Contextual Query Composition Using Pre-Input Query Clusters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing search systems require users to input full search queries, often presenting an overwhelming array of options, making it difficult to quickly find relevant information.
Innovation Solution
A system that groups user queries into context clusters based on input context and content, determining context cluster probabilities, and presenting these clusters to users before query input, allowing selection without typing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users are required to input full search queries, then search accuracy is improved, but user time and effort increase
Solution Approach 1:
The system performs preliminary actions by analyzing user context (location, time, device state) and pre-generating relevant query suggestions before the user actually inputs any search terms. This allows the system to anticipate user needs and present targeted options, reducing both the time users spend typing and maintaining search accuracy through context-aware suggestions.
Solution Approach 2:
The patent replaces the mechanical typing process with a contextual selection process. Instead of requiring users to physically input search queries character by character, the system uses contextual analysis algorithms to generate and present relevant query options, allowing users to select from pre-computed suggestions based on their current context, thereby substituting manual input with automated contextual generation.
2Ease of operation
If query suggestions are provided as users type, then user convenience is improved, but the number of options presented increases
Solution Approach 1:
The system applies local quality by tailoring query suggestions to the specific local context of each user situation. Rather than providing generic or universally applicable suggestions, the system customizes the set of presented options based on local factors such as user location, time of day, device being used, and current application context. This ensures that only highly relevant, context-specific suggestions are presented, maintaining quality while limiting quantity.
Solution Approach 2:
The system dynamically changes parameters of the suggestion generation process based on user context. By adjusting which contextual factors are weighted and how suggestions are filtered based on real-time device state and user behavior patterns, the system optimizes the balance between providing convenient suggestions and limiting the number of options presented to only those most relevant to the current situation.
3Speed
If context clusters are presented before query input, then query selection speed is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary contextual analysis and query clustering before the user inputs any search terms. By pre-processing user context data and organizing potential queries into context-based clusters in advance, the system enables rapid query selection once context is understood, improving speed while managing complexity through advance preparation rather than real-time computation during user interaction.
Solution Approach 2:
The patent introduces context clusters as an intermediary layer between raw user context and final query suggestions. This intermediary structure organizes potential queries into grouped categories based on contextual relevance, making the system more manageable by breaking down the complex task of query generation into structured clusters that can be more easily processed and presented to users, thereby managing system complexity while improving selection speed.
Data Source
AI summary
Methods, systems, and apparatus for generating data describing context clusters and context cluster probabilities, wherein each context cluster includes query inputs based on the input context for each of the query inputs and the content described by each query input, and each context cluster probability indicates a probability that at a query input that belongs to the context cluster will be selected by the user, receiving, from a user device, an indication of a user event that includes data indicating a context of the user device, selecting as a selected context cluster, based on the context cluster probabilities for each of the context clusters and the context of the user device, a context cluster for selection input by the user device, and providing, to the user device, data that causes the user device to display a context cluster selection input that indicates the selected context cluster for user selection.