Contextual Prediction System for E-Commerce Inventory Selection

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Solution Overview

Problem

Existing e-commerce platforms face challenges in delivering contextually relevant content to users during electronic transactions, as they struggle to recognize and utilize context-based patterns in user behavior effectively, leading to irrelevant content being displayed.

Innovation Solution

A contextual prediction system that utilizes a content provisioning platform with a selection engine to dynamically select inventory based on contextual attributes, customer attributes, and predefined rules, integrating with a content provisioning API to deliver contextually relevant content during electronic transactions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing e-commerce platforms display content based on general advertising metrics, then advertising coverage is broad, but content relevance to individual users is low

Engineering Contradiction:

Improvecontent relevanceVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments user data into multiple contextual attributes (device type, location, time, behavior patterns) and customer attributes (demographics, preferences, purchase history). This segmentation allows the selection engine to process and combine these attributes to deliver highly relevant content to specific user segments without requiring the entire system to handle all possible user scenarios simultaneously.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The content provisioning API acts as an intermediary layer between the e-commerce platform and the content delivery system. It receives contextual and customer attributes as input, processes them through the selection engine, and returns appropriately selected content. This intermediary abstraction simplifies the overall system architecture by centralizing the content selection logic.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If the system processes multiple contextual and customer attributes to select inventory, then content relevance improves, but processing time increases

Engineering Contradiction:

Improvecontent personalizationVSAvoidcontent selection time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-defining rules for content selection based on contextual and customer attributes. These rules are established in advance and stored in the selection engine, allowing rapid retrieval and application during actual content selection operations. This pre-processing of selection criteria significantly reduces real-time processing time while maintaining high personalization levels.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The selection engine dynamically changes parameters such as weightings and thresholds for different contextual and customer attributes based on the specific transaction context. By adjusting these parameters adaptively, the system can optimize the balance between processing depth and speed for different scenarios, reducing overall processing time while maintaining relevance.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If the system delivers contextually relevant content dynamically, then user engagement increases, but system complexity increases

Engineering Contradiction:

Improvetransaction valueVSAvoidplatform integration complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The content provisioning platform is designed with multi-functionality, serving multiple purposes: content selection, contextual analysis, customer attribute processing, and inventory matching. This universal approach consolidates what would otherwise require multiple separate systems into a single integrated platform, reducing overall system complexity while enabling dynamic content delivery that increases transaction value.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system incorporates feedback mechanisms where transaction outcomes and user interactions with delivered content are fed back into the selection engine. This feedback loop enables continuous optimization of content selection algorithms, improving transaction value over time while the feedback itself becomes part of the predefined rules, reducing the need for complex real-time adjustments.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11776008B2Contextual prediction system and method

Publication Date: 2023.10.03 ROKT

AI summary

Disclosed herein are a content provisioning platform (150) and a system (100) that utilizes such a content provisioning platform (150). The content provisioning platform (150) includes: a selection engine (153) for selecting, in response to a request from a webpage (300) associated with an electronic transaction, inventory based on at least one of: a contextual attribute associated with the electronic transaction, a customer attribute associated with a user performing the electronic transaction, and a set of predefined rules. The inventory is selected from available inventory derived from at least one inventory provider during the electronic transaction. The webpage (300) is encoded in accordance with a content provisioning application programming interface (API) (140) associated with the content provisioning platform (150), the webpage including a contextual content display region (350) for displaying the selected inventory. The content provisioning API (140) is adapted to deliver the selected content to the contextual content display region (350).