Dynamic Content Blending via User Intent Detection

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Solution Overview

Problem

The increasing blurring of work and personal life due to mobile computing and technology evolution leads to an unhealthy balance, with existing solutions failing to effectively personalize user experiences to maximize work-life balance.

Innovation Solution

An automated system that determines a user's intent based on context information, learned interaction patterns, and historical characteristics, generating a personalized computing experience by blending relevant home/personal and work/productivity content, efficiently allocating screen resources based on current work and life characteristics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If the system provides personalized content blending based on user interaction patterns, then user experience and work-life balance are improved, but system complexity and processing requirements increase

Engineering Contradiction:

Improveuser experienceVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system performs preliminary analysis of user interaction patterns during idle periods or low-usage times, pre-calculating personalized content blends and preparing recommendation profiles before active usage. This allows the system to make complex determinations about user intent and content blending without adding latency during critical user interactions, thus improving ease of operation while managing system complexity through time-based load distribution

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces an intermediary layer consisting of machine learning models and pattern recognition algorithms that mediate between raw user interaction data and content delivery decisions. This intermediary processing layer abstracts the complexity of analyzing interaction patterns, extracting meaningful intent signals, and determining optimal content blends, thereby improving user experience while containing system complexity within a dedicated processing module rather than dispersing it throughout the entire system

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If the system analyzes user interaction patterns and context information in real-time, then content personalization accuracy is improved, but processing time and bandwidth consumption increase

Engineering Contradiction:

Improveuser intent detection accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies partial analysis in real-time by focusing only on the most recent and relevant interaction patterns while relying on pre-processed historical data for the remainder of the user profile. This selective real-time analysis reduces processing time and bandwidth consumption during active usage while maintaining sufficient accuracy for effective personalization, trading off some analytical depth for speed when necessary

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If the system processes and blends multiple types of content, then user efficiency and productivity are improved, but energy consumption and computational resources increase

Engineering Contradiction:

Improveuser efficiencyVSAvoidenergy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system applies local quality by dynamically adjusting the level of content personalization and blending intensity based on the specific context and user needs. During productivity-critical moments, the system intensifies content blending and personalization to maximize user efficiency, while during routine tasks or low-priority operations, it reduces processing intensity. This contextual modulation of computational effort maintains high user efficiency when needed while reducing energy consumption during less demanding periods

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system implements periodic updates and re-analysis of user interaction patterns rather than continuous real-time processing. By periodically refreshing content blends and user profiles at strategically determined intervals, the system maintains high user efficiency through up-to-date personalization while significantly reducing overall energy consumption compared to continuous processing, creating an efficient rhythm of computational activity that balances productivity support with resource conservation

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS11550795B2Dynamic productivity content rendering based upon user interaction patterns

Publication Date: 2023.01.10 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

An efficient blend of home/personal and work/productivity related content based on a user's intent is provided, wherein the user's intent can be determined based on context information, learned user interaction patterns, and historical work and home characteristics and patterns. The system is individualized to the user and operative to generate a user experience that provides a blend of relevant home/personal and work/productivity related information to the user based on the user's current work and life characteristics. From a determined user intent, various aspects provide personalized computing experiences tailored to the user and, in some examples, incorporation of the user's patterns into an efficient blend of personal and productivity workflows. In further examples, the blend of home/personal and work/productivity related content and workflows are selectively displayed to the user such that screen resources are efficiently and advantageously allocated based on a determined relevance to the user's current work and life characteristics.