AI Metadata Optimizer for CPQ Systems
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Solution Overview
Problem
Current CRM systems lack an automated mechanism for assessing and optimizing configure-price-quote (CPQ) systems, particularly in identifying outdated or non-compliant metadata and rules, which can lead to inefficient product management and stale data.
Innovation Solution
Implementing a system that uses machine learning (ML) and artificial intelligence (AI) algorithms to assess and optimize CPQ systems by processing metadata and rules, identifying non-compliant data, and generating recommendations for improvement, with a feedback loop to refine the detection and recommendations based on user actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated assessment and optimization mechanisms are implemented using AI and ML algorithms, then data validity and rule compliance are improved, but system complexity increases
Solution Approach 1:
The patent introduces an automated assessment engine as an intermediary component that mediates between the CPQ system and the metadata/rules being assessed. This engine applies AI and ML algorithms to evaluate data validity and rule compliance without requiring direct complex interactions between all system components, thereby improving reliability while managing system complexity through structured mediation.
Solution Approach 2:
The system implements self-service capabilities where the automated assessment engine continuously evaluates and optimizes its own metadata and rules without external intervention. The engine uses feedback loops to learn from assessment results and automatically adjust parameters, improving data validity while reducing the need for complex external management structures.
2Manufacturing precision
If comprehensive rules and metadata are maintained in the CPQ system, then product management accuracy is improved, but data staleness increases
Solution Approach 1:
The patent implements continuous assessment operations where the automated engine constantly evaluates metadata and rules against current system state. This continuous action ensures that comprehensive rules remain accurate and up-to-date, preventing data staleness while maintaining high product management accuracy through ongoing validation rather than periodic updates.
Solution Approach 2:
The system incorporates feedback mechanisms where assessment results are fed back into the rule evaluation process. The automated engine uses this feedback to identify outdated or non-compliant metadata and triggers updates or alerts, ensuring that comprehensive rules remain current and accurate without manual intervention, thereby reducing data staleness while maintaining precision.
3Device complexity
If manual assessment of metadata and rules is performed, then system simplicity is maintained, but productivity decreases
Solution Approach 1:
The automated assessment engine operates autonomously to evaluate metadata and rules without requiring manual assessment operations. The system self-manages the evaluation process, applying AI and ML algorithms to identify compliance issues and generate recommendations, thereby dramatically improving assessment efficiency while maintaining relative system simplicity through automated self-service rather than complex manual procedures.
Solution Approach 2:
The patent replaces manual mechanical assessment processes with automated computational systems. Instead of human operators manually reviewing metadata and rules, the system uses AI and ML algorithms to perform evaluations automatically, substituting mechanical human labor with automated computational mechanisms that improve productivity while keeping the overall system architecture relatively simple.
4Reliability
If real-time automated assessment is implemented, then rule compliance is improved, but computational resource consumption increases
Solution Approach 1:
The patent implements periodic assessment cycles where the automated engine evaluates metadata and rules at scheduled intervals or triggered by specific events rather than continuously processing all data constantly. This periodic action maintains high rule compliance by regularly assessing compliance while reducing computational resource consumption by processing data in manageable cycles rather than continuous real-time operations.
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated optimization of product management systems. In embodiments, automated optimization of component tools of the product management system is provided by automated evaluation and optimization of associated rules and metadata. In embodiments, metadata and rules may be associated to each other by an assessment engine. A recommendation engine may then identify non-compliant metadata, may determine a condition of the rule, and/or may generate recommendations for the rules based on the non-compliant metadata. Automated optimization of the product management system may include automated creation and mapping of decomposition relationships between commercial products and technical products. In embodiments, input data may be parsed into a set of object with unique attribute fields, which may then be validated. Validated data objects are then processed by an optimization engine that automatically creates and maps decomposition relationships from the validated data.