Dynamic AI Verification Workflows for Risk-Based Object Inspection
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Solution Overview
Problem
Conventional workflow management systems lack adaptability, requiring technical expertise for updates, failing to tailor user experiences, and lacking integrated AI capabilities for physical object verification, leading to inefficiencies and increased costs.
Innovation Solution
A system and method for remotely configuring and deploying AI-guided, multi-step data capture workflows that allow non-technical administrators to define and execute workflows dynamically, supporting on-device AI models and risk-based adaptation, enabling real-time user interface rendering without application updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workflows are hardcoded within the application binary, then the application structure is stable and reliable, but any updates to workflow configurations require full rebuild, quality assurance cycle, app store resubmission, and end-user app updates, resulting in high latency and slow realization of changes
Solution Approach 1:
The patent separates workflow configurations from the application binary by implementing a remote workflow management system. Workflows are segmented into independent, remotely-updatable configuration files that can be modified without rebuilding the entire application. This allows the application binary to remain stable while workflow configurations are updated independently in real-time.
Solution Approach 2:
The patent introduces a workflow engine as an intermediary layer between the application and workflow configurations. This mediator component loads and executes workflow definitions from remote sources, enabling updates without application reconstruction. The workflow engine acts as a buffer that maintains application stability while facilitating rapid configuration changes.
2Device complexity
If all users undergo the same workflow steps regardless of risk level, then the system is simple to implement, but it introduces unnecessary friction for low-risk users and fails to adequately verify high-risk ones, compromising user experience and fraud resilience
Solution Approach 1:
The patent implements dynamic workflow adaptation based on user risk profiles. The system automatically adjusts workflow complexity and verification steps according to assessed user risk levels. Low-risk users experience streamlined workflows with fewer steps, while high-risk users undergo more rigorous verification processes. This dynamic adjustment maintains fraud resilience while optimizing user experience.
Solution Approach 2:
The patent changes workflow parameters such as number of steps, verification requirements, and data collection depth based on user risk assessment. The system dynamically modifies workflow configuration parameters to match the risk level of each user, enabling simple implementation for low-risk cases while maintaining strong fraud detection capabilities for high-risk cases.
3Ease of manufacture
If AI models are baked into the application as a monolithic component, then the application is easier to deploy, but it precludes fine-grained control and the ability to assign specific AI checks at individual workflow steps
Solution Approach 1:
The patent segments AI model functionality into discrete, step-specific components rather than a monolithic integration. Each workflow step can be assigned specific AI models independently, allowing fine-grained control over which AI checks are applied where. This segmentation maintains deployment simplicity while enabling versatile, context-appropriate AI model selection for each workflow step.
Solution Approach 2:
The patent creates a universal AI model repository that can serve multiple workflow steps with different requirements. The same AI infrastructure supports various model types and functions, providing both ease of deployment through a unified system and adaptability through configurable model assignment at each workflow step level.
4Measurement precision
If technical developers author workflow configurations using design tools or scripting interfaces, then the configurations can be precise and controlled, but non-technical users cannot make timely adjustments, requiring development involvement for even minor updates
Solution Approach 1:
The patent introduces a visual workflow designer and administrative interface as intermediaries that enable non-technical users to create and modify workflow configurations. These tools provide user-friendly abstractions over the underlying configuration precision requirements, allowing business users to make timely adjustments without development involvement while maintaining configuration accuracy.
Solution Approach 2:
The patent empowers non-technical users with self-service capabilities to author and update workflow configurations independently. Through intuitive design tools and templates, users can make precise configuration changes without requiring developer assistance, enabling timely adjustments while maintaining the precision needed for reliable workflow execution.
Data Source
AI summary
The present invention relates to a system for artificial intelligence (AI)-guided dynamic configuration and execution of multi-step object verification workflows. The system comprises a computing device operable in an administrator configuration mode for defining workflow definitions and in a user execution mode for initiating and executing workflows. The system supports receiving administrator-defined workflows comprising instructional metadata, validation requirements, and AI model references. Based on contextual identifiers such as user ID or fraud risk score, a session is initiated, and a workflow recipe is retrieved and rendered dynamically on the computing device without requiring application recompilation. The system validates user inputs using on-device AI models, determines workflow progression, and transmits validated data. Workflow variants, fallback logic, and contextual routing are supported. The system enables configurable, scalable, and AI-augmented object verification across various physical inspection use cases.