Resource Prioritization for Spoken Language Inputs
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Solution Overview
Problem
Current natural language processing systems face challenges in prioritizing and managing user resources effectively across multiple user profiles and devices, leading to inconsistencies and limitations in user experiences when handling spoken language inputs.
Innovation Solution
A system that stores user and device profiles, allowing for the creation of group profiles to manage shared resources, uses machine learning models and rules to prioritize resource usage based on user inputs, domains, and intents, and applies weight matrices to determine the appropriate resources for processing spoken language inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system processes user inputs using multiple user profiles and devices, then the system can provide more comprehensive resource access and functionality, but the complexity of managing and prioritizing resources across multiple profiles increases
Solution Approach 1:
The system segments resources by associating them with specific user profiles and devices, allowing independent management of resources for each profile-device combination. This segmentation enables the system to handle multiple user profiles without creating a monolithic complex structure, as each profile's resources can be managed separately through their associated devices.
Solution Approach 2:
The system introduces a new dimension of device association alongside user profiles for resource management. Instead of managing resources solely through user profiles, the system adds the device dimension, creating a two-dimensional resource management framework that enables more granular control and prioritization across multiple profiles without linearly increasing complexity.
2Measurement precision
If the system uses machine learning models and weight matrices to prioritize resources, then the accuracy of resource selection improves, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing weight matrices that encode resource prioritization logic. Instead of performing complex machine learning computations in real-time for each user input, the system prepares the prioritization data in advance, allowing faster and more energy-efficient resource selection during actual user interactions while maintaining high accuracy.
3Quantity of substance
If the system loads all resources from multiple user profiles into memory, then resource availability for processing increases, but the memory consumption and system overhead increase
Solution Approach 1:
The system applies local quality by loading resources selectively based on the specific user profile and device context rather than uniformly loading all resources from all profiles. This contextual resource loading ensures that only relevant resources are loaded into memory for each processing task, maintaining high resource availability for the current context while minimizing unnecessary memory consumption and system overhead.
Data Source
AI summary
Techniques for prioritizing resources of various users, associated with a device, when responding to a user input received from the device are described. When a user input is received from a device, a system may generate a resource list for a group profile (e.g., a household profile) and each user profile (including any guest user profile) associated with the device. Each resource list may include the catalogs of resources (e.g., songs of a playlist, contacts of a contact list, etc.) of the group profile or user profile. The system may also generate a weight matrix including a respective weight for each catalog of each resource list. Various processing components (e.g., an automatic speech recognition component, a natural language understanding component, and an entity resolution component) may process using the resource lists and the weight matrix to determine an output responsive to the user input.