Document Auto-Completion via Context-Aware Content Suggestions
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Solution Overview
Problem
Users face inefficiencies when creating or editing documents, such as forms, due to the time-consuming process of typing in content, and existing auto-completion techniques provide irrelevant suggestions that are not adaptive to the user's context.
Innovation Solution
The proposed solution involves providing content suggestions based on context information associated with the document, including user information, target entity information, and expression habits, to generate precise and adaptive content for sections like titles, descriptions, questions, and options during document creation and editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually type in all document content, then the document can be created with complete control, but the time consumption and effort increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-generating document content (titles, descriptions, questions, options) based on context information before the user needs it. When a user creates a form, the system proactively suggests relevant content based on detected context such as recent communications, calendar events, or contact information, allowing the user to accept or modify suggestions rather than typing everything from scratch.
Solution Approach 2:
The system enables self-service by automatically generating content suggestions without requiring user initiation. The auto-completion system monitors user behavior and context, then autonomously provides relevant content suggestions for form fields, reducing the user's manual input burden while maintaining control over the final document content.
2Productivity
If existing auto-completion techniques are used, then typing speed may be improved, but the suggestions are irrelevant and not adaptive to user context
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring user interactions, accepted suggestions, and modifications. When users accept or reject content suggestions, or when they manually edit suggested content, the system learns from this feedback to improve future suggestions. This feedback loop enables the system to adapt to individual user preferences, writing styles, and contextual needs, making suggestions increasingly relevant over time.
Solution Approach 2:
The system dynamically changes parameters of content suggestions based on context. Instead of providing static auto-completion, the system adjusts the type, format, and content of suggestions based on detected context information such as the current form being created, the user's role, the target audience, and recent interactions. This allows the same system to provide different types of suggestions for different contexts (e.g., survey questions for feedback forms, registration fields for event forms).
Data Source
AI summary
The present disclosure provides method and apparatus for document auto-completion. In an aspect, an instruction for creating a document may be received. The document may be presented in response to the instruction, at least a first section in the document including content suggested according to context information associated with the document. An edit operation to the document may be received. Content suggested in response to the edit operation may be presented in at least a second section in the document. In another aspect, context information associated with completion of a document may be identified. Content of at least one section in the document may be generated, the content being suggested based at least on the context information. The content may be presented in the at least one section in the document.