Multimodal Query Ghosting for Context-Aware Autocomplete
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Solution Overview
Problem
Conventional autocomplete systems struggle to capture user intent, context, and handle incomplete or ambiguous queries effectively, particularly when non-text inputs such as images are used.
Innovation Solution
A system utilizing a generative AI model to generate autocomplete text based on multimodal inputs, including images, text, and contextual history, which displays suggestions as ghosted text or potential inputs, and updates suggestions based on diverging user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional autocomplete systems are used, then the system is simple and fast, but the system cannot capture user intent, context, and handle incomplete or ambiguous queries effectively
Solution Approach 1:
The patent introduces an intermediary processing layer between the user input and the autocomplete suggestions. This intermediary layer includes context analysis modules and intent detection components that process the input query before generating suggestions, enabling the system to capture user intent and context without completely redesigning the autocomplete architecture
Solution Approach 2:
The autocomplete system is divided into multiple functional segments: input processing module, context analysis module, intent detection module, suggestion generation module, and output module. This segmentation allows each component to specialize in specific tasks, improving overall adaptability while maintaining manageable system complexity through modular design
2Ease of operation
If multimodal inputs are processed to generate context-aware autocomplete suggestions, then the user experience is enhanced, but the computational resources required increase
Solution Approach 1:
The system applies partial processing to multimodal inputs by selectively analyzing only the most relevant features and modalities based on the query context. Instead of fully processing all input modalities with equal depth, the system performs targeted analysis that sufficient for generating accurate suggestions while consuming fewer computational resources
Solution Approach 2:
The system dynamically adjusts processing parameters such as analysis depth, model complexity, and resource allocation based on query characteristics. For simple queries, lighter processing is applied; for complex queries requiring deeper context understanding, more computational resources are allocated, optimizing the balance between user experience and resource consumption
3Adaptability or versatility
If autocomplete suggestions are generated for zero-text input using only images, then the system handles diverse input modalities, but the ambiguity of the query increases
Solution Approach 1:
The system implements feedback mechanisms where multiple potential autocomplete suggestions are generated for zero-text image inputs, and these suggestions are presented to the user for selection. The system also uses feedback from user interactions (selections, rejections, modifications) to refine and disambiguate the interpretation of image inputs over time, reducing query ambiguity through iterative learning
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.