Intent-Aware Keyboard Contextual Service Prediction
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Solution Overview
Problem
Users face inefficiencies in accessing relevant information due to the need to manually input search queries, as existing systems lack the ability to predict and provide contextual services based on habitual behaviors and current contexts.
Innovation Solution
An input mechanism program communicates user input and contextual information to a remote service provider, which analyzes this data to offer contextual services such as auto complete suggestions, search results, and knowledge base entries without requiring explicit search queries, leveraging habitual behaviors and current activities to provide relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually input search queries to access information, then search accuracy can be maintained, but user effort and time consumption increase
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns, contextual information, and habitual behaviors before the user actually needs to search. By pre-processing and pre-analyzing data about user preferences, recent activities, and contextual cues, the system prepares predictive search results in advance, reducing both the time and effort users need to spend on actual search queries
Solution Approach 2:
The system enables itself to automatically generate and present search results without requiring explicit user commands. By monitoring user behavior patterns and contextual information autonomously, the system serves itself in predicting what information the user needs and presenting it proactively, thereby eliminating the need for manual query input while maintaining relevance
2Measurement precision
If the system provides contextual services based on habitual behaviors, then information relevance improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of predicting user information needs into distinct functional modules: one module captures habitual behaviors, another processes contextual information, a third analyzes recent activities, and a final module synthesizes these inputs to generate predictions. This segmentation allows each component to specialize in specific aspects of analysis, improving information relevance while managing overall system complexity through modular architecture
Solution Approach 2:
The system implements a multi-functional analysis engine that handles multiple types of input data (behavioral patterns, contextual cues, recent activities) through a unified predictive framework. This universal approach allows the same core system to serve multiple purposes: predicting search queries, suggesting relevant information, and adapting to different user contexts, thereby improving relevance without proportionally increasing complexity
3Ease of operation
If the system automatically presents search results, then user effort is reduced, but risk of providing irrelevant information increases
Solution Approach 1:
The system incorporates feedback mechanisms where user responses to automatically presented search results are continuously monitored and used to refine future predictions. When users interact with (or ignore) suggested information, this feedback loops back to adjust the predictive model, improving accuracy over time while maintaining the reduced-effort benefit of automatic presentation
Solution Approach 2:
Before automatically presenting search results, the system performs preliminary validation by cross-referencing multiple data sources including user habits, contextual information, and recent activities. This preliminary filtering and verification process ensures that only highly relevant and accurate information is presented automatically, reducing the risk of providing irrelevant content while maintaining ease of operation
Data Source
AI summary
Systems, methods, and computer storage media having computer-executable instructions embodied thereon that provide contextual services are provided. Embodiments of the present invention allow an input mechanism to provide contextual services. Exemplary input mechanisms include a keyboard, a gesture interface, and a speech interface. These inputs may be used to provide user input into one or more applications running on a computer. The contextual services provided include composition assistance, grammatical assistance, communication-context assistance, and research assistance. In one embodiment, an input mechanism (“IME”) program provides the contextual service. The IME program may work with a remote contextual-service provider. The IME program communicates user input and contextual information to the contextual-service provider. The contextual-service provider analyzes the input and contextual information to determine whether one or more contextual services should be provided.