Context-Aware Suggestion System Using Preliminary Action and Mediator Principles
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Solution Overview
Problem
Current systems for data inquiry lack the ability to offer personalized suggestions to users based on their interests and context, often presenting irrelevant information as users navigate through different environments.
Innovation Solution
A computing device accesses lists of user information and contextual data to generate hypotheses about user interests, using sensors and databases to identify and present relevant suggestions without explicit user queries, by formulating tasks such as matching, extending, or completing lists based on user preferences and environmental context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional search engines are used to access information, then users can obtain vast quantities of information from numerous sources, but the information presented is often irrelevant to user interests and context
Solution Approach 1:
The system performs preliminary actions by collecting user profile data, context information, and item data in advance before a search query is submitted. This pre-processing enables the system to generate personalized suggestions and filter results based on user interests and current context, thereby improving information relevance without requiring complex real-time processing during the search itself.
Solution Approach 2:
The patent introduces an intermediary component that acts as a bridge between the user and the search engine. This intermediary processes the user's query by incorporating user profile information and context data, then modifies the query or filters the results to align with user interests. This intermediary layer improves relevance while keeping the underlying search engine infrastructure unchanged.
2Reliability
If filtering is employed to improve search results based on user categories of interest, then the likelihood of relevant items increases, but the system requires access to extensive user data and context information
Solution Approach 1:
The system segments the filtering process into distinct components: user profile analysis, context information processing, query modification, and result filtering. Each segment handles a specific aspect of the filtering task, making the overall complex process more manageable and efficient. This segmentation allows the system to achieve high result accuracy while organizing data processing into modular, maintainable units.
3Adaptability or versatility
If the system generates hypotheses about user interests based on lists and contextual data, then personalized suggestions can be provided, but the computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing user profile data and context information to create structured representations that can be quickly queried. User preferences, historical behavior, and context data are organized in advance, enabling rapid hypothesis generation when a query is submitted. This pre-processing significantly reduces the computational burden and time required for personalized suggestion generation.
Solution Approach 2:
The system implements partial action by generating a limited number of high-quality hypotheses rather than exhaustively analyzing all possible user interests. It focuses on the most probable user intentions based on available data, providing sufficiently personalized suggestions without the excessive computational cost of complete analysis. This approach achieves practical personalization while maintaining efficient processing times.
Data Source
AI summary
A system and method for offering suggestions to a user of a mobile computing device based on information relevant to the user and a context data. The mobile computing device has access to lists of data associated with the user. The mobile computing device also has a sensor for detecting data about the user's context. Based on the lists of information and the contextual data, the device generates a hypothesis as to information that may be of interest to the user. Using context data in conjunction with lists in this fashion focuses the system on information likely of interest, without requiring express user input. A search may be performed in accordance with the hypothesis. Based on the results of the search, one or more suggestions are then presented by the mobile computing device to the user. The user has the option to provide feedback input to the device resulting in an update of the suggestion.