Dynamic List Partitioning for Multimodal Client Devices
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Solution Overview
Problem
Multimodal client devices face challenges in efficiently rendering lists to users across different interaction modalities, leading to suboptimal user experience and resource wastage due to the need to render entire lists regardless of the current modality, which affects computational efficiency and user interaction.
Innovation Solution
Implement a system that dynamically partitions lists into subsets based on the current modality of the client device, using sensor data and list attributes to determine the optimal number of items for rendering, allowing for tailored output in various interaction modes such as voice only, visual only, or multimodal interactions, and prioritizing visual rendering over auditory when possible.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the entire list is rendered regardless of current modality, then the user receives complete information, but computational resources are wasted and rendering efficiency decreases
Solution Approach 1:
The patent divides the complete list into multiple subsets based on modality-specific criteria. For example, in voice-only mode, the list is segmented into smaller audio-friendly portions, while visual modes can display larger portions or the entire list. This segmentation allows the system to render only the necessary portion of the list for the current modality, avoiding wasted computational resources while maintaining information completeness for that modality.
Solution Approach 2:
The patent dynamically adjusts the list rendering strategy based on the detected current modality. The system continuously monitors modality changes and reconfigures which list subsets to render accordingly. This dynamic adaptation ensures that computational resources are allocated efficiently based on real-time user interaction mode, optimizing both resource utilization and information delivery.
2Adaptability or versatility
If the entire list is rendered for all modalities, then all interaction modes receive complete data, but unnecessary rendering occurs in specific modalities
Solution Approach 1:
The patent applies different rendering qualities and scopes to different modalities based on their specific characteristics. Visual modalities can handle larger datasets and display more comprehensive list portions, while auditory modalities are optimized for smaller, more concise portions that are easier to process and render in audio form. This local optimization ensures each modality receives appropriately tailored data without wasting energy on unnecessary rendering.
Solution Approach 2:
The system changes key rendering parameters such as the number of items to render, the level of detail, and the rendering format based on the detected modality. By adjusting these parameters dynamically, the system optimizes computational energy consumption for each modality while maintaining adaptability across all interaction types.
3Productivity
If list subsets are dynamically partitioned based on modality, then rendering efficiency improves, but the system complexity increases
Solution Approach 1:
The patent implements a universal list partitioning framework that can serve multiple modalities through a single cohesive system. The same partitioning logic and infrastructure handle voice-only, visual-only, and multimodal interactions, eliminating the need for separate rendering systems for each modality. This multi-functional approach manages complexity by consolidating partitioning logic rather than replicating it across different modality handlers.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor the current modality and automatically adjust list partitioning decisions accordingly. By continuously receiving modality information and using it to guide partitioning choices, the system simplifies the complexity through automated decision-making rather than requiring complex manual configuration for each modality scenario.
4Loss of energy
If modality-specific list partitioning is implemented, then resource wastage is reduced, but the determination of optimal subset size becomes more complex
Solution Approach 1:
The system employs self-service mechanisms where the modality detection and list partitioning operate autonomously based on detected user interaction patterns. The system automatically determines optimal subset sizes by analyzing current modality characteristics without requiring external intervention or complex manual optimization algorithms, simplifying the measurement and determination process while reducing resource consumption.
Data Source
AI summary
Systems, methods, and apparatus for dynamically partitioning items in a list into one or more subsets in a manner tailored to a current modality of a client device is disclosed herein. Multimodal client devices can engage in a variety of interactions across the multimodal spectrum including voice only interactions, voice forward interactions, multimodal interactions, visual forward interactions, visual only interactions, etc. A list can be partitioned into subsets in part based on a currently modality of a multimodal device, one or more list attributes indicating additional information about the list, etc.