Context-Aware Content Suggestion Platform for Aggregation Systems

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Solution Overview

Problem

Users interacting with content aggregation systems, such as business portals, often lack awareness of infrequently used applications and content, and existing methods fail to efficiently suggest relevant elements as business conditions change.

Innovation Solution

A system and method that utilize a suggestion platform to access data context trigger criteria, including a data locator, operator, and threshold, to automatically suggest business information content elements to users based on their current context, allowing for real-time or periodic evaluation and addition of relevant content to the user display.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If users manually select from hundreds of available elements, then they can customize their content aggregation system, but users become overwhelmed and unaware of relevant content they should be using

Engineering Contradiction:

Improveease of content selectionVSAvoidawareness of available content

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The system automatically evaluates data context trigger criteria and generates content suggestions without requiring users to manually search through hundreds of elements. The suggestion engine serves itself by autonomously analyzing business data and identifying relevant content elements that should be suggested to specific users based on their roles and current data context.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements a feedback loop where user interactions with suggested content are tracked and used to refine future suggestions. The suggestion engine continuously learns from user behavior patterns, acceptance rates, and contextual data changes to improve the relevance and accuracy of content recommendations over time.

Inventive Principle:

Principle #23Feedback

2Loss of information

If the system provides all available content elements, then users have complete information, but users cannot efficiently identify relevant content amid the overwhelming volume

Engineering Contradiction:

Improvecompleteness of content informationVSAvoidtime to identify relevant content

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The suggestion engine extracts only the most relevant content elements from the complete set of available elements by evaluating data context trigger criteria. Instead of presenting all hundreds of elements, the system selectively extracts and surfaces only those elements that currently match the user's context, data state, and role-based requirements.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system applies different levels of content recommendation to different users based on their specific contexts, roles, and data environments. Each user receives a customized subset of suggestions tailored to their local needs rather than a uniform list of all available content, making the information both complete for that user's context and efficiently filterable.

Inventive Principle:

Principle #3Local quality

3Reliability

If the system uses manual content curation, then content accuracy can be maintained, but the system cannot adapt quickly to changing business conditions

Engineering Contradiction:

Improveaccuracy of content suggestionsVSAvoidresponsiveness to business changes

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The suggestion engine dynamically adapts content suggestions in real-time as business data and conditions change. The system continuously monitors data context trigger criteria and automatically adjusts recommendations based on current business state, user roles, and contextual factors, enabling rapid adaptation without manual intervention while maintaining accuracy through structured evaluation rules.

Inventive Principle:

Principle #15Dynamics

4Measurement precision

If the system automatically suggests content based on complex criteria, then suggestion accuracy improves, but system complexity increases

Engineering Contradiction:

Improveprecision of content matchingVSAvoidcomplexity of suggestion system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The suggestion engine breaks down complex content matching into separate, manageable evaluation components: data context trigger criteria evaluation, user role assessment, content element filtering, and suggestion generation. Each component handles a specific aspect of the matching process independently, making the overall complex system maintainable and understandable through modular segmentation.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS9898555B2Systems and methods to automatically suggest elements for a content aggregation system

Publication Date: 2018.02.20 SAP PORTALS ISRAEL

AI summary

According to some embodiments, a suggestion platform associated with a content aggregation system may access a plurality of data context trigger criteria associated with potential business information content elements. Each data context trigger criteria may include, for example: (i) a data locator associated with a business information data structure, (ii) an operator, and (iii) a threshold. For each data context trigger criteria, it may be automatically determined if a value in the business information data structure satisfies the data context trigger criteria based on the operator and the threshold. When a data context trigger criteria is satisfied, it may be automatically suggested to a user that the potential business information content element associated with that data context trigger criteria be added to a user display of the content aggregation system.