Dynamic Query Tree Micro-Profiles for Ambiguous Voice Search
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Solution Overview
Problem
Computing devices face challenges in providing efficient and accurate responses to generic or insufficient voice queries due to limited input/output interfaces, leading to processor-intensive, network bandwidth usage, and communication delays.
Innovation Solution
A dynamic tree structure is used to generate a micro-profile in real-time by parsing voice queries, generating pivot points, and asking clarifying questions to narrow down results, leveraging historical search data to optimize the tree structure and reduce unnecessary interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the digital assistant generates numerous candidate responses or performs extensive processing to handle insufficient query information, then the accuracy and usefulness of responses may improve, but processor intensity, computational intensity, network bandwidth usage, and communication duration increase
Solution Approach 1:
The system performs preliminary actions by generating a micro-profile before fully processing the query. This micro-profile contains pre-computed information about the user's preferences, context, and likely intentions based on historical data. When a query arrives with insufficient information, the system can leverage this pre-computed micro-profile to infer missing details without performing extensive real-time processing, thus maintaining accuracy while reducing processor intensity.
Solution Approach 2:
The system creates a simplified copy or representation of the user's profile and context (the micro-profile) that can be quickly referenced during query processing. Instead of accessing and processing the entire user history and context database in real-time, the system uses this compact copied representation to rapidly determine appropriate responses, reducing both computational intensity and communication duration.
2Reliability
If the digital assistant requests numerous clarifying questions to obtain sufficient information, then the accuracy of the final response may improve, but the number of input/output requests and communication duration increase
Solution Approach 1:
The system performs preliminary action by pre-computing the micro-profile that contains anticipated user preferences and contextual information. This allows the system to predict what additional information might be needed and proactively provide it or infer it without requiring multiple back-and-forth clarification questions, thereby reducing communication duration while maintaining response accuracy.
Solution Approach 2:
The system uses feedback from historical user interactions, captured in the micro-profile, to anticipate user needs and preferences. This feedback mechanism allows the system to make informed assumptions about user intent based on past behavior patterns, reducing the need for explicit clarification questions and thereby shortening communication duration while preserving response accuracy.
3Adaptability or versatility
If the digital assistant provides a list of candidate responses for user selection, then the user may find the desired result, but the list may be cumbersome or excessive for the user to listen to or process
Solution Approach 1:
The system extracts only the most relevant and high-probability candidate responses based on the micro-profile analysis, rather than presenting a comprehensive list of all possible responses. By filtering and selecting only the top candidates that are most likely to match user intent, the system maintains user choice while significantly reducing the number of items the user must process, thereby improving ease of operation.
Solution Approach 2:
The system applies local quality by providing different levels of response detail and selection based on the specific query context and user profile. For some queries, it may provide a single highly-confident response; for others, it may provide a small curated list. This localized adaptation of response quantity and quality ensures user choice is preserved where needed while minimizing processing effort where high confidence exists.
4Speed
If the digital assistant processes queries without using historical search data, then the response can be generated quickly using available interfaces, but the response may not be accurate or useful due to limited information
Solution Approach 1:
The system performs preliminary action by pre-computing and storing the micro-profile from historical search data before queries are processed. This pre-computation allows the system to quickly access relevant user preferences and contextual information during query processing without performing extensive real-time analysis, thereby maintaining both fast response generation speed and high response usefulness.
Solution Approach 2:
The system uses dynamics by adaptively adjusting the level of historical data integration based on query characteristics. For simple queries with sufficient information, it can respond quickly with minimal historical data processing. For queries with insufficient information, it dynamically increases the utilization of micro-profile data to improve response usefulness, thus optimizing the balance between speed and reliability based on real-time needs.
Data Source
AI summary
Real-time micro-profile generating using a dynamic tree structure is provided. A system receives a first voice query. The system generates, from historical searches related to the first voice query, a first pivot point in a tree structure for the first voice query having child nodes. The system outputs an audio prompt to request selection of one of the child nodes. The system receives, responsive to the audio prompt, a voice input with a selection of a first child node. The system generates, from historical searches related to the first child node, a second pivot point in the tree structure including grandchild nodes. The system determines, based on a resource reduction policy, to generate a checkpoint to reduce additional child node generation. The system builds, based on a response to the checkpoint, a micro-profile for the electronic account identifier with the tree structure.