Stimulus Information Grounding Linguistic Data
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Solution Overview
Problem
Current crowdsourcing techniques for collecting linguistic information lack a direct nexus between the collected data and real-world features of a target environment, limiting their ability to interact and integrate with physical systems effectively.
Innovation Solution
A processing system generates stimulus information with specific components that correspond to features of a target environment, presenting it to humans for linguistic descriptions, which are then mapped back to control or narrate actions within that environment, using a combination of generation, crowd interaction, and post-processing modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional crowdsourcing techniques are used to collect linguistic information, then a large volume of linguistic data can be gathered from multiple users, but the collected information lacks a direct nexus to real-world features of a target environment
Solution Approach 1:
The patent introduces stimulus information as an intermediary that connects linguistic descriptions to target environment features. The stimulus information contains feature representations that serve as a bridge between the abstract linguistic data collected from crowdsourcing and the concrete real-world features, enabling the collected information to be grounded without losing either volume or contextual connection.
Solution Approach 2:
The patent segments the stimulus information into distinct components, including feature representations and associated metadata. This segmentation allows the system to maintain organized connections between linguistic descriptions and specific target environment features, preventing loss of grounding while collecting large volumes of diverse linguistic data.
2Reliability
If stimulus information with feature mappings is generated and presented to humans for linguistic descriptions, then linguistic information can be grounded in target environment features, but the system complexity increases
Solution Approach 1:
The processing system performs multiple functions using a unified approach: it generates stimulus information, presents it to human users, collects linguistic descriptions, and maps responses back to target environment features. This multi-functionality reduces overall system complexity by consolidating what could be separate complex subsystems into a single integrated processing flow.
Solution Approach 2:
The patent changes the parameter representation of target environment features into a standardized stimulus information format that can be universally processed. By transforming diverse real-world features into a common parameter space for stimulus generation, the system achieves reliable grounding without proportionally increasing complexity.
3Ease of operation
If linguistic descriptions are collected through crowdsourcing without structured stimulus components, then ease of data collection is maintained, but the ability to interact with and control physical systems is limited
Solution Approach 1:
The patent performs preliminary action by pre-structuring the stimulus information with feature representations before presenting it to users. This preliminary structuring enables the collected linguistic descriptions to be directly applicable to physical system interaction, enhancing versatility without complicating the data collection process for users.
Solution Approach 2:
The system implements feedback by mapping linguistic descriptions back to target environment features and using this information to control or narrate actions in the physical system. This feedback loop enhances the adaptability of the system while maintaining simple crowdsourcing-based data collection, as users continue to provide natural language responses without needing to understand the underlying feature mappings.
Data Source
AI summary
A processing system is described which generates stimulus information (SI) having one or more stimulus components (SCs) selected from an inventory of such components. The processing system then presents the SI to a group of human recipients, inviting those recipients to provide linguistic descriptions of the SI. The linguistic information that is received thereby has an implicit link to the SCs. Further, each linguistic component is associated with at least one feature of a target environment, such as a target computer system. Hence, the linguistic information also maps to the features of the target environment. These relationships allow applications to use the linguistic information to interact with the target environment in different ways. In one case, the processing system uses a challenge-response authentication task presentation to convey the stimulus information to the recipients.