User Input Disambiguation Through Physical Item Ensembles
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Solution Overview
Problem
Modern computer systems struggle to disambiguate user queries due to lack of consideration for the user's physical environment, often delivering inappropriate results that require refinement or abandonment of the search.
Innovation Solution
A media guidance application uses sensors to identify ensembles of items in the user's vicinity, associating them with keywords to disambiguate search queries by adding relevant context to ambiguous terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user profile data is used for disambiguation, then search results can be refined, but the system still cannot accurately determine user intent when physical environment context is missing
Solution Approach 1:
The patent introduces environmental sensors as an intermediary between the user and the search system. These sensors detect physical objects, locations, and contextual information that serves as a mediator to disambiguate user queries. For example, detecting a gym ball nearby helps resolve ambiguity in a query about exercise equipment, while detecting a coffee cup helps resolve queries about beverages.
Solution Approach 2:
The system integrates multiple sensor types (visual, auditory, tactile, environmental) into a unified disambiguation mechanism. This multi-functional approach allows the system to handle various ambiguous queries across different contexts using the same core technology, making the system universally applicable to diverse user scenarios without requiring separate specialized systems for each context type.
2Productivity
If traditional search systems operate without environmental sensors, then system complexity remains low, but search result relevance deteriorates when user physical context is considered
Solution Approach 1:
The patent segments the search system into distinct functional modules: sensor acquisition module, environmental analysis module, query disambiguation module, and search execution module. Each module handles specific tasks independently, making the complex system manageable and maintainable. The sensor module collects raw data, the analysis module processes it into meaningful contexts, the disambiguation module uses this context to refine queries, and finally the search module executes the refined queries.
Solution Approach 2:
The system performs preliminary environmental sensing and analysis before the actual search query is processed. By continuously monitoring the physical environment and pre-processing this information into contextual knowledge, the system is ready to immediately apply this context to disambiguate user queries when they occur, rather than reacting to queries after the fact.
3Reliability
If the system continuously monitors physical environment, then real-time disambiguation capability improves, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system employs periodic sensing triggered by specific events or time intervals. Sensors activate when needed - such as when a user approaches, when ambient light levels change, or at scheduled intervals - rather than operating continuously. This periodic action maintains sufficient environmental awareness for reliable disambiguation while dramatically reducing energy consumption compared to continuous operation.
Solution Approach 2:
The system intelligently determines when environmental sensing is necessary based on the current state and user behavior patterns. By learning from user interactions and contextual cues, the system can predict when environmental data will be needed and activate sensors accordingly, making the sensing operation self-regulating and energy-efficient rather than requiring constant external power or continuous operation.
Data Source
AI summary
Systems and methods are described for disambiguating user input based on a physical location of items in a vicinity of a user. The system determines that a query received from a user contains an ambiguity. In response, the system identifies several items in the physical vicinity of the user. Then, the system analyzes the identified plurality of items to determine whether the plurality of items forms a first ensemble of items or a second ensemble of items. If the plurality of items forms a first ensemble of items, the system performs a search using the search query and a first keyword related to the first ensemble of items. If the plurality of items forms a second ensemble of items, the system performs a search using the search query and a second keyword related to the second ensemble of items. The system then outputs results of the performed search.