
Implementations described and claimed herein provide systems and methods for electronic discovery management. In one implementation, a staging path from source(s) to a staging area in a target location is generated automatically in connection with a collection request. Criteria for the collection request is obtained automatically and an encrypted export key for the collection request is captured using first robot(s). An export of a responsive data collection is obtained automatically from the source(s) using the first robot(s). The responsive data collection is exported along the staging path based on the criteria and the encrypted export key. An image of the responsive data collection is generated in the target location by sending parameter(s) to second robot(s), and the collection request is fulfilled by triggering a compression of the image of the responsive data collection into forensic container(s) using the second robot(s).