Variable Processing With ML Usage Prediction for Resource Provisioning
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Solution Overview
Problem
Existing products and services often follow a 'one size fits all' approach, leading to inefficient technical provisioning, staffing, and pricing that do not align with individual user needs, resulting in unnecessary costs or missed features.
Innovation Solution
Employing machine learning techniques to predict user needs and customize product offerings, including features and pricing, based on user data and interaction, using a system that includes UI/UX modules, data sources, and commerce domains to dynamically adjust and optimize product provisioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a one size fits all approach is used for product configuration, then the product can meet the needs of a variety of different users, but technical provisioning and staffing becomes inefficient and out of alignment with eventual use
Solution Approach 1:
The system performs preliminary actions by using machine learning models to predict user needs and usage patterns before the user actually uses the product. This allows technical provisioning, staffing, and resource allocation to be pre-configured based on predictions, rather than using a generic one-size-fits-all approach that must accommodate all possible user scenarios.
Solution Approach 2:
The system changes parameters dynamically by adjusting product configuration, technical provisioning, and staffing levels based on predicted user needs. Instead of fixed parameters, the system uses machine learning to continuously optimize parameters like resource allocation, feature activation, and support staffing based on predicted usage patterns for each user.
2Adaptability or versatility
If product features are configured to meet various user needs, then user coverage is improved, but users may not use all features leading to inefficiency
Solution Approach 1:
The system extracts only the necessary features for each user based on machine learning predictions of their needs and usage patterns. Instead of providing all possible features to every user, the system identifies and activates only the subset of features that each user is predicted to use, eliminating waste of resources on unused functionality.
Solution Approach 2:
The system applies local quality by customizing the product configuration for each user based on their specific predicted needs. Different users receive different feature sets and configurations tailored to their individual usage patterns, rather than a uniform configuration that tries to satisfy all users generically.
3Ease of manufacture
If default product configuration is used, then provisioning is simplified, but unusual user demands differ from default expectations leading to dissatisfaction
Solution Approach 1:
The system performs preliminary analysis of user characteristics and usage patterns using machine learning models before provisioning the product. This allows the system to predict unusual user demands and configure the product appropriately in advance, rather than relying on default configurations that may not suit specific users.
Solution Approach 2:
The system enables self-service by using machine learning to automatically predict user needs and configure the product without requiring manual intervention. The system serves itself by generating predictions and automatically adjusting configurations, eliminating the need for complex manual provisioning processes while still accommodating unusual user demands.
Data Source
AI summary
At least one processor may receive user data indicating a scope of work and predicting at least one resource required to complete the scope of work. The predicting may include processing the user data with a machine learning (ML) process. The at least one processor may determine at least one provisioning property of the at least one resource and apply the at least one provisioning property to a standard product offering, thereby creating a customized product offering commensurate with the scope of work. The at least one processor may perform processing responsive to a user request using the customized product offering.