Visual Response Card Layout for User Input
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Solution Overview
Problem
Current natural language processing systems face challenges in providing an optimal user experience by effectively presenting multiple relevant responses to a single user input, as they often struggle to balance content relevance with limited screen space and user preferences, leading to suboptimal visual content configuration.
Innovation Solution
A system that employs a multi-results placement component utilizing machine learning models, such as contextual bandit models, to determine which skill components and supplemental content providers should contribute to the visual output, optimizing the configuration of visual content based on user preferences, device screen size, and conversation context, thereby presenting a comprehensive and user-friendly response to user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple skill components and supplemental content providers are invoked to respond to a single user input, then the completeness and relevance of the visual response is improved, but the device complexity and screen space management become more difficult
Solution Approach 1:
The system segments the visual response into multiple independent content cards, each generated by a different skill component or supplemental content provider. Each card is self-contained with its own layout and styling, allowing the system to manage complexity by treating each card as an independent unit rather than managing a monolithic complex response structure.
Solution Approach 2:
The visual response system implements a universal card-based container structure that can accommodate content from multiple different skill components and supplemental content providers. This multi-functional framework allows diverse content types to be presented through a unified interface, reducing the need for separate handling mechanisms for each content source.
2Loss of information
If multiple skill components and supplemental content providers are invoked to respond to a single user input, then the completeness and relevance of the visual response is improved, but the screen space utilization becomes suboptimal
Solution Approach 1:
The system dynamically adjusts the number, size, and arrangement of content cards based on available screen space and the importance of each content source. The layout engine dynamically determines whether to display cards in a single column, multiple columns, or as expanding sections, optimizing screen space utilization while maintaining the completeness of the visual response.
Solution Approach 2:
The system transitions from traditional linear or hierarchical response layouts to a two-dimensional card grid arrangement. This dimensional change allows multiple content cards to be displayed simultaneously in a space-efficient manner, enabling the system to present comprehensive information from multiple skill components without excessive vertical scrolling or cramped single-column layouts.
3Ease of operation
If machine learning models are used to determine skill component selection and content configuration, then the user experience and content relevance are improved, but the processing time and computational resources increase
Solution Approach 1:
The system pre-trains machine learning models offline to learn user preferences, content relevance patterns, and optimal layout configurations. During runtime, these pre-trained models quickly infer the best skill component selections and content arrangements without requiring extensive real-time computation, thus improving user experience while minimizing processing time delays.
Solution Approach 2:
The system implements a tiered machine learning approach where simple heuristics handle routine skill component selections, while more complex ML models are only invoked when the situation warrants enhanced optimization. This partial application of ML processing reduces overall computational overhead and processing time while still improving user experience in cases where it matters most.
Data Source
AI summary
Techniques for generating a visual response to a user input are described. A system may receive input data corresponding to a user input, determining a first skill component is to determine a response to the user input, and determine a second skill component is to determine supplemental content related to the user input. The system may also determine a template for presenting a visual response to the user input, where the template is configured for presenting the response and the supplemental content. The system may receive, from the first skill component, first image data corresponding to the first response. The system may also receive, from the second skill component, second image data corresponding to the first supplemental content. The system may send, to a device including a display, a command to present the first image data and the second image data using the template.