Digital Assistant Profile Personalization via Activity Log Analysis

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Solution Overview

Problem

Existing digital assistant systems are inefficient and non-intuitive, requiring users to manually configure settings to achieve desired performance, leading to user frustration and suboptimal interactions.

Innovation Solution

A system and method that analyzes user activity logs to generate queries for natural language conversations, allowing the digital assistant to dynamically personalize user profiles by confirming inferences and refining its understanding of user preferences through targeted feedback.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If users manually adjust settings to achieve desired performance, then the system can be customized to user preferences, but the process becomes inefficient and frustrating for the user

Engineering Contradiction:

Improvecustomization capabilityVSAvoiduser operation efficiency

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The digital assistant performs self-configuration by automatically analyzing activity logs and generating personalized settings without requiring manual user input. The system serves itself by inferring user preferences from observed behavior patterns, eliminating the need for users to manually adjust settings while maintaining customization capability.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements a feedback loop where the digital assistant continuously monitors user activity logs, generates hypotheses about user preferences, tests these hypotheses through natural language conversations, and refines settings accordingly. This iterative feedback process enables automatic adaptation to user needs without manual configuration.

Inventive Principle:

Principle #23Feedback

2Device complexity

If the digital assistant requires manual user input for configuration, then the system structure remains simple, but the user experience becomes non-intuitive and frustrating

Engineering Contradiction:

Improvesystem configuration structureVSAvoiduser interaction intuitiveness

Core Design Contradiction:

Device complexityVSEase of operation

Solution Approach 1:

The patent replaces the mechanical manual configuration process with an automated AI-based system. Instead of requiring users to physically adjust settings through interfaces, the system uses machine learning models to automatically infer preferences from activity logs and generate appropriate configurations, substituting manual mechanical operations with intelligent automation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces an intermediary layer between the user and the configuration system. Activity logs serve as intermediaries that carry user behavior information, while the machine learning model acts as an intermediary processor that translates this information into personalized settings, and the natural language conversation interface serves as an intermediary for verification and refinement.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If the digital assistant uses automated analysis of activity logs, then the system can personalize user profiles dynamically, but the system complexity increases

Engineering Contradiction:

Improveprofile personalization capabilityVSAvoidsystem architecture complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The digital assistant leverages existing multi-functional capabilities already present in the system - activity logging, machine learning processing, and natural language conversation - to achieve profile personalization. By utilizing these universal components for multiple purposes, the system avoids adding separate dedicated infrastructure while still achieving dynamic personalization.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system performs preliminary analysis of activity logs continuously in the background, maintaining updated profiles and hypotheses about user preferences. This preliminary action occurs automatically without requiring triggered events or manual initiation, allowing the system to be ready for personalized interactions at any time while distributing the computational workload over time rather than concentrating it.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10446142B2Crafting feedback dialogue with a digital assistant

Publication Date: 2019.10.15 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Examples described herein dynamically personalize a digital assistant for a specific user, creating a personal connection between the digital assistant and the user. The digital assistant accesses user activity and generates queries based on the user activity. The digital assistant facilitates natural language conversations as machine learning sessions between the digital assistant and the user using the one or more queries to learn the user's preferences and receives user input from the user during the learning session in response to the queries. The digital assistant dynamically updates a personalized profile for the user based on the user input during the natural language conversations.