Multimodal Dialog Ghosting for Ambiguous Query Autocomplete
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Solution Overview
Problem
Conventional autocomplete systems struggle to capture user intent and context, particularly with incomplete or ambiguous queries, limiting their effectiveness in generating relevant suggestions.
Innovation Solution
A system utilizing a generative AI model that processes multimodal inputs, including images and text, to generate autocomplete text, which can include contextual history, and dynamically updates suggestions based on user input, even when the input diverges from previous predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional autocomplete systems are used, then the system is simple and fast, but the system cannot capture user intent and context effectively
Solution Approach 1:
The patent introduces an image description as an intermediary element that bridges the gap between simple autocomplete and complex intent understanding. The system generates text descriptions of uploaded images and uses these descriptions as autocomplete suggestions, allowing the system to capture user intent without requiring complex analysis of the image itself. This intermediary approach enables context-aware suggestions while maintaining system simplicity.
2Reliability
If conventional autocomplete systems are used, then the system responds quickly, but the system cannot handle incomplete or ambiguous queries
Solution Approach 1:
The system performs preliminary action by generating image descriptions and autocomplete suggestions in advance, before the user completes their query. When a user uploads an image, the system immediately generates a text description and potential autocomplete completions based on that description. This preliminary preparation allows the system to quickly handle incomplete or ambiguous queries by providing relevant suggestions without requiring additional processing time when the user types slowly or uncertainly.
3Measurement precision
If multimodal inputs are processed, then the relevance of suggestions is improved, but the computational resources required increase
Solution Approach 1:
The patent applies the extraction principle by separating the image processing function from the autocomplete generation function. Instead of processing the entire image to generate autocomplete suggestions, the system extracts only the essential information by generating a text description of the image. This extracted text description is then used as the basis for autocomplete suggestions, significantly reducing computational energy requirements while maintaining high relevance of suggestions.
Data Source
AI summary
Systems and methods for generating autocomplete text using a language model are disclosed. An image and text-prefix may be entered at an input field of a search application. The image is processed to generate an image description. The image description and the text-prefix signals may be used as input at a language model to generate an autocomplete text by the language model. A contextual history may also be included as input to the language model. The autocomplete text is an output by the language model based on the input at the language model. The auto-complete text may be a next-word ghosting.