Digital Assistant Health Feature Selection via Context Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital assistants and user experience features fail to adaptively adjust their operations based on user context and environmental data, leading to a limited and inappropriate user experience, causing users to abandon their usage.
Innovation Solution
A method and system for a digital assistant to determine a user's current state and desirability score using input datasets, generating a customized health-related feature by clustering health-related features based on shared characteristics, experience level values, and priority scores, and presenting them in real-time or near real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital assistants use fixed programming and general availability for feature selection, then device complexity is reduced and ease of manufacture is improved, but adaptability to user context and environmental data deteriorates
Solution Approach 1:
The system dynamically adjusts feature selection based on real-time user state and environmental context. Instead of fixed programming, the digital assistant continuously adapts its behavior by processing input datasets containing user biometric data, environmental sensors, and contextual information to determine current user state and select appropriate health-related features.
Solution Approach 2:
The system changes operational parameters by using desirability scores and experience level values to dynamically select and prioritize health-related features. The parameter selection is adjusted based on user experience level with different features, allowing the system to adapt its feature presentation according to individual user capabilities and preferences.
2Loss of information
If digital assistants present all available health-related features, then completeness of information is improved, but user experience deteriorates due to information overload and inappropriate timing
Solution Approach 1:
The system extracts and presents only the most relevant health-related features from the complete set of available features. By using desirability scores based on user state and context, the system filters out unnecessary information and presents only the subset of features that are currently most useful to the user, avoiding information overload.
Solution Approach 2:
The system applies partial action by selectively presenting a portion of available health features rather than all features. The desirability score mechanism determines which specific features to present based on current user needs, providing just enough information to be helpful without overwhelming the user with excessive details.
3Measurement precision
If digital assistants use fast search and general availability for feature selection, then processing speed is improved, but measurement precision of user needs deteriorates
Solution Approach 1:
The system performs preliminary action by continuously collecting and pre-processing user data, biometric information, and environmental sensor data in the background. This allows the system to quickly determine user state and select appropriate features without time-consuming analysis when a feature request occurs, maintaining both speed and precision.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring user responses and outcomes to refine its understanding of user needs. The desirability score system learns from user interactions and adjusts feature selection accuracy over time, improving measurement precision while maintaining efficient processing through iterative optimization.
Data Source
AI summary
A method for identifying and presenting a customized health-related feature for a user of a digital assistant. The method includes: determining, based on an input dataset, a current state of a user and a desirability score; generating a first group of a plurality of health-related features based on the current state, wherein the plurality of health-related features within the first group shares at least a common characteristic among the plurality of health-related features; extracting an experience level value for each of the plurality of health-related features within the first group; identifying a customized health-related feature; and presenting the customized health-related feature by the digital assistant.